End To End Cricket Data Analytics Project Using Web Scraping, Python, Pandas and Power BI

Aug 15, 2023

End To End Cricket Data Analytics Project Using Web Scraping, Python, Pandas and Power BI

End-to-end sports data analytics project for your resume. In this project, we will cover web scraping (from the ESPN Cricinfo website), python, pandas, and Power BI to perform analyses on T20 world cup cricket data.

Note : The Bright Data interface has been updated as part of their product enhancement. To access the Datasets \u0026 Web Scraper IDE, please select the third icon from the left-side menu bar. Then, click on the ‘My Scrapers’ option. There, you will find the collectors. From this point, you can proceed with the same process as shown in the video.

Bright data signup link to get 15$ free credits: https://brdta.com/codebasics

Sample LinkedIn post for the final challenge: https://bit.ly/3uHCzQE

Code \u0026 other files can be downloaded from here: https://codebasics.io/resources

Bright Data error guide: https://drive.google.com/file/d/1P-UX…

⭐️ Timestamps ⭐️
00:00:00 Overview
00:00:29 Problem statement
00:02:33 Requirement scoping
00:09:21 Data collection using web scraping (from ESPN Cricinfo website)
00:21:57 Data cleaning and transformation in Python Pandas
00:44:01 Data transformation in Power Query
00:56:52 Data modeling and building parameters using DAX
01:08:17 Build dashboard in Power BI
01:20:53 Collect insights from the dashboard and select the final 11 players
01:38:04 Challenge for you and prize

In this end-to-end data analytics project for beginners and advanced users, we have used cricket T20 world cup (2022) data to build insights on a best 11 players team that we can assemble from the earth that can go and play with aliens. We used bright data web scraping to collect data from espncricinfo website then we performed some data transformation and cleaning in pandas, followed by building dashboards in power bi. This is a solid data science resume project that will make your resume stand out in the competition. It is a fully guided data analytics project with all the materials included.

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#cricketanalysispowerbi #cricketanalysispowerbiproject #powerbiproject #analysisproject #dataanalysisproject #cricketdataanalysispowerbi #dataanalysispowerbi #powerbidashboardproject #powerbidashboard #endtoendpowerbiproject #powerbiproject #powerbiprojects

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Content

0 -> T20 Cricket World Cup was finished just

2.399 -> few weeks back with England claiming

4.2 -> victory over Pakistan, and today we are

6.54 -> beginning a cricket data analytics

8.58 -> series using same T20 World Cup data. We

11.7 -> will begin this project by scraping

13.74 -> data from ESPNcricinfo website. Then

16.68 -> doing data cleaning and transformation

18.18 -> in Pandas, and then eventually building

20.699 -> dashboards in Power BI. Before we begin

23.039 -> any technical work, let's look at the

24.779 -> exact problem statement and our special

27.42 -> stakeholders that we got in this project.

32.48 -> The whole atmosphere is charged with one

36.48 -> title.

40.68 -> Thursday

49.11 -> [Music]

60.04 -> [Phone Ringing]

64.04 -> We like your cricket!

68.1 -> Surrender, Earth!

70.14 -> You need to fight 8 billion people for

72.96 -> that! We destroy! Then you get nothing!

76.799 -> Negotiate.

79.74 -> You defeat us in cricket,

82.02 -> you get Earth! If you lose join me as an

86.159 -> intern! Deal!

99.299 -> Tony, we got to save Earth give me your

102.299 -> best 11!

103.86 -> Best 11 what? Data analysts?

107.04 -> No, cricket players!

110.1 -> As you saw Planet Sporta has challenged

113.04 -> planet Earth to play cricket, and Nick

115.68 -> Fury has assembled the secret agents of

119.04 -> field to work on this project to find

121.74 -> out the best 11 players based on the T20

125.159 -> World Cup Cricket data. Tony Sharma is in

128.22 -> charge of this project. He is not only a

130.679 -> senior data analyst, he's also a cricket

133.68 -> subject matter expert. So, in the next

135.84 -> video you will see what kind of

137.76 -> algorithm Tony Sharma comes up to pick

140.22 -> this best 11 team who can go and defeat

142.98 -> the aliens. At the end of the video, we

144.9 -> are going to give you a challenge based

146.52 -> on this project and by working on that

148.98 -> challenge you will be able to win

150.54 -> exciting prizes. So make sure you watch

152.52 -> till the end.

154.68 -> Nick, this is the requirement you gave me.

159.78 -> You said we don't know the strengths and

162 -> weaknesses of our opponents, but give the

163.98 -> best eleven from the planet. So, this is what

165.9 -> I'm going to do:

167.16 -> I'm going to give you a team that will

169.08 -> score 180 runs on an average.

171.54 -> At the same time, this team will be able

174.06 -> to defend 150 runs. So you have a margin

177.06 -> of 30 runs to play. Do you think

178.5 -> that's enough for you? Yes, this sounds

180.3 -> like a good target because although

182.819 -> aliens have not played Cricket they have

185.04 -> a technology where they can learn things

186.66 -> really fast. Okay, so this is how I've done

188.879 -> this: I've asked I've made different

190.68 -> positions for players, and I've selected

193.019 -> parameters for each of them. I'm gonna

195 -> quickly show these parameters to you and

196.62 -> let me know if you know if you want to

198 -> add some parameters. Okay. You know

199.56 -> anything you know just this is just a

201.48 -> idea I want to give and how I'm going to

203.76 -> select the team. The first ones are the

205.62 -> openers they are going to open the

206.76 -> innings, these are the power hitters. So,

208.98 -> they will be hitting the balls out of

210.42 -> the park,

211.86 -> and

213.18 -> they will also score runs, it's just not

215.64 -> about hitting. So that's how I've

217.08 -> selected the parameters, batting average,

218.519 -> strike rate, uh boundary percentage, very

221.519 -> important where you should be scoring 50%

222.9 -> of the runs, over 50% of the runs by

225.18 -> boundaries because in the powerplay the

227.4 -> fielders are going to be inside and they

229.2 -> should be hitting outside the inner

230.76 -> circle. That's the plan and uh they

233.7 -> should be giving us at least 50 runs

235.26 -> this partnership, and uh within the first

238.019 -> five hours. Got it! I, so we'll basically

241.379 -> get players like Sehwag correct?

244.879 -> Nick, Sehwag isn't playing anymore.

247.26 -> Oh okay, I see.

249.54 -> Okay?

252.06 -> All right, then we have the middle order

254.7 -> or the anchors. So here yeah they won't

258.359 -> be hitting ball hitting the ball as hard

260.639 -> as openers, but they can shift gears and

263.52 -> hit the balls if needed. So, they will

265.44 -> have a better batting average, they will

266.94 -> bat for longer time that's why I've also

269.04 -> included the average balls faced. You know

271.259 -> they're also bat you know like

273 -> aggressively if they want. So those are

274.62 -> the kind of players you'll have here. I'm

276.12 -> going to select three players for this

277.56 -> position, so

279.36 -> overall we have five players now. So,

281.639 -> these five players will give us at least

284.58 -> 120 plus runs in the 13 to 14 overs,

287.22 -> that's the plan. I would like to see

289.62 -> Virat Kohli in this five list.

292.38 -> Oh he is playing. I think he's certainly a

294.72 -> part of this team.

296.16 -> Great! And of course we are we are making

298.08 -> this you asked uh the special

299.4 -> requirement is to consider the World Cup

301.259 -> T20 2022 for uh you know for the

304.68 -> selection of the team, so I'm select I'm

306.12 -> picking only that particular data for

307.62 -> that. Okay!

309.36 -> All right, and uh so this is going to be

312.18 -> an interesting role uh we I'm gonna get

314.58 -> one player for this role, a finisher role.

316.5 -> So, if we are chasing, this particular

318.72 -> person should be able to hit like crazy,

321.18 -> go berserk. But if you lose wickets early,

324.24 -> like if you lose a middle order so badly,

326.759 -> so this person will be able to stabilize

329.16 -> the innings and order the rest of the

330.66 -> lower order batsmen.

332.58 -> So that's the kind of player we're

333.96 -> looking for, we're looking for more of a

335.82 -> batsman here than a bowler. So uh you

339.24 -> know ideally I would need it all rounder

340.62 -> here. But again, uh with more uh batting

344.22 -> all rounder, rather bowling all rounder

345.84 -> and this will leave us with five more

348.06 -> players and uh my seventh and eighth

350.759 -> player will be all rounders or the

353.88 -> lower order batsmen. So, mostly these

355.5 -> can be spinners and uh because the the

358.38 -> last three

359.699 -> slots, I'm going to keep it for fast bowlers.

362.16 -> So, these spinners are the ones who can

364.8 -> also hit and they can hit uh without

367.32 -> thinking. Like they come to bat they just

369.419 -> start hitting. So that's the kind of

370.8 -> players we want because they will be

372.24 -> mostly coming under uh around 17th or

374.82 -> 18th over, and uh so that's why I have

378.12 -> selected uh you know I kept the

380.28 -> parameter in such a way, the batting

381.66 -> average should be at least 15 and you

384.72 -> can see the strike rate is more than

386.039 -> 140.

387.419 -> So, if even if they score 15 runs they

389.819 -> should be scoring that in around eight

391.68 -> or nine balls. And you can see the

394.199 -> bowling economy is really good it's less

396.12 -> than seven, which means if these two

397.979 -> players ball all the 20 overs, they will

400.8 -> be giving only 140, which is good for us

403.56 -> because our team target is to keep the

405.78 -> defense under 150. And their strike rate

408.96 -> is less than 20, which means if they ball

410.819 -> 20 balls, they will get one wicket at

412.68 -> least. If they ball all the 20 overs they

414.72 -> should be getting at least six tickets.

416.759 -> Um

417.6 -> nice!

418.979 -> Yeah and uh

421.44 -> here Comes the specialist fast Bowlers

423.96 -> because

425.639 -> we need to rattle the team, we need to

428.34 -> rattle the the Sportans you know, I

431.58 -> don't think they should be having

432.66 -> capability to face these bowlers. This is

435.419 -> the wolf pack we've got. They are

438.72 -> threatening, they can take wickets at

442.139 -> least every 16 balls or even less than

444.78 -> that. This is the parameter I've said for

446.28 -> them and they can ball very fast, and

448.38 -> they can also you know ball dot balls

451.38 -> over uh 40% which means, if they

454.02 -> ball four overs

455.52 -> around 10 of that will be dot balls,

457.5 -> which is great for a T20 game.

459.66 -> Exactly, dot walls are so precious for

462.78 -> T20!

464.34 -> Yes and with these 11 players I think

467.94 -> I'm pretty sure we'll save the Earth.

470.759 -> What do you think?

472.139 -> The algorithm looks pretty solid to me. I

474.9 -> am excited to see the final results

476.94 -> which our Power BI dashboard can produce.

480 -> Yeah. So this is already in progress. So,

483.419 -> I'll be uh you know I'll be showing you

485.34 -> the you know the final dashboard, and we

487.8 -> can do an analysis together to pick the

489.419 -> final eleven. Looking forward, thank you!

491.52 -> Thank you, Nick! See you then, bye!

494.22 -> Folks we had our resources on GitHub, but

497.46 -> we have moved to this resources section

499.68 -> on Codebasics.io because the file

502.319 -> sizes were getting very big. So go to

504.96 -> Codebasics.io click on resources

507.78 -> here, and you will be able to find all

510.72 -> the files here. So, here you can click on

513.18 -> download and then you can just create an

516.18 -> account for the Codebasics website and

518.7 -> login once you're logged in you will be

521.039 -> able to download all these files.

523.5 -> Now you can download whatever files you

526.02 -> need, sometimes people may not need

528.06 -> all the files although we are going to

529.86 -> provide download all button later, but

533.1 -> this way you can download just the

534.839 -> required files. Here, for example I am

537.12 -> going to download my web scrapping code

540.42 -> and once it is downloaded, you can open

543.959 -> it,

545.399 -> unzip,

548.7 -> and here you will be able to see all

551.16 -> those JS file that we need in Bright

555.6 -> Data.

557.1 -> Similarly, you can download rest of the

559.14 -> files as well.

567.29 -> [Music]

594.74 -> Folks, why so much fuss? Just use something

598.14 -> readymade. For example Bright Data!

605.72 -> [Music]

611.42 -> Bright Data uses proxy networks for building web

614.82 -> scrapers that works seamlessly. They have

617.76 -> various solutions, such as residential

619.7 -> proxies, web unlocker, and so on. They

622.8 -> also provide readymade datasets. The

625.38 -> tool that we are going to use

626.64 -> specifically is called data collector

628.56 -> and if you use the link which is given

630.6 -> in the video description below, it's a

632.279 -> special link for Codeasics, you can log

635.399 -> in here it says work email, but don't

637.56 -> worry you can use your personal email ID

640.26 -> and still login. Just select some

643.2 -> values from these two drop downs and you

645.959 -> will be able to create an account. Once

647.88 -> the account is created,

649.8 -> if you look at the dashboard you will

652.079 -> find that you will have $15

654.6 -> balance. I want to thank Bright Data for

658.079 -> giving this $15 free credit for all Code

661.56 -> basics viewers. For our data scrapping

665.16 -> work we need hardly three or four

666.959 -> dollars. So, this is more than enough! You

668.64 -> can use this credit for your other

670.68 -> projects too!

