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Ahmad Varasteh

By the end of this project, you will learn to use data Exploration techniques in order to uncover some initial patterns, insights and interesting points in your dataset. We are going to use a dataset consisting 5 CSV files, consisting of the data related to players in FIFA video game. We will clean and prepare it by dropping useless columns, calculating new features for our dataset and filling up the null values properly. and then we will start our exploration and we'll do some visualizations.

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By the end of this project, you will learn to use data Exploration techniques in order to uncover some initial patterns, insights and interesting points in your dataset. We are going to use a dataset consisting 5 CSV files, consisting of the data related to players in FIFA video game. We will clean and prepare it by dropping useless columns, calculating new features for our dataset and filling up the null values properly. and then we will start our exploration and we'll do some visualizations.

Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

FIFA20 Data Exploration using Python
By the end of this project, you will learn to use data Exploration techniques in order to uncover some initial patterns, insights and interesting points in your dataset. We are going to use a dataset consisting 5 CSV files, consisting of the data related to players in FIFA video game. We will clean and prepare it by dropping useless columns, calculating new features for our dataset and filling up the null values properly. and then we will start our exploration and we'll do some visualizations.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Includes initial data exploration techniques
Uses real-world data from the FIFA video game
Provides opportunities for students to practice and apply their exploration and visualization skills
Taught by an instructor with expertise in data exploration
Builds on students' existing knowledge of data exploration and visualization
Requires students to have basic programming skills in Python

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Reviews summary

Fifa20 data exploration using python

Learners say this fun yet short course introduces Python and its capabilities, especially for those new to Python. Engaging assignments include building a virtual environment, completing course assignments using Ploty and Pandas, and exploring a FIFA dataset. However, some students felt that the difficult exams were rushed, and may be too challenging for beginners. Others found the course to be too short, and lacked depth.
Fun and engaging assignments for Python beginners.
"Great idea, but there is room for improvement in the implementation."
"I would recommend this course to someone who is familiar with python but is bellow an intermediate level.Very good content and both the professor and the project are very easy to follow along."
"Nicely guided project with some great visualization. This project definitely shows beginners like me on the capabilities of python and its uses."
Some learners felt the course was too short.
"Not bad at all. Fun and great way to get into using pandas. The second to last task was too rushed and a bit of a stretch though. Slow down at that point I suggest."
"But still, this a great guided project."
"it's a little bit short for my liking"
"in the end I kinda get lost on what the instructor is doing so I just had to follow along."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in FIFA20 Data Exploration using Python with these activities:
Review the PANDAS Basics
This activity will help you brush up on Python skills especially manipulating data with PANDAS
Browse courses on Pandas
Show steps
  • Go through the official PANDAS documentation
  • Review examples for various data manipulation techniques
Practice Data Cleaning using PANDAS
For this course, you will be working with a lot of datasets and it's crucial that they are clean
Browse courses on Data Cleaning
Show steps
  • Find a messy dataset online and try cleaning it with PANDAS functions
Create a data dictionary for the FIFA dataset
Creating a data dictionary is a great way to summarize key information about the dataset like variable names, data types and formats which will be useful in the long run.
Show steps
  • Go through each feature and figure out its data type and format
  • Document this information in a data dictionary
Three other activities
Expand to see all activities and additional details
Show all six activities
Gather Resources on Machine Learning Techniques for Sports Data Analysis
This course will touch on how machine learning can be used in sports analytics. Extend your knowledge by researching other applicable techniques.
Browse courses on Machine Learning
Show steps
  • Identify articles, tutorials, or books that focus on machine learning in sports analytics
  • Compile a list of relevant resources
Explore additional resources for Data Visualization in Python
This course provides a jumping off point, but there are many ways to represent data visually
Browse courses on Data Visualization
Show steps
  • Find some resources or tutorials on data visualization
  • Experiment with different visualization techniques
Contribute to an Open-Source Project in Data Science
Contributing to open source projects is a great way to enhance your skills and learn from others.
Browse courses on Open Source
Show steps
  • Find an open-source project that aligns with your interests
  • Identify ways you can contribute to the project
  • Submit your contributions to the project

