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Cricket Data Analysis | PySpark Foundation part 2

Cricket Data Analysis | PySpark Foundation part 2

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Instructors: Akash Sunil Pawar
1 section • 28 lectures • 56m total length
Video: MP4 1280x720 44 KHz | English + Sub
Updated 7/2022 | Size: 346 MB

Data Engineering, PySpark, Data Analysis, Coding exercise, Data Analytics

What you'll learn
Fundamentals of PySpark
Hands on experience in PySpark
Understanding of data using PySpark
Performing various data analysis operations
Data Analytics
Analysis of data

Requirements
There are no pre-requisites for the course. We will learn and practice together.Basic Python knowledge is a plusGood to have watched 1st part of this course

Description
Have you ever wondered How Big Data is helping Teams Win Big at the T20 World Cups/IPL?
In this course we will focus on Data analysis to get useful insights on IPL dataset with the help of PySpark.

Learn to code PySpark like a real world developer. Here our major focus will be on Practical applications of PySpark and bridge the gap between academic knowledge and practical skill.

About PySpark
Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python!
One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!
Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!

What you will learn
Introduction to Course
What is Data Analysis
Data analysis in Elections
Data Analysis in Cricket
Learning Outcomes
Insights we will get from data
Upload the data
Read the data
Understanding the data
Cleaning the data
Understanding data part 2
Total runs in an inning by a team
Highest runs scored by a Team
Lowest score by a team
Validation of results
Highest Run Scorers for RCB
Highest Run scorers batting first
Highest run scorers batting second
Creating buckets of overs
Bucketwise runs scored
Run rate in each phase
Best batters in powerplay
Best batters in Death
Understanding Bowlers data
Most wickets against RCB by a bowler
Most wickets in powerplay
Best bowler in Death
Recap and Summary

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Cricket Data Analysis | PySpark Foundation part 2

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