676.56 -> We are going to capture four type of

678.42 -> data for our project. Number one is this

681.3 -> detailed match results table. So we will

683.7 -> scrap this entire table. When you click

686.04 -> on this particular scorecard link, you

688.26 -> will get a detailed bowling and batting

691.38 -> scorecard. So, we are going to grab all

694.38 -> these tables as well. And then when you

696.3 -> click on any player, you will get player

698.519 -> specific information. So we'll grab few

700.38 -> fields from this also.

703.579 -> I'm going to click on user dashboard and

707.579 -> go to something called a data collector.

710.459 -> Now data collector is web crawler

713.88 -> basically which will go to the website

715.74 -> and collect the data. I have already

717.54 -> created the four collectors. So, this one

719.76 -> is for the match results. So, let's say

722.519 -> this page you want to grab that right,

725.279 -> let's look at that code and understand

727.38 -> how that works. So, when you right click

729.839 -> and say inspect, it will show you the

733.26 -> HTML tags for that particular page. And

736.2 -> you can click on this, and let's say you

739.5 -> are getting this particular table right

742.14 -> or this particular let's say cell.

744.779 -> When you look at this, see this is one

747.48 -> row, this is second row, third row and so

749.399 -> on and this particular table

751.92 -> is inside this tbody and the table

754.26 -> class is enginetable. So, now let me show

757.14 -> you my collector. So, I will go to my

761.519 -> collector here and say edit code and in

765.42 -> this one you will see the interaction

768.18 -> code as well as the parcel code. Now this

770.399 -> is a JavaScript code JavaScript is

772.68 -> better suited for web scrapping because

775.019 -> a JavaScript code runs inside the

777.42 -> browser. So, it becomes easier for that. So,

780.12 -> here this particular link that you're

782.459 -> seeing is nothing but this particular

785.1 -> page. So, what I'm saying in my collector

787.86 -> is go to that page and then collect

791.16 -> parse. Now when you say parse, it is going

793.5 -> to execute all this code. Now once again

796.139 -> this is a JavaScript code, where I am

799.82 -> locating the enginetable table, then I

803.519 -> am going into tbody and then tr.

805.98 -> data1 So, if you look at here

808.74 -> see there is an enginetable then trt

811.38 -> body and then tr.data1 and this one

814.68 -> is an array. So if you go through this

817.62 -> array one by one, you'll be able to get

819.959 -> all these records, so and that's exactly

822.6 -> what I'm doing here. See, the first

825.54 -> element is team one, second is team two,

827.88 -> then winner and so on. So if you look at

829.92 -> any row here see the first one is team

832.62 -> one, second one is team two, you can see

834.42 -> on the left side then the winner, the

837.839 -> margin, the ground and so on so that's

839.82 -> exactly what we are doing here. And then

843.12 -> it will grab all this data into match

847.62 -> summary array this is Javascript array

850.139 -> basically, and it will return that okay?

852.959 -> So let me just run this here.

856.38 -> It will take some time but you will see

858.48 -> that in the browser the page is loaded.

860.459 -> So it executed this navigate function

862.5 -> band here and now it is collecting all

865.44 -> this information. So if you look at it in

867.66 -> the output you will see this information

869.6 -> is now available as a Json, and if you

872.579 -> download that Json file this is how it

875.339 -> will look. Now, let me just format it so

877.68 -> that you can see it properly.

880.5 -> You can see that now you know you have

882.899 -> team one, team two, who is the winner,

884.76 -> margin, ground, match date, and the

887.04 -> scorecard. So, we grab this particular

890.72 -> first table in its entirety,

894.72 -> we have the entire match result for T20

898.38 -> World Cup. Now the way Bright Data

902.04 -> collector works is they're going to use

905.22 -> a smart proxy Network okay and using

908.399 -> that infrastructure, it will do web

911.399 -> scraping so that you have seamless data

914.699 -> collection without having to worry about

916.5 -> some website blocking your IP, and so on

919.139 -> because it is sort of like VPN, it is

921.24 -> using different IPs and you will not

923.699 -> have any trouble in your data collection

926.22 -> process the kind of trouble that you

928.26 -> have when you are using plain Python

930.18 -> script. If you're new to Bright Data what

932.94 -> you can do is you can use one of the

934.68 -> templates. So for example, I can click on

936.839 -> develop a self-managed uh collector, and

940.38 -> you can use one of these templates. See

942.72 -> here you are collecting data from uh

945.839 -> Quora for example, here you're collecting

948.06 -> a data from YouTube okay? So, you use that

951.66 -> template and you can run that collector

953.88 -> and you will get an understanding of

956.22 -> that. I have provided the code for all

959.22 -> these four collectors on GitHub. The link

962.339 -> is in video description below. So, if you

964.5 -> look at the web scrapping codes

966.959 -> see here is the batting summary, here is

969.24 -> the bowling summary, so these are the

971.579 -> code that you have you have interaction

973.26 -> code and you have parcer code. Now let me

975.6 -> show you batting uh summary web

977.88 -> collector. So, here I can go to batting

980.94 -> summary, click here

982.98 -> and then

984.36 -> advanced action, edit the code. Here what

987.48 -> we are doing is this first page that you

990.24 -> have is this page only okay? So, you're

993.3 -> going through that match summary page,

995.459 -> and then you are collecting all the

997.68 -> links. All right, which links?

999.839 -> So, these links see when you right click

1002.24 -> and say copy link address, you get this

1005.3 -> particular link right? So we are

1007.1 -> collecting all those links in this

1009.56 -> particular code all right? Then we are

1011.959 -> calling next stage. So, in the next stage

1013.94 -> it will execute this particular code

1015.62 -> okay, and this will be the link of that

1019.16 -> mass score, and if you want to see it

1021.019 -> from the previous run I have see I have

1023.3 -> all these links which I collected from

1026.78 -> the first stage. And in the second stage

1029.48 -> what you will do is you will go through

1032.78 -> this scorecard. So, for example this is my

1034.52 -> scorecard and if you inspect this

1037.699 -> particular page and let me click on this

1040.939 -> element and go here

1042.86 -> right,

1044 -> so if you do this one you will find that

1048.799 -> you are collecting data from a table

1050.96 -> called CI scorecard table, and if I check

1055.64 -> that in my collector here you will find

1059.66 -> that,

1061.4 -> here

1063.44 -> I am collecting that particular table

1066.32 -> and going through my first inning, second

1069.2 -> inning, etc.

1070.58 -> This code is not as hard as you think. It

1073.46 -> is just HTML, you need to have basic

1075.26 -> knowledge of HTML. You're going through

1077.419 -> those HTML elements and you're just

1079.039 -> trying to grab data from it. So, you're

1081.44 -> going through an entire list,

1084.26 -> all the scorecard one by one, and you're

1087.32 -> putting uh that summary here okay? And

1090.86 -> when you click on this it's gonna take

1092.48 -> time. What it will do is it will load

1094.46 -> this page, then it will go to this link,

1096.5 -> then it will you know grab all this

1099.98 -> tables, the batting tables, then it will

1102.919 -> go back it will

1105.679 -> it will go to the second link collect

1108.98 -> all the tables and so on. When you click

1111.44 -> on this it will do a sample run for one

1113.84 -> or two matches. But if you want to run

1115.52 -> this collector uh you know for the

1118.22 -> entire data collection, what you can do

1120.5 -> is you can click here and you can say

1122.78 -> initiate manually. So, it will run the

1125.179 -> whole collector. You can also set it on a

1127.22 -> schedule or initiate by API in some

1129.5 -> Python code as well. I am going to show

1131.48 -> you match results execution because it

1133.52 -> doesn't take much time so you click on

1135.26 -> this and in the delivery preferences, you

1137.84 -> know I'm going to type in my email ID

1139.4 -> here. So, it will deliver the data as a

1142.7 -> Json to my email ID.

1151.419 -> All right? So, just say initiate manually,

1155.96 -> start,

1157.76 -> and see this starting now it will run

1160.1 -> internally. It is using smart proxy

1162.32 -> framework and it is going to ESPN

1165.26 -> Cricinfo website grabbing that data and

1168.14 -> once data is ready it would have sent me

1170.72 -> an email. All right! When I check my email,

1172.82 -> I got the match results, and when you

1175.7 -> click on download results

1178.82 -> it would have downloaded this particular

1181.58 -> Json file, and when you look at this Json

1183.679 -> file see I have the entire match summary

1187.4 -> okay? So, this way I ran all the

1190.1 -> collectors and grabbed all the data, and

1192.02 -> I have put that data on GitHub in T20

1195.82 -> Json and CSV file all right? So, this is Json

1199.1 -> files, if you want to directly get the

1201.26 -> data you can get it from here, but I

1203.12 -> highly recommend that you use Bright Data

1205.28 -> for data collection because data

1207.559 -> collection is super important part of

1209.72 -> any data science or data analytics

1211.52 -> project. When you log in make sure you

1213.62 -> are seeing this $15 credit and then you

1215.96 -> can go to data collection, collectors and

1219.14 -> create your first collector by clicking

1221.6 -> on this open IDE. So when you do that, you

1225.679 -> can just say start from scratch you're

1228.14 -> creating a blank collector. You're not

1230.179 -> using any of these templates and when

1232.039 -> you see the JavaScript UI what you will

1235.34 -> do is you will use the code which I have

1238.34 -> provided, you again check video

1240.02 -> description carefully for all the

1242.24 -> download instructions. You will have this

1244.58 -> file, for example T20 World Cup matches

1247.28 -> results.js so open that file and it

1251.059 -> has two sections: interaction code and

1253.1 -> parser code. So, for interaction code I

1255.62 -> can just copy paste this here,

1258.08 -> and you know use that code here. Whereas,

1261.5 -> for the parser code, copy paste just that

1264.799 -> particular portion here in the parser

1267.74 -> code, and that's it! This is your

1269.72 -> collector, it is ready okay, and you can

1271.46 -> run it, verify it, you can just say finish

1274.1 -> editing you can give the name to that

1276.86 -> collector. So, let me just say finish

1279.62 -> editing here. So, it takes few seconds and

1281.9 -> then your new collector is ready. You can

1283.82 -> edit collector name you can call it

1285.559 -> whatever T20 World Cup match summary

1291.74 -> okay? You saved it if you just cancel

1295.94 -> that icon, you will see that collector.

1297.74 -> Now you can run that collector manually.

1300.44 -> If you want to edit the code you can

1302.12 -> once again go inside. So, this way you

1305.36 -> create four different collector. Again, I

1307.76 -> have provided the code to you. If you

1310.58 -> need any help you can click on the help

1312.74 -> button, you can do learning you can watch

1314.6 -> Bright Data tutorials on YouTube as well.

1319.64 -> We are done with web scraping part, and

1322.159 -> the files that we extracted from ESPN

1324.86 -> cricinfo are available in T20 Json files

1329.059 -> folder. Now, if you want to use these

1331.159 -> files readymade check video description

1333.2 -> below you should be able to download

1334.58 -> these files. And if you look at these

1336.799 -> four Json files for example batting

1338.96 -> summary,

1340.1 -> we have this batting summary Json

1343.28 -> element for each match. For example

1345.2 -> Namibia vs Sri Lanka So, there is one

1347.78 -> element in this array. Then the second

1350.12 -> element in this array is UAE Vs

1352.46 -> Netherlands and so on. Now, when we pull

1355.76 -> this data in Power BI it would be

1358.039 -> beneficial if the data is available in

1360.26 -> CSV file, something like this where you

1364.039 -> know it's just a single flat table and

1366.14 -> you have cricket match and the

1368.659 -> corresponding batting score right, and

1371.179 -> then see here also there is UAE and the

1373.58 -> batting score and so on, and here is the

1376.1 -> batting position. So we need to do

1378.919 -> transformation basically. We need to

1380.84 -> transform

1381.98 -> this Json file into this particular CSV

1386.659 -> format. You will also see an additional

1388.94 -> column. For example if the player is out

1391.76 -> or not out in the Json file we did not

1394.58 -> have that information okay? Uh all we had

1397.94 -> was let's say if dismissal is blank that

1401.299 -> means the player was not out. But if the

1403.76 -> dismissal had some string that that

1406.1 -> means the player was out. So, we have to

1408.679 -> do this data transformation and Python

1411.38 -> pandas is probably the best way of

1414.559 -> performing this transformation. If you

1416.84 -> don't know about Python programming

1418.28 -> language, you can go to Codebasics.io

1420.679 -> super affordable, easy Python course for

1423.559 -> total beginners, you can follow that. Then

1425.9 -> for pandas, search Codebasics pandas

1428.179 -> tutorial you find my playlist which is

1430.28 -> very popular, and just watch first you

1433.159 -> know maybe six or seven videos, and

1436.1 -> that's it it will take you less than one

1438.02 -> hour. Assuming you have now some Python

1441.559 -> and Pandas knowledge, now let's start a

1444.799 -> jupyter notebook and work on the

1447.679 -> transformation. I went to my cricket

1449.48 -> analytics folder and launched jupyter

1452.419 -> notebook by running this command and it

1454.7 -> looks like this. See I am at a location

1456.38 -> where T20 CSV files in Json file

1459.679 -> directories are there at the same

1461.179 -> location. I am going to create a new

1464.059 -> Python 3 notebook and import some

1467.539 -> necessary libraries. We are going to use

1469.1 -> pandas and other library called Json and

1472.76 -> then I will open the first file which is

1476.059 -> the match results okay? So I will say

1479.059 -> match results as F and then data is

1482.9 -> equal to in Json you can just say

1485.559 -> json.load file pointer, and it will load

1488.179 -> that data for you. So, let's print what

1489.86 -> kind of data it loads. So here I have the

1492.98 -> match summary, remember the match summary

1495.26 -> table. So, let me show you the Json file

1497.6 -> so that you get an idea.

