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Ali Amr Souidan
In this 1-hour long project-based course, you will learn how to load a dataset into a pandas dataframe, you will learn how to tidy a messy dataset (Data Tidying), you will get to also visualize the dataset using Matplotlib and seaborn, you will learn how to engineer new features, you will also get to learn how to merge datasets (Data Integration)
By the end of this project, you will be able to fully analyze a FIFA dataset using python’s Pandas library. Throughout the tasks you will be able to identify and apply the key aspects about data analysis such as Data Cleaning, Data transformation, Data Visualization , Data tidying and...
Read more
In this 1-hour long project-based course, you will learn how to load a dataset into a pandas dataframe, you will learn how to tidy a messy dataset (Data Tidying), you will get to also visualize the dataset using Matplotlib and seaborn, you will learn how to engineer new features, you will also get to learn how to merge datasets (Data Integration)
By the end of this project, you will be able to fully analyze a FIFA dataset using python’s Pandas library. Throughout the tasks you will be able to identify and apply the key aspects about data analysis such as Data Cleaning, Data transformation, Data Visualization , Data tidying and...
Read more
In this 1-hour long project-based course, you will learn how to load a dataset into a pandas dataframe, you will learn how to tidy a messy dataset (Data Tidying), you will get to also visualize the dataset using Matplotlib and seaborn, you will learn how to engineer new features, you will also get to learn how to merge datasets (Data Integration)
By the end of this project, you will be able to fully analyze a FIFA dataset using python’s Pandas library. Throughout the tasks you will be able to identify and apply the key aspects about data analysis such as Data Cleaning, Data transformation, Data Visualization , Data tidying and feature engineering.
All these skills are crucial to the world of data engineering and are very beneficial in today’s job market that is leaning more and more towards utilizing data in every way
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/6pa05w/exploring
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Know what's good ,
what to watch for , and
possible dealbreakers
This course is perfect for those eager to learn fundamental data analysis skills using Python's Pandas library
This project-based course provides hands-on experience in data cleaning, tidying, visualization, and feature engineering
Fully analyze FIFA dataset using Python Pandas, and apply key aspects of data analysis
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Save Exploring and Analyzing Fifa's Datasets Using Python to your list so you can find it easily later:
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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 Exploring and Analyzing Fifa's Datasets Using Python with these
activities:
Watch the guided tutorials in the course
Show steps
Watching the guided tutorials will help you to learn the basics of Pandas and data analysis.
Browse courses on
Pandas
Show steps
-
Watch the guided tutorials in each module
Review the book Pandas for Data Analysis
Show steps
Reviewing this book beforehand will provide you with a solid foundation in using Pandas for data analysis.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read the first three chapters of the book
-
Complete the first three Jupyter notebooks in the book's GitHub repository
Complete the practice drills in the course
Show steps
Completing the practice drills will help you to practice the skills and knowledge you learn in the course.
Browse courses on
Pandas
Show steps
-
Complete the practice drills in each module
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in the peer discussion forums
Show steps
Participating in the peer discussion forums will allow you to get help from other students and to share your own knowledge.
Browse courses on
Pandas
Show steps
-
Post questions in the discussion forums
-
Answer questions from other students
Create a data analysis project using Pandas
Show steps
Working on a project will allow you to apply the skills and knowledge you learn in the course to a real-world scenario.
Browse courses on
Python
Show steps
-
Choose a dataset to analyze
-
Load the dataset into a Pandas dataframe
-
Clean and prepare the data
-
Analyze the data and draw conclusions
-
Visualize the data
Create a data visualization using Pandas and Matplotlib
Show steps
Creating a data visualization will help you to visualize and understand the data you are analyzing.
Browse courses on
Pandas
Show steps
-
Choose a dataset to visualize
-
Load the dataset into a Pandas dataframe
-
Create a data visualization using Matplotlib
-
Save your data visualization
Write a blog post about using Pandas for data analysis
Show steps
Writing a blog post will help you to solidify your understanding of Pandas and data analysis.
Browse courses on
Pandas
Show steps
-
Choose a topic for your blog post
-
Research your topic
-
Write your blog post
-
Publish your blog post
Participate in data analysis competitions
Show steps
Participating in data analysis competitions will allow you to test your skills and to learn from other data analysts.
