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Ryan Ahmed
In this hands-on guided project, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the first of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.
Register for this course and see more details by visiting:
OpenCourser.com/course/bu4ame/master
What's inside
Syllabus
Project Overview
In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the first of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Ryan Ahmed, who is an expert in data analysis with pandas
Explores pandas, which is a powerful and widely used open-source data analysis tool
Serves as an introductory course to a larger learning path on data analysis with pandas
Provides hands-on practical experience through guided projects
Assumes no prior knowledge, making it suitable for beginners
Save this course
Save Master Data Analysis with Pandas: Learning Path 1 (Enhanced) 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 Master Data Analysis with Pandas: Learning Path 1 (Enhanced) with these
activities:
Review your notes and assignments from previous data analysis courses
Show steps
Reviewing your notes and assignments from previous data analysis courses will help you refresh your memory and build upon your existing knowledge.
Browse courses on
Data Analysis
Show steps
-
Gather your notes and assignments from previous data analysis courses.
-
Review the materials and identify areas where you need to refresh your knowledge.
-
Focus on reviewing the concepts and techniques that are most relevant to this course.
Organize your course materials into a structured format
Show steps
Organizing your course materials will help you easily find the information you need and stay on top of your studies.
Show steps
-
Gather all of your course materials, including notes, assignments, and readings.
-
Create a system for organizing your materials, such as using folders or a digital notebook.
-
Review your materials regularly and update your organization system as needed.
Create a cheat sheet of essential Pandas functions
Show steps
Creating a cheat sheet of essential Pandas functions will help you quickly reference the most common functions and their usage.
Browse courses on
Pandas
Show steps
-
Gather a list of essential Pandas functions.
-
Create a document or spreadsheet that includes the function name, syntax, and a brief description.
-
Review the cheat sheet regularly.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read 'Python for Data Analysis'
Show steps
Familiarizing yourself with the fundamentals of Python for data analysis will help you better understand the concepts covered in this course.
View
Python for Data Analysis
on Amazon
Show steps
-
Purchase the book or borrow it from a library.
-
Read the first three chapters.
-
Complete the exercises at the end of each chapter.
Join a study group or online forum for Pandas users
Show steps
Joining a study group or online forum will allow you to connect with other Pandas users and learn from their experiences.
Browse courses on
Pandas
Show steps
-
Find a study group or online forum that is relevant to your interests.
-
Introduce yourself and ask questions.
-
Participate in discussions and share your knowledge.
Solve practice problems on Pandas data manipulation
Show steps
Solving practice problems will help you solidify your understanding of the Pandas library and improve your data manipulation skills.
Browse courses on
Pandas
Show steps
-
Find a website or book with practice problems.
-
Solve at least 10 problems.
-
Review your solutions and identify areas where you need improvement.
Follow a tutorial on advanced Pandas data analysis techniques
Show steps
Following a tutorial on advanced Pandas techniques will help you expand your knowledge and learn how to handle more complex data analysis tasks.
Browse courses on
Pandas
Show steps
-
Find a tutorial on a specific data analysis technique that you want to learn.
-
Follow the tutorial step-by-step.
-
Apply the technique to a real-world dataset.
Build a portfolio of data analysis projects using Pandas
Show steps
Building a portfolio of data analysis projects will showcase your skills and help you stand out in the job market.
Browse courses on
Pandas
Show steps
-
Identify a few data analysis problems that you can solve using Pandas.
-
Gather the necessary data.
-
Clean and prepare the data.
-
Analyze the data using Pandas.
-
Create visualizations to present your findings.
Contribute to an open-source Pandas project
Show steps
Contributing to an open-source Pandas project will help you learn how to work with others and contribute to the Pandas community.
Browse courses on
Pandas
Show steps
-
Find an open-source Pandas project that you are interested in.
-
Read the project documentation and familiarize yourself with the codebase.
-
Identify a small issue or feature that you can work on.
-
Create a pull request and submit your changes.
-
Work with the project maintainers to get your changes merged.
Review your notes and assignments from previous data analysis courses
Show steps
Reviewing your notes and assignments from previous data analysis courses will help you refresh your memory and build upon your existing knowledge.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Gather your notes and assignments from previous data analysis courses.