1499.58 -> So, my match summary is this one where

1504.26 -> basically it's a match uh who won the

1507.44 -> match right? So between these two Namibia

1509.84 -> won the match by this run and so on. And

1512.24 -> if you look at match summary element. See

1514.34 -> it's it's just one element and if you

1516.08 -> look at this array this array also has

1518.179 -> one element

1519.44 -> okay? So, let's look at that element. So,

1523.22 -> first of all data array has only one

1525.679 -> element, you can confirm it by see

1527.96 -> printing just one element, and then

1531.5 -> data 0 and then match

1535.64 -> summary if you print

1537.74 -> that's your main list where you have all

1540.62 -> the match results. So, what I can do is I

1543.62 -> can create a dataframe out of it and I

1546.38 -> will just say df_match is equal to PD

1551.299 -> .

1552.919 -> dataframe

1555.679 -> in the data frame you can supply the

1558.08 -> entire list

1559.34 -> as an array, and if you print

1563.72 -> that head of that dataframe see

1566.659 -> wonderful! So, my dataframe is created

1570.14 -> and I can just quickly check how many

1574.279 -> elements it has and there is a method

1576.679 -> called shape, when you do that it prints

1579.38 -> the shape basically 45 rows and 7

1583.279 -> columns and I'm going to do a few

1587.6 -> processing steps which is let's see, I

1590.299 -> will use uh this scorecard as kind of a

1593.96 -> key of this particular data frame. What I

1596.84 -> mean by that is I want to treat this as

1599.179 -> a match ID so that I can connect with

1602.539 -> other tables because I have other tables

1606.26 -> and when when I import them in Power BI

1609.5 -> I need a way to link them. Sort of like

1612.44 -> primary, foreign key in SQL terms. So, I want to treat this scorecard which is a unique ID

1617.86 -> basically as a primary key for this. So,

1622.7 -> let's uh do that. So, I will just rename

1625.58 -> that column I will just say dfmatch.

1628.94 -> rename

1629.9 -> and how do I want to rename the column?

1632 -> So, I want to say that rename scorecard

1635.36 -> as match ID axis is equal to 1 which

1638.12 -> is you know you have column axis and

1640.4 -> rows axis and when you print head, you

1643.82 -> will see the column is rename. If you're

1645.44 -> not getting this just hold on later on

1647.84 -> you will understand why I am calling it

1650.36 -> a match ID. Now, once I have this

1653.36 -> particular dataframe I want to export

1656.48 -> this data into a CSV file okay, so that

1660.2 -> you know all the data all the data is in

1663.62 -> single CSV with nice columns. But before

1667.279 -> I do that, let me process the batting

1670.58 -> summary. So, the other file that I have is

1674.48 -> batting summary

1676.279 -> okay, so I will say

1679.179 -> batting summary you can add markdown

1683.48 -> columns, markdown rows in the notebook

1687.08 -> and let's see how this one looks like.

1690.799 -> So, batting summary has this particular

1694.1 -> format let's see in the notepad plus

1696.5 -> plus. Where is my batting summary huh? So,

1699.26 -> batting summary first of all the outer

1701.48 -> array has multiple Json objects, you see

1704.659 -> these are all multiple Json objects. If I

1707.24 -> open the first Json object that has a

1710.36 -> element called batting summary. See, it

1712.279 -> has just one element batting summary, and

1715.82 -> in that batting summary there is a score

1718.039 -> of one match. So Namibia Vs Sri Lanka

1720.74 -> see all the players number one player,

1722.84 -> number two player, they are in one

1724.88 -> element and if you close this and open

1727.22 -> the second element okay, you will find UAE

1731.179 -> Vs Netherlands match. So, all these

1733.82 -> matches are presented by one single Json

1736.82 -> element inside my array okay? So now what

1741.08 -> I will do is I will go through that

1743.539 -> those records, so I will say for

1746.799 -> record in data and this each record is

1750.679 -> one match and one match has multiple

1752.9 -> records for the player right? So if I

1755.48 -> print for example record

1759.159 -> batting summary, what I will get is the

1763.76 -> batting summary of one match. So Namibia

1766.159 -> Vs Sri Lanka okay?

1767.779 -> So this, this so I'll get total I

1770.24 -> think 11 records and if you want to just

1773.419 -> append everything in in one array right,

1775.82 -> because our eventual goal folks is to

1779.48 -> get a single list. Basically, I should

1782.36 -> have a single list where all the matches

1784.58 -> are present and if you want to create a

1786.5 -> single list in pandas, what you can do is

1789.38 -> I can create a

1791.48 -> list called all records, and then I can

1795.26 -> just keep on appending. I can just say

1797.12 -> all_records.extend

1799.76 -> and when I say extend, it is just extend is

1803 -> basically you have one array, and you are

1804.799 -> appending another array after that. So,

1808.1 -> let me just show you so let's say you

1809.72 -> have an array called a okay, and if you

1813.32 -> have another array called b, and if you

1816.86 -> say a extend b and if you print

1822.98 -> a you get this. So it's just joining

1826.34 -> those array okay? So similarly here I am

1829.76 -> joining all the records and after I join

1833.779 -> all of them, I can print all records.

1841.159 -> See I got all the records in a single

1844.46 -> flat list. So Namibia Vs Sri Lanka if

1846.86 -> I just scroll down I will see UAE Vs

1849.32 -> Netherlands and so on and from this I

1852.2 -> can create a dataframe

1856.36 -> dataframe like this,

1860.539 -> and when you print

1863.6 -> see

1864.86 -> I get the this is a continuous so if you

1868.1 -> print tail of it you will see the final

1871.1 -> match Pakistan Vs England I don't

1873.2 -> know if you have seen that it was pretty

1875.659 -> interesting match, World Cup final 2022.

1879.44 -> Now, that I have a dataframe ready uh

1884.059 -> let's let me look at I just want to do

1886.82 -> some analysis I'll just print let's say

1888.44 -> first 11 elements

1890.419 -> or so. And see I want to do couple of

1893 -> things here first of all here the

1896.12 -> dismissal column, I want to convert this

1898.46 -> column into out or not out okay? So, I

1901.82 -> want to have a column like this which

1903.32 -> tells me if the player is out or not out.

1905.419 -> And the way you can do that is look at

1907.76 -> the dismissal column and if you don't

1909.86 -> find anything in it, if it is blank, it

1912.38 -> means the player was not out. So, let's

1914.179 -> first do that and how do you do that?

1916.82 -> Well, you can create a new column called

1921.08 -> out or not out

1922.88 -> in pandas just by doing this based on

1926.24 -> the dismissal column. So, my dismissal

1928.52 -> column is this

1930.2 -> okay and on this you can use apply

1934.279 -> method. If you've seen my pandas tutorial

1936.2 -> you will know.

1937.279 -> So you can say on dismissal column apply

1941.299 -> some transformation and create a new

1943.159 -> column called out or not out. So this is

1945.559 -> the new column, this is how we create a

1947.659 -> new column, and on the existing column I

1950.84 -> want to apply some transformation. What

1952.64 -> is that transformation? Well, Lambda x

1954.5 -> Lambda is this is just a short way of

1957.74 -> writing Python function. So, here I am

1960.32 -> going to use ternary operator. So, I'll

1962.6 -> say

1963.679 -> player is out if

1966.62 -> X when I say X you you're getting each

1969.86 -> value from this column okay, so if let's

1971.96 -> say X is this if it is out, if the length

1976.34 -> of X is greater than 0,

1980.179 -> else it is not out

1986.6 -> okay?

1988.399 -> So, I will

1991.82 -> let's see okay

1994.1 -> df is not defined obviously. I am used to

1996.919 -> writing df all the time.

1998.779 -> So, you see uh you have out and not out,

2002.32 -> and if I print few more records you will

2005.2 -> see whenever there is a blank it is not

2007.6 -> out. Otherwise, it is out okay? All right,

2011.019 -> now that I got out not out I don't need

2013.779 -> this dismissal column. So, I can just drop

2016.48 -> that column okay and this is how you

2018.46 -> drop it you will just say drop which

2020.14 -> column you want to drop and in place is

2022.24 -> equal to true. If you don't specify in

2024.22 -> plus is equal to true it will not modify

2026.74 -> that dataframe. It will be a new

2028.299 -> dataframe okay? So, when you run this,

2032.46 -> dismissal is gone,

2034.539 -> and all you have is

2036.88 -> out and not out, and you have another

2039.039 -> column called batsman name which has

2041.2 -> some issues, which is see this kind of

2043.48 -> special characters you have. You want to

2045.399 -> remove this special character and you can

2048.82 -> just simply locate all the special

2051.339 -> characters and then you know apply some

2053.679 -> Lambda function. For example

2056.32 -> you can say dfbatsman.apply this

2060.22 -> you can use regular expression, you can

2062.32 -> use a replace function, you can do n

2065.74 -> number of things. But just to keep things

2068.02 -> simple, I will just uh you know I found

2071.5 -> this as well as there are there are

2073 -> other records where I found this

2074.44 -> character. So I'm just removing them and

2076.899 -> now you see in Kusal Mendisa, uh Mendis

2079.659 -> actually, you don't see let me see,

2083.08 -> you don't see that particular character.

2086.379 -> So, let's see yes see here you don't see

2090.639 -> that extra character.

2093.339 -> All right! Now,

2096.04 -> how do you connect

2098.619 -> this particular dataframe,

2101.619 -> with the match because as I said for our

2105.16 -> visualization purpose later on in Power

2107.08 -> BI we need a way to link all these

2109.119 -> tables. Now, just carefully notice these

2111.88 -> two tables okay? So, this is one table

2115.18 -> that I have

2116.68 -> okay,

2117.82 -> and this is the second table that I have

2120.52 -> I can just use Snipping Tool and just

2122.8 -> kind of take a screenshot of that

2125.68 -> so that you get an idea. So, let's say I

2127.839 -> have this particular table right

2131.32 -> and then I have this another table. So,

2135.339 -> now let's try to match these two. So, I

2137.8 -> want to now connect these two tables. How

2141.28 -> do I do that? See I have Namibia and

2145.119 -> Sri Lanka right? I have team name and

2148.06 -> here I have Namibia Vs Sri Lanka I

2151.24 -> mean that is the only key I have between

2152.98 -> these two tables, so that I can join them

2154.839 -> or I can link them basically because I

2157.599 -> have scorecard here, but I don't have a

2160.3 -> scorecard here in this particular table.

2162.94 -> So though yeah the only thing I have is

2165.04 -> match right? So, here it says Namibia

2167.02 -> Vs Sri Lanka here it says team one

2168.88 -> and team two now I can say maybe take

2173.2 -> team one and then use Vs in the middle

2176.2 -> and then do team two right and that way

2179.14 -> I can join them. But the problem could be

2181.599 -> in in this table the names could be

2183.94 -> reverse right? So here let's say it's

2186.7 -> Netherlands Vs UAE whereas here for

2191.02 -> example

2197.099 -> uh let me print this

2200.98 -> See here I have UAE Vs Netherlands, see

2204.96 -> so if I just simply say Team 1 Vs

2207.94 -> Team 2, it's not gonna work. I have to

2209.92 -> use both the combination team 1 then

2212.56 -> Vs then team 2 then team 2 Vs and

2215.02 -> team 1 so that is the way I can

2217.96 -> connect,

2218.8 -> and for doing that

2220.96 -> I need to go back to this code and

2223.9 -> create

2225.04 -> a kind of like a dictionary okay? So

2228.64 -> dictionary

2230.38 -> like this. So let me just do that here, so

2235.18 -> I want to create a match IDs dictionary

2239.32 -> okay and the dictionary will look

2240.94 -> something like this: So, let's say there

2242.74 -> is Namibia Vs

2247.619 -> Sri Lanka

2250.66 -> okay? So, let's say it looks something

2252.22 -> like this,

2254.2 -> and then

2256 -> I can have maybe a match ID as a value

2258.28 -> right?