Browse courses on
Pandas
Show steps
-
Find a data analysis competition to participate in
-
Build a model to solve the competition problem
-
Submit your model to the competition
Watch the guided tutorials in the course
Show steps
Watching the guided tutorials will help you to learn the basics of Pandas and data analysis.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Watch the guided tutorials in each module
Review the book Pandas for Data Analysis
Show steps
Reviewing this book beforehand will provide you with a solid foundation in using Pandas for data analysis.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book
- Complete the first three Jupyter notebooks in the book's GitHub repository
Complete the practice drills in the course
Show steps
Completing the practice drills will help you to practice the skills and knowledge you learn in the course.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Complete the practice drills in each module
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in the peer discussion forums
Show steps
Participating in the peer discussion forums will allow you to get help from other students and to share your own knowledge.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Post questions in the discussion forums
- Answer questions from other students
Create a data analysis project using Pandas
Show steps
Working on a project will allow you to apply the skills and knowledge you learn in the course to a real-world scenario.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a dataset to analyze
- Load the dataset into a Pandas dataframe
- Clean and prepare the data
- Analyze the data and draw conclusions
- Visualize the data
Create a data visualization using Pandas and Matplotlib
Show steps
Creating a data visualization will help you to visualize and understand the data you are analyzing.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Choose a dataset to visualize
- Load the dataset into a Pandas dataframe
- Create a data visualization using Matplotlib
- Save your data visualization
Write a blog post about using Pandas for data analysis
Show steps
Writing a blog post will help you to solidify your understanding of Pandas and data analysis.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Choose a topic for your blog post
- Research your topic
- Write your blog post
- Publish your blog post
Participate in data analysis competitions
Show steps
Participating in data analysis competitions will allow you to test your skills and to learn from other data analysts.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find a data analysis competition to participate in
- Build a model to solve the competition problem
- Submit your model to the competition
Career center
Learners who complete Exploring and Analyzing Fifa's Datasets Using Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you use data to solve business problems, track key performance indicators, and analyze trends and patterns that help organizations optimize marketing campaigns, improve operations, and reach new customers. This course provides a foundation in data analysis, helping you to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can help you build the skills necessary to succeed as a Data Analyst as this role requires the ability to gather, analyze, and interpret data to provide insights to stakeholders.
Data Engineer
Data Engineer
Data Engineers design and build scalable, reliable, and efficient data systems and infrastructure. They ensure that data is available, accessible, and performant for data analysts and scientists to use in their work. This course provides a foundation in data engineering, helping you to learn how to load data into a Pandas dataframe, transform data using Python code, and merge datasets to create new insights. This course can be especially helpful for those interested in a career as a Data Engineer, as it provides important skills and knowledge in the field
Data Scientist
Data Scientist
Data Scientists use data to build models that can predict future outcomes, identify patterns, and help organizations make better decisions. This course provides a foundation in data science, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can help you build the skills necessary to succeed as a Data Scientist as it provides the foundational skills required to work with and analyze data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models that can automate tasks, improve decision-making, and provide personalized experiences. This course provides a foundation in machine learning, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can help you build the skills necessary to succeed as a Machine Learning Engineer as it provides experience in working with and preparing data for machine learning models.
Business Analyst
Business Analyst
Business Analysts use data to understand how businesses operate, identify opportunities for improvement, and develop strategies for growth. This course provides a foundation in business analysis, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can be helpful for those interested in a career as a Business Analyst, as it provides the skills necessary to gather, analyze, and interpret data to make recommendations for business improvement.
Statistician
Statistician
Statisticians use data to solve problems, make predictions, and draw conclusions. This course provides a foundation in statistics, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can be helpful for those interested in a career as a Statistician, as it provides the skills necessary to work with and analyze data to make informed decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to build financial models, assess risk, and make investment decisions. This course provides a foundation in quantitative analysis, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can be helpful for those interested in a career as a Quantitative Analyst, as it provides the skills necessary to analyze data to make sound investment decisions.