- Review the materials and identify areas where you need to refresh your knowledge.
- Focus on reviewing the concepts and techniques that are most relevant to this course.
Organize your course materials into a structured format
Show steps
Organizing your course materials will help you easily find the information you need and stay on top of your studies.
Show steps
Show steps
Show steps
- Gather all of your course materials, including notes, assignments, and readings.
- Create a system for organizing your materials, such as using folders or a digital notebook.
- Review your materials regularly and update your organization system as needed.
Create a cheat sheet of essential Pandas functions
Show steps
Creating a cheat sheet of essential Pandas functions will help you quickly reference the most common functions and their usage.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Gather a list of essential Pandas functions.
- Create a document or spreadsheet that includes the function name, syntax, and a brief description.
- Review the cheat sheet regularly.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Read 'Python for Data Analysis'
Show steps
Familiarizing yourself with the fundamentals of Python for data analysis will help you better understand the concepts covered in this course.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Purchase the book or borrow it from a library.
- Read the first three chapters.
- Complete the exercises at the end of each chapter.
Join a study group or online forum for Pandas users
Show steps
Joining a study group or online forum will allow you to connect with other Pandas users and learn from their experiences.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find a study group or online forum that is relevant to your interests.
- Introduce yourself and ask questions.
- Participate in discussions and share your knowledge.
Solve practice problems on Pandas data manipulation
Show steps
Solving practice problems will help you solidify your understanding of the Pandas library and improve your data manipulation skills.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find a website or book with practice problems.
- Solve at least 10 problems.
- Review your solutions and identify areas where you need improvement.
Follow a tutorial on advanced Pandas data analysis techniques
Show steps
Following a tutorial on advanced Pandas techniques will help you expand your knowledge and learn how to handle more complex data analysis tasks.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find a tutorial on a specific data analysis technique that you want to learn.
- Follow the tutorial step-by-step.
- Apply the technique to a real-world dataset.
Build a portfolio of data analysis projects using Pandas
Show steps
Building a portfolio of data analysis projects will showcase your skills and help you stand out in the job market.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Identify a few data analysis problems that you can solve using Pandas.
- Gather the necessary data.
- Clean and prepare the data.
- Analyze the data using Pandas.
- Create visualizations to present your findings.
Contribute to an open-source Pandas project
Show steps
Contributing to an open-source Pandas project will help you learn how to work with others and contribute to the Pandas community.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Find an open-source Pandas project that you are interested in.
- Read the project documentation and familiarize yourself with the codebase.
- Identify a small issue or feature that you can work on.
- Create a pull request and submit your changes.
- Work with the project maintainers to get your changes merged.
Career center
Learners who complete Master Data Analysis with Pandas: Learning Path 1 (Enhanced) will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst processes and interprets data and uses it to solve business problems. Data Analysts may work on projects such as determining the best pricing strategy or identifying trends in customer behavior. This course helps build a foundation for a career as a Data Analyst by teaching the fundamentals of data analysis and manipulation with Pandas and Python.
Business Analyst
Business Analyst
A Business Analyst helps develop methods, processes, and techniques for capturing, analyzing, and managing requirements. Business Analysts may work on projects such as developing a new product or improving a customer service process. This course may be useful for Business Analysts by helping them learn how to use data to improve business outcomes.
Data Scientist
Data Scientist
A Data Scientist uses data to extract knowledge and insights. Data Scientists may work on projects such as developing new AI algorithms or forecasting future trends. This course may be useful for Data Scientists by helping them learn how to use Pandas and Python to manipulate and analyze data.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. Machine Learning Engineers may work on projects such as developing a new image recognition system or predicting customer churn. This course may be useful for Machine Learning Engineers by helping them learn how to use Pandas and Python to prepare and analyze data for machine learning models.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. Software Engineers may work on projects such as developing a new website or improving the performance of an existing software system. This course may be useful for Software Engineers by helping them learn how to use Pandas and Python to analyze and manipulate data in software applications.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of a product. Product Managers may work on projects such as launching a new product or improving an existing product. This course may be useful for Product Managers by helping them learn how to use Pandas and Python to analyze data and make informed decisions about product development.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns. Marketing Managers may work on projects such as increasing brand awareness or generating leads. This course may be useful for Marketing Managers by helping them learn how to use Pandas and Python to analyze data and measure the success of marketing campaigns.