2259.54 -> And then

2261.099 -> I can have same thing

2264.3 -> but in the reverse order.

2268.48 -> Why I need this because

2271.78 -> the order is not guaranteed. So I need to

2274.06 -> have both,

2275.32 -> and then I have let's say Netherlands

2277.72 -> Vs UAE correct? So, let's say I have

2281.2 -> this kind of Python dictionary where I

2284.079 -> have Team 1 Vs Team 2, Team 2

2286.66 -> Vs Team 1 and the match ID then that

2289.119 -> is going to be

2290.5 -> helpful. So I I can create that

2293.38 -> dictionary by going through so let me

2297.64 -> just remove this, I think you've got an

2299.32 -> idea or let me put it in the second cell.

2303.16 -> So, I can say for index row in dfmatch

2307.78 -> .iterrows So, there is a function called

2310.24 -> iter rows which will go over each row

2312.94 -> one by one and then every row has

2317.68 -> Team 1 and Team 2

2320.32 -> okay, and you can use that as your key

2325.359 -> one.

2326.5 -> So this will be your key 1 correct,

2329.8 -> Key 1 because see Namibia Vs Sri Lanka

2332.8 -> so I'm creating that by doing this and

2335.38 -> then first doing Sri Lanka Vs

2336.76 -> Namibia you can have key 2

2339.7 -> So Key 2 is nothing but just you know

2343.359 -> you're reversing the order: Team 2

2345.04 -> Vs Team 1 and then you want to add

2348.94 -> that into this dictionary. So, here I will

2351.46 -> say add this into my dictionary key 1

2356.26 -> okay so key 1 is this right and what

2359.14 -> is the value you are adding the match ID,

2361.72 -> so you will say

2363.22 -> row

2367.9 -> match ID

2370.24 -> and you will do the same thing for

2373.839 -> key 2

2376.54 -> and when you look at now match_ids_

2380.079 -> dict

2380.98 -> it looks something like this. See

2383.619 -> every team, their original order, reverse

2386.92 -> order and the corresponding match IDs dict

2389.98 -> Now, you'll be like okay so how do I use

2392.619 -> this dictionary? Well, see okay let's go

2396.04 -> back.

2397.96 -> So, the way you use this dictionary is to

2402.339 -> bring match ID column in this particular

2404.98 -> dataframe. So how do I bring match ID

2406.96 -> column in this particular dataframe?

2408.52 -> Well, this has a match column right? So, I

2411.88 -> can look into my match IDs dictionary.

2415.06 -> So you see if I for this match I want to

2417.579 -> get a match ID how do I get it? So I will

2420.22 -> say match IDs dict and this okay in quote,

2428.92 -> so if you give this see you get the

2431.74 -> match ID!

2433.06 -> So, I can create a new column in this

2435.4 -> dataframe okay, I can create a new

2440.44 -> column in this dataframe call it match

2444.88 -> ID equal to

2453.06 -> batting.

2457 ->

2458.5 -> so I want to apply transformation on

2460.48 -> this match column so I will say on this

2463.72 -> column apply transformation map is sort

2466.78 -> of like is a function similar to uh

2469.54 -> apply and you are going to give this

2472.66 -> mapping. So you are just mapping it

2474.64 -> basically okay, and when you do this you

2478.72 -> get

2479.74 -> match ID. See, now you found a way to link

2482.92 -> these two tables okay? And then you will

2486.16 -> export

2487.54 -> this particular table as is into a CSV

2491.02 -> file, and the way you can export this

2493.78 -> table is by doing this.

2499.66 -> Okay, I have that file open by the way

2501.94 -> that's why. But I'll just say temp.csv

2504.339 -> for example and when I see temp.csv

2508.839 -> see in my

2511.24 -> CSV files folder, see temp.csv

2516.82 -> and it looks something like this: it is

2519.04 -> same file as I was showing before

2520.68 -> basically it is this file okay? Now, in

2525.04 -> the interest of time I'm not going to go

2526.839 -> over the entire transformation for other

2529.18 -> files too because the code can be bigger

2531.64 -> and the tutorial will get so long. So, I'm

2534.28 -> going to share with you the entire

2536.079 -> notebook you can take a look later on.

2538 -> But see here I first process match

2541.48 -> results, then I process batting summary,

2543.88 -> and at every point I am exporting the

2546.64 -> file. So I exported match summary first,

2548.94 -> then I exported the batting summary, and

2553 -> then I exported bowling summary. See

2555.82 -> bowling summary and the player

2557.859 -> information. Now player information the

2560.74 -> name I have given is no images because

2563.02 -> for player I need their image as well. So,

2566.32 -> I have two files like player CSV with no

2569.02 -> images. So let me show you that.

2571.18 -> Let me remove this stamp.csv and see I

2574.72 -> have dim players.csv and no images so no

2577.96 -> image is something that my notebook is

2580.54 -> giving okay, and you can see there is

2583.3 -> player name, there is team, batting style,

2586.3 -> bowling Style

2588.579 -> player role and the description. Some

2591.46 -> players don't have description but some

2593.38 -> players do. So,

2595.3 -> and in this file we have manually added

2598.839 -> their images so we collected their

2601.359 -> images manually,

2602.8 -> and we added it here in dim players.csv

2605.98 -> So, if you open that file you will see an

2608.079 -> extra column okay? So I have dim players

2611.74 -> open looks like already so in that we

2614.26 -> have an extra column called image, and if

2617.5 -> you click on this image you will see the

2620.319 -> player's picture basically right? See if

2623.38 -> you click on any any image

2626.319 -> you will see that particular player's

2629.74 -> image all right? So, I hope you had fun

2633.339 -> and now in the next step we will be

2636.7 -> importing this CSV file into Power BI

2642.94 -> I'm going to launch Power BI desktop. I

2646.24 -> have already installed this application.

2647.56 -> If you don't have this you can just

2649.48 -> YouTube and find the installation

2651.7 -> instruction. So, Power BI desktop click on

2653.92 -> that,

2654.7 -> close this and just say file,

2657.64 -> save as

2659.26 -> you know just go to downloads and just

2661.48 -> give some file name. I will just say T20

2663.64 -> cric

2665.02 -> and then you will go to get data click

2669.04 -> on more and import the entire folder of

2671.68 -> CSV files. Now, if you look at our

2673.619 -> downloads folder, so just check video

2676.06 -> description below I have given the

2678.4 -> instruction to download the CSV files. So,

2681.16 -> these CSV files either you have used

2683.5 -> Bright Data to capture it or you can use

2685.66 -> ready made files which I have given to

2687.64 -> you and here in this folder you see five

2691 -> CSV file. Now for dim players we have two

2693.52 -> versions: one with images, one without

2695.079 -> images. So, I'm going to delete the no

2697.119 -> image CSV file because that's not going

2699.46 -> to be useful, and I will here click on

2702.579 -> folder connect and go to that folder and

2705.819 -> grab all those four files. So, this PC you

2709.24 -> go to downloads T20 CSV file okay, it's

2713.98 -> going to import all those four files. Now,

2716.319 -> here you will click on transform data to

2718.78 -> perform

2719.859 -> data transformation in Power Query. So,

2722.68 -> Power Query is a component inside Power

2724.96 -> BI that allows you to do data

2726.76 -> transformation. Here, right click and say

2729.94 -> duplicate I'll tell you why I'm doing

2731.68 -> that. But go to the first step and then

2734.94 -> click on this binary okay? Now, when you

2738.099 -> click on that

2739.24 -> it's gonna expand that file. So, our first

2742.119 -> file, if you look at the steps on the

2743.74 -> right hand side,

2745 -> see the first file is dim match summary

2747.099 -> and when we clicked on it what it did is

2749.859 -> it expanded that particular file. So, I

2753.46 -> will just call this

2756 -> dim match

2758.56 -> summary,

2760.66 -> and similarly I'll just duplicate

2764.4 -> these raw steps multiple times,

2767.68 -> and then here I will expand dim player.

2770.74 -> So, once again click on this and it's

2772.54 -> going to expand it I will call it dim

2775.42 -> players

2777.819 -> and do similar things for next two files

2787.02 -> okay?

2788.98 ->

2800.859 -> Now, that I have expanded all the files I

2803.2 -> will quickly look at the data and

2804.88 -> perform some transformation. When I'm

2806.319 -> looking at dim players I see it did not

2808.78 -> recognize the column names properly. This

2811.24 -> can happen. So, what you will do is you

2814.599 -> will say go to the transform tab and

2817.599 -> then use first row as headers. So see now

2820.96 -> it is using name, team, image, etc. In the

2823.72 -> previous step if you look at it it did

2826.24 -> not have that right? It was having column

2828.46 -> 1, column 2, etc. and now I said use

2831.16 -> this first row as a header row.

2833.56 -> So, after that it looks something like

2836.319 -> this and when I glance through the data

2839.68 -> I I noticed couple of issues. Number one

2842.14 -> is the player name has this in bracket C,

2845.319 -> this is captain basically. So in the

2847.24 -> cricket scorecard if someone is a

2849.579 -> captain in bracket you will see C. I

2851.8 -> don't need that I just need a player

2853.48 -> name. So, you are going to apply some

2857.14 -> transformation where you'll use extract

2859.18 -> option and you will say text before

2860.92 -> delimiter. So, delimiter is bracket right?

2863.74 -> So, you want to get text before the

2866.2 -> bracket so in this underscore C if you

2869.02 -> get text before this bracket you'll get

2870.94 -> only Shakib Al Hassan. So, you'll say okay

2875.04 -> and you see that C is gone. So in the

2877.599 -> previous step you see Shakib Al hassan in

2880.72 -> bracket C, but in the next one that C is

2883.42 -> gone. It is a usual practice to do data

2887.26 -> trimming after this delimiter step. So

2890.859 -> you will say format and trim and that

2893.26 -> will just in case if there are extra

2895.18 -> spaces it is going to

2897.7 -> remove those.

2899.38 -> One other thing that I do is

2902.859 -> I can sort these values. So if I say sort

2906.64 -> ascending

2908.02 -> and when you just quickly glance through

2911.02 -> these values, you will find some

2912.4 -> duplicates. See Matthew Wade

2914.74 -> the same record due to some reason

2916.98 -> appeared two times, and you want to

2919.3 -> remove this duplicates. In real life you

2922.24 -> will always find this kind of data

2923.98 -> problems okay? So I'm gonna now remove

2927.04 -> sorted rows here and then I will say

2930.22 -> right click and remove duplicate. So, that

2932.14 -> will remove the duplicates value see

2934.42 -> right now the rows is 213 and in the

2938.079 -> previous step we had 219 rows so it

2940.9 -> removed six duplicates. Many times what

2945.04 -> will happen is you are building

2947.56 -> dashboard at that time you will

2949.3 -> encounter issues and then you have to go

2951.819 -> back to Power Query and perform this

2953.92 -> data transformation okay? I kind of knew

2955.96 -> about this steps so I'm doing it but in

2958.66 -> general you will glance through it you

2960.16 -> will sort it you'll do bunch of uh

2962.44 -> things to figure out all these data

2964.3 -> issues. And, now we are done with uh dim

2967.079 -> players uh data transformation. We can

2970.66 -> move to the next one which is dim match

2973.18 -> summary. Here

2975.04 -> when I glance through this data it all

2977.56 -> looks good, the only thing I'm going to

2980.14 -> do here is create a new column called

2982.96 -> stage. Now in T20 cricket any match in

2987.099 -> this particular World Cup of 2022 any

2989.56 -> match that was played before 22nd

2992.2 -> October, was a qualifier match, otherwise

2995.5 -> it was considered to be the Super 12. So,

3000.119 -> let me show you that so the logic here

3003.18 -> that we're going to use is if date is

3007.2 -> less than 22nd October 2022, then

3011.46 -> the match was qualifier okay?

3014.42 -> Qualifier otherwise it was super 12.

3018.839 -> So, I'm going to create a new column

3020.94 -> called stage and it will be a

3022.92 -> conditional column. So, let me just show

3024.42 -> you so that you get an idea. Click on ADD

3026.76 -> column and say

3029.099 -> conditional column and in that

3031.14 -> conditional column you have a match date,

3033.599 -> see match date.

3035.28 -> So

3036.599 -> the new column name by the way is stage

3038.64 -> stage could be qualifier or super 12 and

3042.06 -> if match date is

3045.54 -> before,

3047.7 -> before which date well 22nd October

3050.819 -> so I'll go and say 22nd October okay, I

3054.599 -> just know it it's just a one fact that I

3057.119 -> know and if that is the case then this

3061.26 -> stage was qualifier.