Data Visualization Engineer
Data Visualization Engineer
Data Visualization Engineers design and build interactive data visualizations that help users to understand data and make better decisions. This course provides a foundation in data visualization, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course can be especially helpful for those interested in a career as a Data Visualization Engineer, as it provides the skills necessary to create visually appealing and informative data visualizations.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases to ensure that data is stored, organized, and accessed efficiently. This course provides a foundation in database administration, helping you to learn how to load data into a database, create and modify tables, and optimize database performance. This course can be helpful for those interested in a career as a Database Administrator.
Information Analyst
Information Analyst
Information Analysts gather, analyze, and interpret data to provide insights to stakeholders. This course can provide a foundation in data analysis, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course may be helpful for those interested in a career as an Information Analyst, as it provides some of the necessary skills for working with and analyzing data.
Data Architect
Data Architect
Data Architects design and implement data management solutions that meet the needs of an organization. This course provides a foundation in data architecture, helping you to learn how to design and implement data models, integrate data from multiple sources, and ensure data quality. This course may be helpful for those interested in a career as a Data Architect, as it provides some of the necessary knowledge for designing and implementing data management solutions.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends, patterns, and opportunities that can help organizations make better decisions. This course can provide a foundation in business intelligence, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course may be helpful for those interested in a career as a Business Intelligence Analyst, as it provides some of the necessary skills for working with and analyzing data.
Financial Analyst
Financial Analyst
Financial Analysts use data to evaluate financial performance, make investment recommendations, and assess risk. This course can provide a foundation in financial analysis, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course may be helpful for those interested in a career as a Financial Analyst, as it provides some of the necessary skills for working with and analyzing financial data.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand consumer behavior, market trends, and competitive landscapes. This course can provide a foundation in market research, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course may be helpful for those interested in a career as a Market Researcher, as it provides some of the necessary skills for working with and analyzing data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can provide a foundation in software engineering, helping you to learn how to clean, visualize, and merge datasets using Python libraries like Pandas, Matplotlib, and Seaborn. This course may be helpful for those interested in a career as a Software Engineer, as it provides some of the necessary skills for working with and analyzing data in software development.
For more career information including salaries, visit:
OpenCourser.com/course/6pa05w/exploring
Reading list
We've selected 12 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
Exploring and Analyzing Fifa's Datasets Using Python.
Provides a comprehensive overview of the pandas library, which is used for data cleaning, transformation, and visualization in Python. It valuable resource for understanding the core concepts and techniques of data analysis using pandas.
Covers the fundamentals of data analysis using Python, including data cleaning, wrangling, visualization, and statistical modeling. It great foundation for learning the skills necessary for this course.
Introduces Matplotlib and Seaborn, two popular Python libraries for data visualization. It covers the basics of creating various types of plots and charts, which is essential for this course.
Explains the concepts and techniques of feature engineering, which is the process of transforming raw data into features that are more suitable for machine learning models. It valuable resource for understanding the importance of feature engineering in this course.
Offers a comprehensive overview of data science using Python. It covers a wide range of topics, including data analysis, machine learning, and deep learning. While it is not specifically focused on FIFA data, it provides valuable background knowledge for this course.
Provides a practical guide to data analysis using Pandas. It covers data cleaning, manipulation, and visualization, which are core skills for this course.
Save
Introduces the basics of data analysis using R, a statistical programming language. While it does not cover Python, it provides valuable insights into data analysis concepts and techniques that are applicable to this course.
Provides a business-oriented perspective on data science. It covers topics such as data management, analysis, and visualization, which are relevant to this course.
Introduces the fundamentals of machine learning using Python. While it does not focus specifically on data analysis, it provides valuable insights into machine learning techniques that can be applied to this course.
Introduces the concepts and techniques of deep learning using Python. While it is not directly relevant to this course, it provides valuable background knowledge for those interested in exploring deep learning applications.
Provides a hands-on introduction to data science using Python. It covers a wide range of topics, including data cleaning, analysis, and visualization, which are valuable skills for this course.
Save
Provides a comprehensive overview of Python for data science. It covers topics such as data analysis, machine learning, and deep learning, which are valuable for this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6pa05w/exploring
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Time
15 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ali Amr Souidan
Good to know
This course is perfect for those eager to learn fundamental data analysis skills using Python's Pandas library
This project-based course provides hands-on experience in data cleaning, tidying, visualization, and feature engineering
Fully analyze FIFA dataset using Python Pandas, and apply key aspects of data analysis
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