Financial Analyst
Financial Analyst
A Financial Analyst provides guidance on financial matters. Financial Analysts may work on projects such as evaluating investment opportunities or developing financial models. This course may be useful for Financial Analysts by helping them learn how to use Pandas and Python to analyze financial data.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve business problems. Operations Research Analysts may work on projects such as optimizing supply chain management or improving customer service. This course may be useful for Operations Research Analysts by helping them learn how to use Pandas and Python to analyze and manipulate data.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. Statisticians may work on projects such as conducting clinical trials or developing statistical models. This course may be useful for Statisticians by helping them learn how to use Pandas and Python to analyze and manipulate data.
Actuary
Actuary
An Actuary uses mathematical and statistical techniques to assess risk. Actuaries may work on projects such as pricing insurance policies or developing pension plans. This course may be useful for Actuaries by helping them learn how to use Pandas and Python to analyze and manipulate data.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the distribution and patterns of health and disease. Epidemiologists may work on projects such as studying the causes of cancer or developing public health policies. This course may be useful for Epidemiologists by helping them learn how to use Pandas and Python to analyze and interpret health data.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to solve problems in biology and medicine. Biostatisticians may work on projects such as designing clinical trials or developing new statistical methods. This course may be useful for Biostatisticians by helping them learn how to use Pandas and Python to analyze and interpret biological and medical data.
Database Administrator
Database Administrator
A Database Administrator is responsible for the maintenance and performance of a database system. Database Administrators may work on projects such as optimizing database performance or recovering data from a database failure. This course may be useful for Database Administrators by helping them learn how to use Pandas and Python to analyze and manipulate data in a database.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. Data Engineers may work on projects such as developing a new data warehouse or improving the performance of an existing data pipeline. This course may be useful for Data Engineers by helping them learn how to use Pandas and Python to manipulate and analyze data.
For more career information including salaries, visit:
OpenCourser.com/course/bu4ame/master
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
Master Data Analysis with Pandas: Learning Path 1 (Enhanced).
As the creator of Pandas, Wes McKinney provides an in-depth exploration of Python's capabilities for data analysis. is an excellent resource for learners interested in gaining a deeper understanding of Python's data analysis capabilities.
Save
Offers a comprehensive guide to using Pandas for data analysis. It covers essential concepts, techniques, and best practices, making it a suitable resource for learners seeking to master Pandas.
Offers a comprehensive introduction to data science and its underlying principles. It covers essential concepts, techniques, and tools, making it a valuable resource for learners seeking to gain a solid foundation in data science.
Offers a practical guide to using Python for data science. It covers essential techniques and industry-standard tools, making it a valuable resource for learners seeking to apply Python in real-world data science projects.
This handbook provides a comprehensive overview of Python's data science capabilities. It covers essential concepts, tools, and techniques, serving as a valuable resource for learners seeking to expand their knowledge of Python for data science.
Provides a detailed overview of how to combine Pandas and Python for data analysis. It suitable resource for learners seeking to expand their knowledge of Pandas and its applications.
Offers a deep dive into advanced analytics techniques using Python. It covers topics such as statistical modeling, machine learning, and natural language processing, making it a valuable resource for learners seeking to expand their knowledge of advanced data analysis techniques.
Offers a comprehensive guide to using Python for machine learning. It covers essential concepts, techniques, and algorithms, making it a valuable resource for learners seeking to apply machine learning in various domains.
Offers a hands-on introduction to machine learning using Python. It covers essential concepts, techniques, and algorithms, making it a suitable resource for learners seeking to understand the fundamentals of machine learning.
Save
While this book focuses on R rather than Python, it provides a comprehensive introduction to data science concepts and techniques. It covers essential topics such as data manipulation, visualization, and statistical modeling, making it a valuable resource for learners seeking to gain a broader understanding of data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bu4ame/master
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Taught by Ryan Ahmed, who is an expert in data analysis with pandas
Explores pandas, which is a powerful and widely used open-source data analysis tool
Serves as an introductory course to a larger learning path on data analysis with pandas
Provides hands-on practical experience through guided projects
Assumes no prior knowledge, making it suitable for beginners
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