3063.839 -> You know in any tournament there is

3065.339 -> qualifier and then there is a main

3066.78 -> tournament otherwise it is called super

3071.16 -> 12.

3073.2 -> And, when you say okay see you find

3075.66 -> something like this. So this was

3078 -> all of these were qualifier, and then

3080.4 -> then tournament started from here.

3082.7 -> Actually, it's not less than equal to it

3085.14 -> is less than this so I'm just going to

3086.76 -> modify the formula and just hit OK.

3090.059 -> So this is Scotland Zimbabwe was the

3092.579 -> last qualifier match, and then

3095.76 -> uh from next match onwards uh we had a

3099.96 -> main tournament which is super 12. So

3102.24 -> just to summarize I created this new

3104.579 -> stage column here, and then I'm gonna

3107.04 -> change the type to text. So here ABC123

3109.619 -> means text or number. So, I'll just change

3111.96 -> it to only text,

3115.98 -> then the next one is fact bowling

3118.619 -> summary so I go here and then I will

3121.74 -> just rename few columns. For example

3123.72 -> bowling team I can just say team. So

3125.52 -> double click this and just say team okay?

3127.8 -> And then you have things like 0s. So

3130.619 -> I'll just say 0s instead of having

3133.559 -> 0s I I feel zeros is a better column

3138.059 -> name. So, once again you will see that you

3141.24 -> might be renaming a lot of columns. So,

3143.579 -> here I will say fours sixes and so on.

3149.04 -> Now see for calculating

3158.839 -> statistics for bowling performance

3161.819 -> I am gonna do some transformation with

3163.8 -> over. So many times you know you have 2.5

3166.5 -> over if you just 2 over and five balls

3168.599 -> so it is better if I have a column

3170.4 -> called balls, and then if I run then I

3173.339 -> can divide run by balls to get the

3175.14 -> bowling of strike rate and so on

3176.88 -> therefore I will just uh you know create

3180.66 -> a balls column from this,

3183.9 -> and the way you do that is you go to

3185.94 -> overs and then you first split the

3189.24 -> column okay by delimiter, and that

3191.099 -> delimiter is dot. So you'll just say okay

3193.859 -> and it created two columns: overs 1 and

3196.92 -> overs 2. So, for example see 2.5 I I

3200.4 -> previously had 2.5 here

3202.74 -> okay? So, let's see 2.5 so it will now say

3206.52 -> 2 and 5 in one one and second column

3210.24 -> right overs.1 overs.2

3212.64 -> and then here in this column null values

3215.88 -> you can replace with zero right? 4 and

3218.099 -> null is basically 4 over. So this can be

3220.26 -> replaced with 0 and you can just go here

3222.72 -> replace values, null replace that with

3226.559 -> zero okay.

3229.22 -> And now you can add a new custom column

3233.339 -> so you will just say add

3235.14 -> create a custom column call that balls,

3239.7 -> and balls is nothing but overs 1 into 6

3243.359 -> correct?

3245.339 -> So,

3246.78 -> two overs will be how many balls? Two

3248.579 -> into six twelve and then if you have any

3250.44 -> additional ball you add that and that is

3252.24 -> overs 2. So, you insert that and then you

3255.24 -> get this particular formula. So if you

3257.7 -> look at uh different overs here for

3260.76 -> example

3261.78 -> uh

3264.48 -> let's say this is this first one is four

3266.76 -> over so that will be 24 balls correct? So,

3269.28 -> see 24 balls but if you look at this

3271.559 -> particular uh row let's say

3274.92 -> this row is 18 balls, but let's check

3278.099 -> this one

3279.119 -> this is 17 balls. So, 17 balls is 2 over

3282.48 -> and five balls so two or two or is 2

3284.819 -> into 6, 12 and then 5 is 17.

3289.319 -> See 17 here,

3294.18 -> and then let's perform some quick

3295.98 -> transformation in batting summary as

3297.96 -> well. Again, I'm gonna rename all these

3300.66 -> columns fours and sixes

3317.22 -> and I don't like that this column is in

3319.319 -> a text form. So, maybe I can have a simple

3321.839 -> column called out you know I can call this

3324.3 -> column out and if it is out then value

3327.18 -> is 1 otherwise it's 0 okay? So, not out

3331.079 -> is 0. So, how do you do that? So, you can

3334.2 -> just go to transform and say replace

3336.359 -> values, and if the value is not out, out

3340.319 -> is 0 and if the value is out once again

3344.28 -> you can just say replace values.

3346.5 -> So replace values if the value is out

3349.8 -> then that means 0. So, now we got nice

3353.28 -> binary 0 1 type of column.

3358.079 -> Not out is 0 and then out is 1

3361.319 -> actually

3363.3 -> okay? So see

3365.339 -> something like this.

3372.839 -> And by the way this balls column should

3374.64 -> be number. So, I'll just say this is the

3377.94 -> whole number,

3379.98 -> and in batting summary also you need to

3382.2 -> do similar transformation for captain

3384.18 -> where you remove the bracket C text. So,

3387.18 -> once again you click this and you just

3389.94 -> extract text before delimiter bracket

3393.78 -> okay, and you will see see that c thing

3397.079 -> is gone. See Gerhard whatever in bracket C

3400.079 -> and the next one it is gone. So, now we

3402.66 -> are done with our transformation, you can

3405.24 -> go to home close and apply.

3413.339 -> After data transformation, we need to

3415.74 -> look into Data modeling. For that, you can

3418.44 -> go to this particular tab

3420.42 -> and you will see it has already

3422.48 -> established some relationship based on

3425.819 -> column names.

3427.319 -> So, let me just pull fact tables in the

3430.2 -> middle okay?

3431.819 -> So, I am going to just arrange them

3434.46 -> nicely

3435.599 -> and fact tables are basically the the

3438.24 -> transactions and then the dimension

3440.76 -> tables are basically the attributes. So,

3444 -> you can just Google if you want to read

3445.68 -> more about fact dimension table and the

3449.04 -> star schema. So, now you can see that when

3452.94 -> you hover your mouse cursor here based

3455.52 -> on match_id it established

3457.859 -> this link between the two okay? It's like

3460.559 -> one to many see, one to many

3462.3 -> relationship similarly here also based

3465.66 -> on match ID established this

3467.339 -> relationship. Now we will link dim player

3469.44 -> table with this one so here the player

3472.02 -> name is basically bowler name here and

3475.319 -> that thing is called name here okay? So, I

3478.68 -> will left mouse click, drag and drop on

3481.74 -> name here and that will establish a

3484.859 -> relationship. When you hover your mouse

3486.54 -> cursor over here see bowler name and

3488.4 -> name are highlighted you which means

3490.559 -> these two tables are linked through that

3493.079 -> particular column.

3495 -> Same thing here batsman name is

3497.7 -> linked to name here, and that's it our

3500.64 -> data modeling in this case is pretty

3502.619 -> simple.

3503.88 -> Once data modeling is done,

3506.46 -> the next step would be to create DAX

3510 -> measures.

3511.559 -> So, DAX measures are something that we'll

3513.599 -> be using in building the actual visuals

3516.18 -> okay?

3517.02 -> So, for that here I will create a

3520.319 -> category or a folder where I can keep

3522.599 -> all those DAX measures. So, I'll click

3524.819 -> on enter data here and I will call that

3527.76 -> key measures okay, so key measures

3534.359 -> here is this key message 2, just ignore

3536.7 -> that. This is just a category where

3539.339 -> you're going to add all your measures. So

3541.92 -> here I will click on this and I will say

3545.52 -> new measure. Now we need couple of

3547.74 -> measures and I have given an Excel file

3550.079 -> again checked video description. You can

3552.299 -> download it from here I have given a

3554.579 -> complete list of measures that you need

3557.579 -> for this project. So, the first one is

3559.74 -> total runs okay? So,

3562.14 -> it's that one is pretty simple so you

3564.66 -> will say here uh you can click on this

3567.78 -> icon to kind of expand it and Ctrl

3570.119 -> scroll to make it bigger. So, total

3573.96 -> total runs is equal to

3577.2 -> sum of

3585.24 -> total runs is equal to sum of all the

3588.24 -> runs in fact batting summary table

3589.98 -> right? So, here

3592.74 -> fact batting summary

3597.74 -> runs and that's it.

3601.68 -> So, that creates a total runs uh measure.

3605.72 -> This column one is not needed so you can

3608.339 -> right click you can click and delete it.

3611.22 -> So, total runs is one measure that we

3614.52 -> have already created.

3616.26 -> Now,

3619.559 -> let's create the other one so the other

3621.78 -> one that we need is total innings

3625.02 -> batted okay and that one would be

3629.4 -> so once I can click here

3631.98 -> and I'm going to just copy paste that

3634.44 -> formula.

3636.059 -> So Ctrl C Ctrl V

3639.9 -> okay? So, fact batting summary and you're

3643.14 -> getting a count of match IDs and that

3645.119 -> will be the total innings batted.

3648.359 -> Now, what's the purpose of these measures

3650.099 -> exactly, if if you have no idea on DAX,

3653.64 -> etc.

3654.78 -> um I would suggest that you can check my

3657.78 -> uh course on Codebasics.io for Power BI

3661.26 -> where I have explained Power BI pretty

3663.299 -> much in detail with a lot of practical

3666 -> and fun learning where we completed a

3668.94 -> real life project with one and a half

3670.92 -> million records, and the course has

3672.599 -> received amazing reviews. So, go check

3674.52 -> that out I'm gonna go quickly through

3677.28 -> DAX measure creation because this is not

3678.96 -> a Power BI detail course correct? That's

3681.72 -> why. All right! So, let's say I have

3683.579 -> created these two measures and if I want

3686.7 -> to quickly check it what I can do is I

3689.52 -> can pull a player's name here and then

3692.46 -> in that table,

3694.859 -> I can add those measures. So, let's say I

3698.22 -> add total runs for example and I can

3701.52 -> look for a particular player. For example

3703.619 -> my favorite one especially in the last

3706.5 -> series was Suryakumar Yadav and here it

3710.52 -> says he scored 239 runs right? You can

3714.359 -> quickly check if the measure is correct

3716.099 -> or not by opening fact batting summary

3719.819 -> CSV file and when that file is open

3723.78 -> you can create a filter you can go to

3725.579 -> data and filter and you know here in the

3728.339 -> filter you can type in SuryaKumar Yadav.

3731.04 -> So let's say let's say you have a file

3732.9 -> like this you can say

3735.42 -> Suryakumar Yadav,

3737.52 -> and

3739.2 -> if you look at his runs if you if you

3741.42 -> just highlight this you will see 239

3745.14 -> and that's what I have

3747 -> 239

3748.5 -> right? Similarly, you can create other

3750.9 -> measures. So what other measures do we

3753.66 -> have here? So, let's let's check that.

3757.5 -> The other one is total inning dismissed

3760.319 -> okay? And total inning dismissed would be

3764.46 -> let's see.

3768.18 -> So here I'll say new measure,

3774.599 -> total

3777.18 -> innings

3778.92 -> dismissed

3781.26 -> is equal to sum of once again you can do

3785.4 -> Ctrl scroll to see the whole thing

3787.799 -> uh okay batting summary and out.

3795.599 -> So, how many times the player got out,

3798 -> okay total innings dismissed and when

3800.7 -> you pull that here.

3803 -> So, when I say total running dismiss here

3805.559 -> say I'm getting some error and if I

3807.66 -> click on this it says that function sum

3810.48 -> cannot work with value type string which

3812.76 -> means we created this measure based on

3815.099 -> this out column, and out column is not a

3817.26 -> number it looks like. So, you can go click

3820.14 -> here home click on you know transform

3824.76 -> data to go to the Power Query and you

3826.98 -> see the the out column in fact betting

3829.319 -> summary is ABC which means it's text. So

3831.78 -> you can change it to a whole number. So,

3834.54 -> just change it to whole number say close

3837.24 -> and apply,

3840.42 -> and then it will show up fine. So, Aaron

3842.94 -> Finch was out two times and and you can

3845.46 -> see all this uh statistics.

3847.619 -> In the similar fashion you will be

3850.559 -> creating all the rest of the DAX

3853.26 -> measure okay? So, once again this file is

3855.72 -> given to you, you download it, and I want

3858.72 -> you to create all these measures. And

3861.72 -> once you create all these measures it's

3863.46 -> gonna look something like this: See here

3866.579 -> it will look something like this so you

3868.74 -> will by the way create all these

3870.78 -> measures and also group them in these

3874.14 -> kind of folders. So how do you group them?

3875.579 -> So, let me show that so when you have

3878.28 -> these measures let's say these three

3880.079 -> measures right and if you want to group

3882.66 -> them

3883.74 -> uh what you can do is this you can enter

3886.859 -> a display folder here basically, you can

3888.9 -> say batting

3891.299 -> and it will put in a batting folder and

3893.22 -> then you just drag and drop okay? So, you

3896.64 -> drag it here drag it here

3900.66 -> okay? So, this way see you have a nice

3903.24 -> batting folder. So your goal by the way

3905.28 -> this is an exercise for you. Folks this

3907.14 -> is not hard. Don't worry, you can do it!