Career center

Learners who complete FIFA20 Data Exploration using Python will develop knowledge and skills that may be useful to these careers:
Data Scientist
Professionals in this role, who gather and analyze data to help organizations make informed decisions, may be interested in the FIFA20 Data Exploration using Python course. This course could help these professionals develop a comprehensive understanding of data exploration techniques, enabling them to effectively analyze and extract meaningful insights from large and complex datasets. In particular, the hands-on experience in cleaning and preparing data, as well as calculating new features and filling null values, would be valuable for a Data Scientist seeking to excel in the field.
Data Analyst
This role, involving the analysis and interpretation of data to identify trends and patterns, could greatly benefit from the FIFA20 Data Exploration using Python course. Through this course, Data Analysts can gain a deep understanding of data exploration techniques, enabling them to effectively uncover insights and develop actionable recommendations based on data. The practical experience in data cleaning, preparation, and visualization would provide valuable hands-on skills for success in this field.
Machine Learning Engineer
Professionals in this role, who design and develop machine learning models to solve complex problems, can significantly benefit from the FIFA20 Data Exploration using Python course. The course offers valuable insights into data exploration techniques, enabling Machine Learning Engineers to effectively prepare and analyze data before building and evaluating machine learning models. Understanding how to identify patterns and trends in data is crucial for the success of Machine Learning Engineers, making this course a valuable addition to their skill set.
Statistician
Professionals in this role, who collect, analyze, and interpret data to inform decision-making, may find the FIFA20 Data Exploration using Python course highly relevant. The course provides a comprehensive introduction to data exploration techniques, enabling Statisticians to effectively analyze and extract meaningful insights from data. Particularly, the focus on data cleaning, preparation, and visualization would be valuable for Statisticians seeking to enhance their data analysis capabilities.
Business Analyst
This role, focusing on analyzing business data to identify opportunities for improvement and growth, may benefit from the FIFA20 Data Exploration using Python course. The course provides a solid foundation in data exploration techniques, allowing Business Analysts to effectively extract insights and make data-driven recommendations. The practical experience in data cleaning, preparation, and visualization would be particularly valuable for Business Analysts seeking to advance their data analysis skills.
Data Engineer
Professionals in this role, who design, build, and maintain data management systems, may find the FIFA20 Data Exploration using Python course useful for developing their data exploration skills. The course provides a practical foundation in data cleaning and preparation techniques, which are essential for Data Engineers to ensure data quality and integrity. Understanding how to identify patterns and trends in data can also assist Data Engineers in designing and optimizing data management systems.
Software Engineer
Software Engineers can benefit from the FIFA20 Data Exploration using Python course as it provides a solid foundation in data exploration techniques, enabling them to effectively analyze and utilize data in software development projects. The practical experience in data cleaning, preparation, and visualization, as well as the understanding of data patterns and trends, can be valuable for Software Engineers seeking to build robust and data-driven software applications.
Quantitative Analyst
Professionals in this role may find the FIFA20 Data Exploration using Python course useful for developing their data analysis and exploration skills. The course provides a foundation in data exploration techniques, data cleaning, and visualization, which are essential for Quantitative Analysts to effectively analyze and interpret financial data and make informed decisions.
Data Visualization Engineer
This role, involving the design and development of data visualizations to communicate insights effectively, could benefit from the FIFA20 Data Exploration using Python course. The course provides a solid foundation in data exploration techniques and data visualization best practices, enabling Data Visualization Engineers to create compelling and informative data visualizations. The practical experience in data cleaning, preparation, and visualization would be valuable for those seeking to excel in this field.
Database Administrator
Professionals in this role, who manage and maintain databases, may find the FIFA20 Data Exploration using Python course useful for developing their data analysis and exploration skills. The course provides a foundation in data exploration techniques, data cleaning, and visualization, which can be valuable for Database Administrators seeking to optimize database performance and ensure data quality.
Product Manager
Product Managers can benefit from the FIFA20 Data Exploration using Python course as it provides a solid foundation in data exploration techniques, enabling them to effectively analyze user data and make data-driven decisions. The practical experience in data cleaning, preparation, and visualization, as well as the understanding of data patterns and trends, can be valuable for Product Managers seeking to develop successful products that meet the needs of users.
Marketing Analyst
Professionals in this role, who analyze marketing data to measure the effectiveness of marketing campaigns, may find the FIFA20 Data Exploration using Python course useful for developing their data analysis skills. The course provides a foundation in data exploration techniques, data cleaning, and visualization, which can be valuable for Marketing Analysts seeking to gain insights from marketing data and optimize campaign performance.
Customer Success Manager
Customer Success Managers can benefit from the FIFA20 Data Exploration using Python course as it provides a foundation in data exploration techniques, enabling them to effectively analyze customer data and identify opportunities for improvement. The practical experience in data cleaning, preparation, and visualization, as well as the understanding of data patterns and trends, can be valuable for Customer Success Managers seeking to build strong customer relationships and drive customer success.
Market Researcher
Professionals in this role, who conduct research to gather and analyze market data, may find the FIFA20 Data Exploration using Python course useful for developing their data analysis skills. The course provides a foundation in data exploration techniques, data cleaning, and visualization, which can be valuable for Market Researchers seeking to gain insights from market data and make informed decisions.
Financial Analyst
Financial Analysts can benefit from the FIFA20 Data Exploration using Python course as it provides a foundation in data exploration techniques, enabling them to effectively analyze financial data and make informed decisions. The practical experience in data cleaning, preparation, and visualization, as well as the understanding of data patterns and trends, can be valuable for Financial Analysts seeking to develop robust financial models and make sound investment recommendations.

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in FIFA20 Data Exploration using Python.
Provides a biography of Pep Guardiola, one of the most successful football managers of all time.
Provides a global history of football, tracing the game's origins and spread to its current status as a global sport.
Features interviews with some of the biggest names in football, providing insights into their thoughts and motivations.
Tells the inspiring story of a small Italian football team that achieved remarkable success against all odds.
Challenges many of the conventional wisdoms about football, using data to show how the game is really played.
Provides a fascinating look at the economics of football, and how it can be used to explain the success or failure of different teams.

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