3909.299 -> Your goal is to create all the measures,

3912.18 -> you know I just showed you two, three

3914.22 -> measures but you have to create all

3916.92 -> these measures on your own, and just put

3918.9 -> them in respective folder. Once, you're

3921.839 -> done creating a measures you have to

3923.94 -> also create some calculated columns.

3927.02 -> Calculated columns is just a jargon.

3929.94 -> Actually, it's nothing but similar to

3932.22 -> Excel formula. So, if you have Excel

3934.2 -> formula for example let's say if I go to

3936.839 -> this CSV file

3938.64 -> and I remove this filter right, so let me

3942.24 -> remove that filter

3943.98 -> I want to know total runs from

3947.16 -> boundaries okay? So let's say boundary

3949.799 -> runs,

3951.78 -> if I want to do that I can use some

3955.98 -> formula in Excel correct? So, whatever

3957.9 -> let's say if you have whatever number of

3960 -> fours,

3961.02 -> into 4 plus whatever number sixes you

3964.68 -> have into six that will be your boundary

3967.859 -> runs correct? And if you drag and drop

3969.599 -> this formula like this, you will see the

3972.599 -> boundary runs. For example this guy

3974.22 -> scored one four and one six so that will

3977.099 -> make it 10 run. Similar to this Excel format

3979.68 -> we are going to create

3981.319 -> calculated column in our DAX. So, let's

3985.319 -> go to our file here and

3989.4 -> and by the way this this visual was just

3991.38 -> for your validation, so you can delete it.

3993.66 -> So, here in the batting summary I will

3996.42 -> create my first calculated column which

3999.599 -> is let's say boundary runs right? So

4001.94 -> boundary runs is this. So, how do you do

4004.4 -> that? So, you can go to this table view

4006.68 -> and you can say new column okay? And this

4011.119 -> new column will be this this is the name

4014.18 -> okay? So, let's say boundary runs

4018.079 -> and that boundary runs is equal to

4023.059 -> see that

4024.859 -> fact batting summary

4028.359 -> fours

4030.2 -> into four plus fact

4033.819 -> batting summary

4036.079 -> sixes

4039.079 -> into six if you have slight bit of Excel

4041.359 -> idea this is pretty straightforward. So,

4043.7 -> you create a new column here once again

4046.039 -> for validation you can look at anything

4049.099 -> here. See like this one for example

4051.859 -> uh this person,

4054.98 -> this person hit

4056.9 -> Pat Cummins two fours, one six, so two

4059.42 -> force is two into four eight, and one six

4062.059 -> is

4062.9 -> eight and six is fourteen, so fourteen

4064.88 -> runs. Pretty straightforward. So, create

4067.099 -> all these three calculated columns and

4070.22 -> once again if you want to look at the

4073.28 -> final of pbix file which I have given

4075.98 -> again check video description you you

4077.96 -> are given all the assets, and that final

4081.74 -> file would have

4084.38 -> see all those measures. So, anything which

4086.42 -> has this symbol is a calculated column.

4088.22 -> So, see it has boundary runs

4091.039 -> okay?

4093.14 -> So, it has pretty much

4095.42 -> everything that you need.

4100.219 -> The visual here correctly represents how

4103.04 -> much attention dashboarding gets, and now

4105.98 -> we are going to start dashboarding for

4108.38 -> our project. When you work as a data

4110.839 -> analyst in any company, a usually

4112.819 -> business managers will provide you some

4115.339 -> kind of rough mock-up with their

4117.199 -> understanding on how they want to see

4118.94 -> the dashboard. So, here they have given

4122.299 -> this image which they can draw on note and

4124.58 -> pen where they want to see different

4127.819 -> tabs for power hitters, anchors, fast

4130.88 -> bowlers. Remember previously we covered

4133.4 -> different criteria for each of these

4135.259 -> categories, and when you select any

4137.48 -> category let's say you select hitters or

4139.279 -> anchors you will see the players in that

4142.04 -> category along with the statistics such

4144.799 -> as their runs, strike rate, betting average,

4147.38 -> etc. And on the right hand side you want

4149.6 -> to have a criteria filter where based on

4152.179 -> our criteria we can see a list of

4155 -> players here and then at the bottom you

4157.04 -> will have some kind of trends for

4159.08 -> various statistics and bottom right

4161.12 -> would be the scatter chart between

4164.54 -> strike rate and batting average. Now, they

4167.9 -> can provide this mock-up in a rough

4169.88 -> format like this or sometimes people use

4172.239 -> PowerPoint or some other tool and they

4174.5 -> will just draw their rough ideas and as

4176.9 -> a data analyst it is our responsibility

4178.94 -> to communicate back. You know

4180.859 -> communication is a very important skill

4182.6 -> when it comes to data analyst career. So,

4185.299 -> we are going to provide you all these

4187.699 -> mock-ups the name of this file is

4191.319 -> mockup.txt which you will find in the

4195.14 -> video description below once again when

4197.06 -> you download all those files you will

4199.64 -> see this file.

4201.199 -> Now, I'm going to take this stage 2.pbix

4204.56 -> file which has all the DAX measures

4207.14 -> created you can also do the same you can

4209.36 -> just get that file and I will start

4212.42 -> building our visual. So, here the first

4218.06 -> thing that we are building is the page

4221.48 -> for power hitters,

4223.64 -> and what I will do is I will go to dim

4226.34 -> player table and grab the name of the

4229.58 -> player and just drag and drop here.

4231.8 -> So it shows me see all the names of

4234.739 -> every single possible player,

4237.14 -> and if you check our mockups we want to

4239.6 -> have certain uh columns in this field

4242.659 -> such as let's say team for example, then

4246.5 -> the batting style

4248.6 -> okay, then the Innings batted so you can

4252.02 -> go to key measures and you can say okay

4254.78 -> innings batted, and then total amount of

4258.08 -> runs that these players made,

4261.14 -> total balls that they faced, strike rate,

4266.3 -> their batting average, and so on.

4279.8 -> And once you have all these columns, you

4282.26 -> want to look at your criteria for

4284.6 -> openers, your power hitters, and here it

4288.199 -> says see their batting average should be

4290.3 -> greater than 30, strike rate should be

4292.58 -> greater than 140. So, now you will use

4295.34 -> this filter tab to filter those players

4298.76 -> because this list is showing all the

4300.86 -> players right? You want your batting

4304.76 -> average to be greater than 30, so I can

4308.179 -> say batting average should be greater

4310.159 -> than

4311.48 -> 30 apply filter and it will see filter

4314.42 -> all those players. Then you want a strike

4318.86 -> rate to be greater than 140, so I will go

4322.4 -> to strike rate and say is strike rate is

4327.26 -> greater than 140.

4332.96 -> So, strike rate greater than 140.

4334.64 -> Innings batted greater than 3, and

4337.219 -> boundary percentage is greater than 50. So,

4339.92 -> innings batted

4342.679 -> uh where is it okay innings that it

4346.94 -> should be greater than 3,

4350.3 -> and then

4353.719 -> boundary percentage greater than 50. So

4356.36 -> boundary percentage is greater than 50. 50

4359.3 -> percent is 0.5 right so that

4363.98 -> is greater than

4366.44 -> apply filter,

4368.12 -> and then the last one is betting

4371.659 -> position should be less than four.

4373.82 -> So, in whatever matches they played their

4376.76 -> batting position should be in in the

4378.5 -> opening somewhere. So, it is less than

4381.86 -> four.

4384.02 -> And when you apply all this criteria you

4386.659 -> see a nice list of players who can be a

4389.84 -> your potential power hitter in your

4391.88 -> final 11 team.

4394.94 -> You can also do some you know

4397.64 -> visualization related changes. For

4399.26 -> example total runs I want to see them as

4402.38 -> a bar chart, like a horizontal bar chart.

4404.3 -> So you can now go to visualization tab

4407.719 -> you can say total runs I want to do

4409.94 -> conditional formatting and I want to

4412.46 -> display the data bar charts, and the bar

4415.159 -> charts looks something like this. You can

4417.38 -> sort these columns as well by the way. So,

4419.9 -> if you click on it you can see the

4422.48 -> player with highest runs. We all know Jos

4425.78 -> Buttler I still remember their inning

4427.76 -> with India, Joe Butler and Alex Hales just

4431.36 -> killed it in the semi-finals.

4433.4 -> So, you see they have

4435.14 -> pretty good runs, uh strike rate, and so

4438.38 -> on. Now, folks building the whole

4440.239 -> dashboard is few hours of process, and I

4443 -> don't want to waste your time just going

4445.34 -> over all of that. So, I'm just going to

4448.34 -> show you

4449.42 -> a stage 3 file. So, once again whatever

4452 -> files you have downloaded

4454.1 -> okay video description check it, you will

4456.92 -> find this file called stage 3 and that

4459.86 -> has all the raw visuals created. So, you

4463.159 -> know you will have a page for power

4464.9 -> hitters for example, then you will have a

4467.3 -> page for anchors middle order okay, and how

4470.6 -> do you create this page? Well when you go

4472.219 -> to here this will be page one, so you you

4475.04 -> say okay power hitters or openers and

4477.92 -> then you create a new page and you

4479.659 -> create a new page for anchors or middle

4482.12 -> order right? So, anchors middle order use

4484.58 -> you just type double click and type in

4486.32 -> it. Anchors

4487.76 -> middle order and then you drag and drop

4490.4 -> and start building the visuals here. And

4492.92 -> when you build the visuals in the raw

4495.38 -> format, ultimately they will look

4497.6 -> something like this okay now if you are

4500.54 -> once again new to Power BI and you don't

4503.96 -> know the basics of these visuals, what you

4507.5 -> can do is you can go to Codebasics.

4509.78 -> io and just take our Power BI course.

4512.719 -> It's

4513.92 -> it covers all of those Concepts pretty

4517.46 -> much in detail. But for this video,

4520.46 -> I'm going to give you this stage 3 files

4523.1 -> so that you can check various properties

4526.04 -> of the visual in case you don't know how

4528.44 -> to build it. So, when you click on it uh

4530.659 -> see here it will show you what kind of

4532.58 -> visual it is. So, this one is a card

4534.32 -> visual and if you want to look at the

4536.54 -> formatting for example then you can just

4538.82 -> go click here and you can look at

4541.219 -> various you know properties. So, we are

4544.04 -> going to assume that you have built all

4546.26 -> these raw visuals and now it is time to

4548.54 -> beautify them, you know to make them look

4550.4 -> good and kind of connect different

4552.08 -> pieces. Once you beautify your dashboard

4554.42 -> it's gonna look something like this. Now

4556.94 -> this is what we have built but you can

4559.219 -> build it as per your own preference for

4562.1 -> colors and different visual behavior. We

4564.98 -> have provided you all the dashboarding

4567.199 -> tips here which you can use to build a

4569.719 -> dashboard. See, I'm not going to spoon

4571.88 -> feed you because when you go work in the

4573.8 -> industry,

4574.699 -> it becomes essential that you use your

4577.46 -> Googling skills to figure things out. If

4580.34 -> you are trying to change a color of some

4581.78 -> visuals okay, uh you can just Google it.

4584.54 -> Googling is an art that can be

4587.239 -> tremendously helpful and this is a

4589.28 -> unique opportunity for you to use that

4591.8 -> art to learn certain things on your own

4594.199 -> while we are providing You full

4596.3 -> assistance. So, read dashboarding tips and

4599.12 -> then try to make the entire dashboard

4601.28 -> look pretty good. In our Power BI course

4603.8 -> also, we have an entire chapter on

4606.08 -> designing an effective dashboard. Now

4609.38 -> you'll be glad to know that this

4612.14 -> particular dashboard is designed by one

4614.42 -> of our students. So, he took our Power BI

4616.82 -> course, his name is Ashish Babaria. He

4619.34 -> also participates in our Codebasic.

4622.1 -> io resume project challenges. So, if you

4624.44 -> go to the website to resume a project

4625.94 -> challenges which are free for everyone

4627.26 -> to participate, he has won our first

4630.56 -> prize in two challenges, and if you click

4633.56 -> on this LinkedIn icon you will see his

4635.78 -> post here where he builds beautiful

4639.14 -> beautiful dashboards. Now Ashish's

4641.78 -> background if you check it he's a trade

4643.58 -> agro specialist. So, he comes from a

4645.56 -> non-technical background. He learned

4647.9 -> Power BI. Look at his background, he's

4650.179 -> trade agro specialist. So, he doesn't

4652.1 -> have formal training on data analytics,

4654.08 -> etc. He learned Power BI mainly from Code

4656.36 -> basics channel and various other

4657.8 -> resources, and by participating in resume

4660.14 -> project challenges we were able to

4663.02 -> spot his challenge and he is now working

4666.62 -> with us as a freelancer, and he's helping

4668.659 -> us with all these projects. So, look at the

4670.58 -> quality of and professionalism of this

4673.04 -> dashboard. He learned things on his own

4675.62 -> at a later part of his career, and you

4678.679 -> can become like Ashish too. Now, you can

4681.8 -> click on various player categories. So, in

4685.4 -> the power hitters you want to have few

4687.38 -> players from this particular list, and if

4690.08 -> you look at the the filter criteria see

4692.84 -> this is all the filter criteria that we

4695.239 -> had. Now, look at the filter criteria for

4698.42 -> example for fast bowlers right? So, in

4702.26 -> fast bowlers you want to have bowling

4704.659 -> economy to be less than seven and they

4706.58 -> should be taking uh wicket every 16

4709.58 -> balls. So, if you go to our fast bowlers

4712.76 -> control click on that and if you click

4715.52 -> on this visual, you will see all the

4718.58 -> filters that we have okay? So see, bowling

4722.239 -> strike rate is less than 16, dot ball is

4726.14 -> greater than 40%, and so on. So, you

4728.6 -> see all those uh details here, and if you

4731.719 -> want to modify any criteria it's super

4733.88 -> easy. You just go here type in and you

4736.219 -> can play with uh different things, and in

4739.4 -> the next part of this uh session what

4743.239 -> we're going to do is invite Tony Sharma

4745.52 -> who is a subject matter expert and in

4748.04 -> charge of this project. He will help us

4750.58 -> decide the final 11. So, if you control

4753.38 -> click on it see we have our final 11

4756.739 -> almost decided. But you can modify

4759.56 -> certain criteria, substitute different

4761.42 -> players and if you look at this team if

4763.76 -> you know a cricket just look at this

4765.679 -> team. It looks pretty solid like

4767.96 -> unbeatable team. So see, Power BI data

4771.62 -> analytics can help you generate data

4774.92 -> driven insights uh that can make a huge

4777.56 -> difference in the problem that you are

4779.3 -> trying to solve. We are going to provide

4781.159 -> you this final file also so if you're if

4783.8 -> you have a question on some visual

4785.3 -> behavior, you can click on it and you can

4787.64 -> you know check or the different visuals

4790.699 -> that we have. You can look at format and

4793.94 -> their you know various properties that

4796.4 -> these elements are having and I will

4798.44 -> quickly play a time lapse view so that

4800.78 -> you you have some idea on how this

4803.179 -> visual was built.

4806.75 -> [Music]

4822.61 -> [Music]

4837.4 -> thank you

4838.55 -> [Music]

4856.219 -> So, Nick we are just few minutes away

4858.56 -> from saving the world. How does this

4861.32 -> dashboard look?

4862.719 -> Dashboard looks amazing! But can you show

4865.52 -> me the best 11?

4867.26 -> Not yet. We are getting there just give

4869.48 -> me a few minutes. Let me explain how we've done this. So, in the previous session we had, I

4874.34 -> explained what are the parameters I'm using

4877.699 -> right and now you can see this

4879.26 -> parameters come into action, coming come

4881.239 -> live. So, this is how I've created the

4883.4 -> parameters. You can see how I filter the

4885.14 -> place for the openers, these are the

4886.82 -> power hitters and uh you know I also

4889.82 -> have like a solid graph of them like to

4892.64 -> understand their consistency, to

4894.199 -> understand their playing trend, and all

4895.64 -> of that and I also have a scatter plot

4897.86 -> to show

4898.88 -> how their batting average fairs their

4901.219 -> strike rate. So, you can see these are the

4903.5 -> players I've got here correct? So, these

4906.26 -> players will be able to strike ball at

4910.4 -> around you go I have players you can

4911.96 -> strike ball at 160, 170 even close to

4914.179 -> 170, at the same time give me an average

4916.34 -> of 35.

4917.9 -> You can see that, and Jos Buttler here

4919.82 -> gives me the highest average. But he's is a

4922.52 -> good striker as well. He strikes at

4924.26 -> 140 plus which is which fits our

4926.12 -> parameter. So, you can see that these

4928.159 -> players are there. So, out of these players

4931.04 -> I'm going to select Jos Buttler because

4934.28 -> you can see he's consistent all the

4937.88 -> matches, he has played all the matches

4939.739 -> pretty decently, and he's a good Wicket

4942.5 -> keeper as well. So, we need a wicket

4944.36 -> keeper. So, he's gonna be a wicket keeper

4946.159 -> batter,

4947.239 -> and his partner Alex Hales is a good

4949.58 -> choice, he's a good choice of a second

4951.199 -> opener.

4952.28 -> But again he is not consistent in all

4954.5 -> the matches. Also I need a left hand

4957.199 -> combination with a better strike rate. So,

4959.48 -> I'm going to choose Rilee Russouw from

4962.48 -> South Africa. He's a better option for me.

4964.699 -> He has a you know he can strike ball

4967.04 -> like crazy. I can show you the combined

4970.1 -> uh you know the performance of these both,

4972.199 -> I'm just selecting these both. So if these

4974.3 -> two players play together, Rilee Russouw and Jos Buttler

4977.199 -> if they play together, they will give us

4980.36 -> 40 runs in average at a strike rate of

4982.88 -> 150 plus.

4984.02 -> So, if they too bat without losing a

4986.84 -> wicket

4987.92 -> so we'll hit our Target of 180.

4990.44 -> If they bat 120 balls they will give us

4992.659 -> 180 runs.

4994.46 -> And they will stand at least for four

4997.34 -> overs on an average because the average

4999.38 -> ball spaced you can see it's 23.9,

5002.08 -> and they score runs at 60% in

5005.26 -> boundaries which is so so perfect to

5008.08 -> what we need.

5009.28 -> You can see the consistency is is pretty

5011.98 -> much there, it's it's dropping here and

5013.78 -> there but but they are you know they

5015.58 -> together as a package will give us what

5018.159 -> we need. I love this feature that you can

5020.62 -> select two players and see their

5022.659 -> statistics uh on average basis because

5025.659 -> this way let's say if these two players

5027.34 -> are playing and if I have questions on

5029.44 -> their partnership statistics, I can get

5033.04 -> the view of those numbers easily here. It

5035.8 -> is not exactly like a partnership but it

5037.6 -> will give you their combined performance.

5039.699 -> Got it! I got the point!

5042.4 -> Yes and uh so I'm also going to select

5046.42 -> Alex Hales just as my reserve opener.

5050.199 -> So, I'm going to select three players for

5052 -> this position potentially.

5053.8 -> But

5055.36 -> the the what the two I'm going to play

5056.98 -> is Jos Buttler and Rilee Rossouw. All right!

5059.14 -> Let's move to the anchors where we'll be

5060.699 -> select three players here

5062.44 -> again you can see the filters I applied

5064.9 -> on this data: batting average, strike rate,

5067.179 -> innings batted, exactly like we

5069.58 -> discussed right?

5071.26 -> Here the interesting part is um

5074.08 -> we have Virat Kohli on the top with most

5077.14 -> runs and then follows Suryakumar

5079.179 -> Yadav. So, let's check into this chart the

5081.58 -> scatter plot.

5082.78 -> Virat Kohli is clearly the winner

5084.219 -> because he gives us a lot of runs,

5086.14 -> he's a run machine we definitely need to

5088.78 -> pick him and the second player could be

5091.239 -> I couldn't think anything better than

5093.1 -> Suryakumar Yadav because you can see his average is

5096.4 -> 60. He could give us 60 runs on an

5099.82 -> average and he's striking at 190. This

5103 -> is the best we have in the team.

5104.98 -> So, even

5106.719 -> our openers did not score runs at a good

5109.3 -> strike rate this guy can come and

5111.46 -> propel the score.

5112.9 -> So we need him in the team for sure and

5116.14 -> these two players are solid they have a

5118.48 -> good partnership, it's a great idea to

5120.159 -> play them together. So, for my fifth

5121.96 -> position I'm gonna go with

5125.14 -> the three options have is like Lorcan

5127.659 -> Tucker, Glenn Phillips and Daryl Mitchell.

5130.9 -> So, I'm based on the statistics, based on

5134.32 -> the averages,

5135.64 -> it's easy for me to choose Glenn

5138.4 -> Phillips.

5139.659 -> So, even though he has a strike rate of

5142.54 -> 160,

5144.34 -> which is which is really high for the

5146.08 -> position, he scores at an average of 40.

5148.78 -> So, that's the reason I go for this he's

5150.46 -> clearly my number five. Okay. Right.

5153.219 -> And uh

5154.659 -> let's move on to the next one.

5156.52 -> So this is going to get slightly tricky

5158.739 -> because I have many players in this

5161.08 -> position uh you know whom we can pick. So

5164.86 -> like I said I'm looking for a batting

5167.08 -> all rounder here and that batting all

5169.84 -> rounder there could be uh you know

5172.06 -> batsman who can score runs at a very

5173.92 -> high strike rate, at the same time anchor

5176.44 -> the innings, or you know that this person

5179.08 -> can be a fast bowler or a spinner as

5181.84 -> well. In order to you know justify my

5184.48 -> selection let me come back to this place

5186.159 -> again. But like and I will go to my I'll

5188.62 -> go to select my specialist fast bowlers first. So, that

5191.08 -> it becomes very easy for me whether I

5192.82 -> need a fast bowler in the position or

5195.34 -> whether whether at all I need a bowler

5196.9 -> at all in the position.

5198.219 -> If I need a good bowler or a good

5199.9 -> batsman in that position, so I need I

5201.34 -> will select my fast bowlers first.

5203.139 -> So, here my selection is super super super easy.

5206.26 -> I'm I'm gonna select this guy: Sam Curran.

5210.04 -> You can see his economy is 6.53,

5213.76 -> and his bowling average is 11.38 which

5216.46 -> means he gets a wicket for every 11 runs

5219.1 -> that he provides, and his bowling strike

5222.52 -> rate is also staggering. He gets a wicket

5225.04 -> for every ten and a half balls which means if

5227.32 -> he balls the full four overs we are

5229.54 -> definitely getting two wickets. And look

5231.46 -> at this guy

5232.659 -> he has got 11 wickets at an economy

5235.239 -> currency economy of 5.37,

5237.639 -> he gives less than six runs in over. We

5239.8 -> definitely need him on our team and he's

5241.719 -> a fast bowler

5243.1 -> and he picks wickets in less than 10

5245.139 -> balls. So, these two are definitely in our

5247.179 -> team. And of course Shaheen Shah Afridi, how

5250.42 -> can we ignore him? The kind of player is

5252.94 -> is a left arm fast bowler and he can

5256.9 -> really rattle the batsman.

5259.719 -> He's one of my favorite!

5261.82 -> Yeah so you're picking this three. Tim

5264.28 -> Southee is good but if I have to pick

5266.139 -> three I'm just gonna be just three.

5268.3 -> So, these are the parameters I've applied

5270.1 -> here you can see that uh you know,

5272.44 -> innings bowled, bowling strike rate, bowling

5275.38 -> average exactly like we discussed in the

5277.3 -> parameter session.

5279.88 -> So, let's see the combined performance

5281.5 -> together just to get an idea. Sam Curran,

5283.659 -> Anrich Nortje,

5285.82 -> Shaheen Shah Afridi.

5288.04 -> You can see these guys will pick a

5290.739 -> wicket for every 11 runs they give, which

5292.48 -> means if these three bowl all the 20

5294.219 -> overs they will get the full team all

5296.56 -> out for 110 runs, 113 runs and they

5300.699 -> will get a wicket every 11 balls. If the

5303.699 -> bowl all the 20 overs, the team is all out.

5307.659 -> And they imagine they're bowling first

5310.36 -> three or four overs, they will let's say

5312.58 -> if these players they they ball first

5315.639 -> six over, how many Wicket they will take?

5318.34 -> If they bowl the first six overs they

5320.139 -> will pick a wicket for every 12 balls,

5321.58 -> six overs is 36 so they will pick at

5323.8 -> least three wickets that's that's on

5325.179 -> average. That would be awesome! If you

5327.04 -> pick three wickets in the power play!

5330.1 -> That's gonna be amazing! They'll

5331.6 -> definitely scalp the top three, they will

5333.46 -> open the middle order for us.

5335.86 -> And uh look at the economy the combined

5339.52 -> economy is six which means if they bowl

5342.159 -> the first six overs they will be just

5343.84 -> giving 36 runs, and the dot ball they

5346.659 -> produce is close to 50 percent which

5348.4 -> means if they bowl all the 20 overs

5350.199 -> the batsman is able to score only the

5352.36 -> half of the overs, like only the 10 overs

5354.46 -> the rest of the 10 hours they're scoring

5356.86 -> nothing, it's a dot ball. These three are

5359.98 -> the major strength to our team. All right,

5362.62 -> so let me come to the all-rounders now

5364.659 -> before I go to the finisher row.

5366.58 -> So, we have three solid fast bowlers. So, in

5370.48 -> my all-rounder I'm going to have the

5372.88 -> spin element,

5374.02 -> but at the same time I want these two

5375.88 -> players to bat as well, at a

5378.1 -> higher strike rate. So, that's the reason

5380.56 -> as I explained earlier I've kept the

5382.6 -> strike rate parameter as 140. If I don't

5385.06 -> consider the strike rate let's say if I

5386.679 -> get the strike rate is 100, I'm gonna get

5388.42 -> more players like even I get Ben

5390.34 -> Stokes here. But I'm considering strike

5393.4 -> rate 140. Oh got it! I was expecting to

5396.58 -> see Ben Stokes. Now, I know why he's not

5398.56 -> showing up. Yeah, yeah I will explain why

5400.84 -> Ben Stokes is not here in the other page

5403.36 -> as well because I wanted him at that, I

5405.04 -> wanted him at that role earlier.

5407.02 -> So, you can see from this graph Rashid

5410.32 -> Khan is really good in terms of uh you

5412.96 -> know the bowling strike rate,

5414.82 -> I mean I can't say it's really good

5416.739 -> because bowling strike rate should be

5417.94 -> lesser right, it's it's not good and uh

5421.239 -> you have Sikandar Raza

5423.699 -> who has the economy we kind of finding

5426.04 -> people in the zone this this zone is the

5428.02 -> best zone, who has the lesser economy and

5430.06 -> lesser bowling strike right. So Shadab

5432.1 -> Khan is really the winner.

5433.54 -> He definitely deserves a spot because

5436.06 -> you can see his performance, he's quite

5438.34 -> consistent as well. You know his bowling

5440.38 -> average is good whenever he got a chance

5442.06 -> to bat he has batted well as well this

5443.679 -> is his bowling and batting performance

5445 -> it's of high consistency. But Shadab

5447.76 -> Khan

5448.719 -> can I play him at number seven?

5450.76 -> I'm not sure because if my number six

5453.219 -> did not bat well I want someone more

5455.5 -> capable to bat at number seven. So, I

5457.719 -> would play Shadab Khan at number eight.

5459.28 -> But then at number seven I would play

5462.159 -> Sikandar Raza. This is the person I'm

5464.44 -> going to play at number seven because

5466.9 -> look at the strike rate it's 147 and he

5469.84 -> also has a very high average batting

5471.46 -> average of 27 runs. For a number seven

5474.04 -> he normally played number five or number

5476.02 -> four but for number seven if you have

5477.82 -> this kind of an average it's great with

5479.5 -> this strike rate. So, that's that's going

5481.78 -> to be my number seven, and number eight

5483.82 -> I've got my 9 10 11 as well. So, I just

5487.3 -> need to select my number six.

5490 -> So, again choosing this position is

5492.04 -> slightly difficult I would show you why

5494.139 -> uh

5495.76 -> you know let me take out the filter from

5498.46 -> this diagram right?

5500.32 -> You would see that I also have Ben

5502.48 -> Stokes here

5503.92 -> So, Ben Stokes could not make this list

5506.32 -> because his strike rate was too low in

5508.3 -> the series. His strike rate was just 105.

5510.52 -> If the batsman is scoring just run a

5512.38 -> ball we cannot have them in the number

5513.58 -> six because this position might require

5515.62 -> to hit

5516.76 -> like crazy.

5518.08 -> So, that's the reason Ben Stokes might

5520.239 -> not find this place

5521.679 -> so I'm going to say it's greater than

5523.06 -> 130.

5530.08 -> And again if I have to say that um whom

5536.02 -> I'm gonna pick from here. So, we have

5537.94 -> three fast bowler, two spinners. Glenn

5540.159 -> Maxwell looks like a good option. But

5541.6 -> he's a spinner again. I'm not sure

5543.58 -> whether my sixth bowling option should

5545.38 -> be a spinner. But his bowling average is

5547.84 -> good uh you can see his bowling is

5550.3 -> economy is six, uh he is is that is

5553.06 -> bowling uh strike rate is 6.33, which

5555.159 -> means he has got a wicket almost every

5556.48 -> six balls. So, he is one of my options. My

5559.12 -> other option is Marcus Stoinis. I'm gonna

5561.94 -> play with this guy. His bowling average is

5565.12 -> not that good, his economy is not that

5567.1 -> good, but he's a good striker of the ball

5569.08 -> and he's someone I can also trust to

5571.48 -> anchor the game.

5572.739 -> So, I could I could go for Marcus Stoinis

5575.02 -> if I'm if I want a better batsman with

5577.12 -> high striking rate or if I want someone

5579.52 -> more balanced, I could go with Hardik

5581.56 -> Pandya. So, this guy

5583.659 -> will give you a good batting average but

5585.76 -> his strike rate is also not not that

5587.5 -> great, and his consistency is also not

5589.3 -> that good in this world cup. But his

5590.86 -> bowling is good if I really need the

5592.9 -> sixth bowling option I would go for Hardik

5594.82 -> Pandya. But the kind of bowlers that I

5596.679 -> have Shaheen Shah Afridi, Sam Curran

5599.62 -> and Anrich Nortje and all those players I

5601.84 -> don't think I would need Hardik Pandya

5603.82 -> in my team. Then I will go for Marcus

5605.739 -> Stoinis.

5606.82 -> So,

5608.139 -> I will make my decision easier by

5610 -> showing the final eleven right? So, if I go

5612.28 -> to the final eleven,

5613.78 -> I've already picked the top 11 like I

5616.12 -> say right? So you can see this is our

5618.76 -> batting 11. Josh Buttler, Rilee Russouw

5621.4 -> will be opening the game, Virat Kohli

5624.159 -> followed by Suryakumar Yadav, Glenn

5626.139 -> Phillips and in this number six I've

5628.9 -> selected here Hardik Pandya. But I want

5631.9 -> to go with let's say uh Marcus Stoinis.

5638.08 -> You can see the combined performance of

5639.94 -> the team batting average is 37.76, the

5643.3 -> team on average cost 37 runs, strike rate

5645.699 -> is 150.

5647.44 -> You know all the facts, it's given here.

5649.84 -> But if I choose Marcus Stoinis since my

5652.78 -> sixth batting position should increase

5655.239 -> the batting strength I'm focusing more

5657.639 -> on the batting strength now because our

5658.78 -> bowling is already great,

5660.88 -> I'm selecting Marcus Stoinis

5663.34 -> and removing Hardik Pandya you would see

5669.76 -> my batting average improved from 37 to

5672.04 -> 39.6, and my strike rate improved from

5674.38 -> 151 to 154.4. So, this is the reason I

5677.679 -> would go for Marcus Stoinis. But I can

5680.02 -> also go for Maxwell right? You can see

5682.54 -> that the uh you know the performance of

5684.4 -> Maxwell and Marcus Stoinis is a very much

5686.08 -> comparable. They have, they have a very

5687.88 -> similar strike rate, almost very close,

5689.8 -> and uh Maxwell has bowled well. He has picked up

5692.92 -> wickets well. But he's a spin bowler, he's

5695.139 -> a right arm off break bowler. So, since

5698.5 -> I wouldn't need my sixth b owling option as

5700.6 -> someone who can you know who can bowl

5702.4 -> pace. I'm going for Marcus Stoinis.

5705.34 -> But on a given day we will still have

5708.82 -> options you can still choose Glenn

5710.62 -> Maxwell on a given day depending upon

5712.06 -> the pitch, if the sixth bowling option

5713.679 -> can be a spinner, then my second option

5715.659 -> is Maxwell. Uh if I need someone more in

5719.32 -> the bowling side, if if some of our fast

5721.06 -> bowlers get injured then I would choose

5723.159 -> Hardik Pandya for number six. So, I would

5725.44 -> keep all these three options open. But

5727.179 -> right now my team in the final 11, I

5729.639 -> would have

5730.84 -> uh Marcus Stoinis,

5733.3 -> and uh in the opening I would go for

5736 -> Rilee Russouw, but if you want Alex Hales

5738.699 -> he is also available for selection. Got

5740.8 -> it just yeah this list I am feeling

5744.699 -> super excited anyone who is following

5746.56 -> cricket

5747.58 -> if they look at this list of players,

5749.86 -> they will be like this team is

5751.54 -> unbeatable. This is the best team that we have got

5754.239 -> on our planet and I'm sure

5756.159 -> you would win this game and save our

5758.56 -> planet.

5759.52 -> So, Tony this team is great but I know

5762.219 -> that Mitchell Starc is a pretty good

5763.78 -> fast bowler from Australia. Can you tell

5765.88 -> me why he's not in the final 11?

5768.639 -> Sure I think he must have not made our

5770.38 -> standards. Let me check I'm pretty sure

5772.84 -> he's there but he didn't meet our

5774.28 -> standards. So, I think

5776.5 -> um if you recall it correctly

5779.08 -> maybe his bowling strike rate is

5781.179 -> definitely not under 16. So, let me take

5783.28 -> this out and uh maybe he didn't bowl

5787.06 -> dot balls like we want and this bowling

5790.78 -> average might not be less than 20, I

5792.76 -> should remove that as well and his

5794.199 -> economy is definitely not less than

5796.179 -> seven. Let me take that out

5798.159 -> and let me see if we can have him in the

5801.159 -> 11 don't think we have him yet. So maybe

5804.4 -> he didn't even uh play the four innings

5806.62 -> yeah you could find him here now Mitchell

5808.78 -> Starc,

5809.86 -> You can see his economy is 8.5 which is

5812.38 -> more than uh you know seven his bowling

5814.54 -> average is 34, his bowling strike rate is 24.

5817.239 -> So, that's the reason why he's not there

5819.46 -> and he has also played bowled just three

5821.679 -> innings. We wanted at least four innings.

5823.78 -> Yeah. That's the reason. All right, it's

5826.06 -> clear now.

5827.56 -> Okay, here's your final 11 Nick. Go get

5831.219 -> us the cup

5832.239 -> and save the planet.

5834.04 -> The future of this planet is focused on

5837.46 -> these 11 players.

5840.1 -> They are traveling to the wilderness

5843.88 -> out in the dark,

5846.159 -> to bring us light.

5849.58 -> And may we all,

5851.82 -> citizens of data hope and pray

5855.48 -> our analysis and insights shall work. Defeat

5860.62 -> the Sportans and bring us glory!

5864.77 -> [Music]

5870.4 -> Three weeks later! You defeat us in

5873.52 -> cricket, you get Earth!

5877.179 -> If you lose join me as an intern. Join me as an intern.

5886.12 -> Now comes the most interesting part of

5888.46 -> this entire project series, which is an

5890.56 -> exercise and by working on this exercise

5893.199 -> you will be able to win an exciting

5895.36 -> prize which is 20% scholarship on one of

5898.9 -> our premium courses on Codebasics.

5901.3 -> io For exercise what you need to do is

5904 -> number one in the tooltip for players

5906.94 -> you are seeing let's say if it is Surya

5909.1 -> Kumar Yadav you are seeing India Vs

5911.02 -> Pakistan, India Vs whatever team, you

5914.139 -> all know he is from India. So, you need to

5916.719 -> remove that IND and just have Vs

5919.6 -> Pakistan, Vs Sri Lanka. So, you need to

5922.36 -> update that tooltip that is exercise

5923.98 -> number one. Second one is you have to

5926.86 -> update the visual look and feel of the

5928.96 -> entire dashboard. So you can change

5930.46 -> colors, the placement you know different

5933.34 -> visual aspects of the entire dashboard.

5936.1 -> So come up with your own design and

5939.58 -> colors. Number three is providing some

5942.219 -> more insights. So, whatever we have

5943.84 -> covered in dashboard in addition to that

5946.179 -> try to add more insights and then once

5949.42 -> you have that dashboard created, you can

5951.58 -> write a nice LinkedIn post and tag me,

5954.639 -> Hemanand and add following particular

5957.52 -> hashtag so that you we know that you

5959.62 -> have submitted this. We are going to

5961.6 -> provide some sample LinkedIn Post in the

5963.88 -> description below. These are for resume

5965.44 -> projects. But basically you can write

5967.6 -> nice post upload your uh project on Novi

5971.32 -> Pro or maybe just short a simple video

5973.36 -> and make a LinkedIn post on it I wish

5976.9 -> you all the best and I hope you learned

5979.179 -> a lot of different things in this

5982 -> project. This is going to be an excellent

5984.76 -> project for your resume, and make sure

5987.699 -> you're writing LinkedIn post that way

5989.26 -> you know you can draw attention from

5991.36 -> potential recruiters. If you like this

5993.699 -> video give it a thumbs up and please

5995.38 -> share it with your friends. Any question

5997.6 -> or comments post in the comment box

6000 -> below or we have a Discord server for

6002.639 -> Codebasics go there and you can post

6004.44 -> your question there as well! Thank you!

6007.91 -> [Music]

Source: https://www.youtube.com/watch?v=4QkYy1wANXA