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Kevin Noelsaint
and
Anh Le
Code and run your first Python script in minutes without installing anything!
This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages. In this course, you will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
To allow for a truly hands-on, self-paced learning experience, this course is video-free.
Read more
Code and run your first Python script in minutes without installing anything!
This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages. In this course, you will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
To allow for a truly hands-on, self-paced learning experience, this course is video-free.
Read more
Code and run your first Python script in minutes without installing anything!
This course is designed for learners with no coding experience, providing a crash course in Python, which enables the learners to delve into core data analysis topics that can be transferred to other languages. In this course, you will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
To allow for a truly hands-on, self-paced learning experience, this course is video-free.
Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a longer-form lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.
Register for this course and see more details by visiting:
OpenCourser.com/course/4t9vfc/data
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What's inside
Syllabus
Describing a Numerical Data Set
Importing and Describing Mixed Data Sets (pandas)
Statistical Tests to Determine if Populations are Different
Read more
Syllabus
Describing a Numerical Data Set
Importing and Describing Mixed Data Sets (pandas)
Statistical Tests to Determine if Populations are Different
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops a solid foundation in Python, which can enhance data analysis skills and is also applicable to other programming languages
Provides a practical, hands-on learning experience through runnable code examples and instant feedback
Suitable for beginners, with detailed explanations and approachable coding exercises
Emphasizes the importance of data organization and understanding, which is crucial for effective data analysis
Facilitates a deeper understanding by encouraging learners to explore code examples through suggested edits
Provides a capstone lab to apply learned concepts in a real-world context, consolidating theoretical knowledge and practical skills
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Reviews summary
Hands-on python data analysis for beginners
According to students, this course is an excellent starting point for data analysis in Python, particularly for absolute beginners with no prior coding experience. Learners consistently praise its hands-on approach, featuring runnable code examples and interactive exercises that provide instant feedback, making learning highly effective. The self-paced, video-free format is appreciated by many for its flexibility, though a few note it can feel dry for some complex topics. While it provides a solid foundation in pandas and matplotlib, some suggest that the statistical tests section could benefit from more depth for advanced understanding. Overall, it's widely regarded as a practical and effective course for career upskilling.
A self-paced, text-based course without video lectures.
"I loved the no-video format, allowing me to learn at my own pace."
"The content is decent for beginners, but I found the 'no video' approach a bit dry at times. ... sometimes a visual explanation would have been better for complex topics."
"While it says for no coding experience, I struggled a bit without video lectures. Some explanations felt rushed..."
Setting up the Spyder environment is straightforward.
"The Spyder environment setup was straightforward."
"The environment setup was painless."
Utilizes effective interactive exercises and runnable code.
"The hands-on examples and interactive exercises truly solidify the concepts."
"The runnable code examples within the lessons made learning by doing incredibly effective. Instant feedback on exercises was also a huge plus."
"I liked the practical focus on pandas and matplotlib. The course structure is logical, and the exercises really help reinforce learning."
Exceptionally good for those new to coding and data analysis.
"This course is fantastic for absolute beginners! ... Highly recommend for anyone starting with Python data analysis."
"As someone with zero prior coding experience, this course was a lifesaver. It starts with the very basics of Python, which I appreciated."
"Perfect for beginners! The structure, explanations, and exercises are all top-notch. I learned so much about pandas and matplotlib quickly."
Could offer more comprehensive coverage on advanced statistical concepts.
"I found the statistical tests section a bit brief, could use more in-depth examples."
"While it says for no coding experience, I struggled a bit without video lectures. Some explanations felt rushed, especially in the statistical section."
"Okay course. It's good that it's hands-on, but some sections felt a bit shallow. Needed more depth on statistical interpretation."
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 Data Analysis in Python with pandas & matplotlib in Spyder with these
activities:
Read a book about Python data analysis
Show steps
Reading a book about Python data analysis will provide you with a comprehensive overview of the subject and help you build a strong foundation.
View
Python for Data Analysis
on Amazon
Show steps
-
Find a book that is appropriate for your level of experience.
-
Read the book carefully and take notes.
-
Complete the exercises and projects in the book.
Gather resources and tools
Show steps
Gathering resources and tools will help you become more confident with the core concepts of the course.
Browse courses on
Python
Show steps
-
Find and bookmark relevant articles, tutorials, and videos.
-
Create a list of useful Python libraries and packages.
-
Organize your resources into a folder or notebook.
Follow Python tutorials
Show steps
Following Python tutorials will help you learn the basics of the language and how to apply it to data analysis.
Browse courses on
Python
Show steps
-
Find tutorials on topics such as data manipulation, visualization, and statistical analysis.
-
Follow the tutorials step-by-step and complete the exercises.
-
Experiment with the code and try to apply it to your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Python coding exercises
Show steps
Solving Python coding exercises will help you improve your understanding of the syntax and structure of the language.
Browse courses on
Python
Show steps
-
Find online coding challenges or exercises.
-
Attempt to solve the exercises on your own.
-
Review the solutions and compare them to your own.
Analyze real-world datasets
Show steps
Analyzing real-world datasets will help you develop your data analysis skills and learn how to apply Python to solve real-world problems.
Browse courses on
Python
Show steps
-
Find a publicly available dataset that interests you.
-
Load the dataset into Python and explore it using data analysis techniques.
-
Perform statistical tests to draw insights from the data.
Write a blog post or article about Python data analysis
Show steps
Writing a blog post or article about Python data analysis will help you solidify your understanding of the subject and share your knowledge with others.
Browse courses on
Python
Show steps
-
Choose a topic that you are passionate about and that you have some experience in.
-
Research the topic and gather information from reliable sources.
-
Write a well-structured and engaging blog post or article.
Contribute to an open-source Python project
Show steps
Contributing to an open-source Python project will help you learn how to collaborate with others and contribute to the Python community.
Browse courses on
Python
Show steps
-
Find an open-source Python project that interests you.
-
Contact the project maintainers and ask how you can contribute.
-
Make a pull request to the project with your contributions.
Build a Python project
Show steps
Building a Python project will help you apply your skills to a real-world problem and showcase your abilities to potential employers.
Browse courses on
Python
Show steps
-
Identify a problem that you would like to solve using Python.
-
Design and develop a Python solution to the problem.
-
Deploy your project and share it with others.
Read a book about Python data analysis
Show steps
Reading a book about Python data analysis will provide you with a comprehensive overview of the subject and help you build a strong foundation.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Find a book that is appropriate for your level of experience.
- Read the book carefully and take notes.
- Complete the exercises and projects in the book.
Gather resources and tools
Show steps
Gathering resources and tools will help you become more confident with the core concepts of the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find and bookmark relevant articles, tutorials, and videos.
- Create a list of useful Python libraries and packages.
- Organize your resources into a folder or notebook.
Follow Python tutorials
Show steps
Following Python tutorials will help you learn the basics of the language and how to apply it to data analysis.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find tutorials on topics such as data manipulation, visualization, and statistical analysis.
- Follow the tutorials step-by-step and complete the exercises.
- Experiment with the code and try to apply it to your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Python coding exercises
Show steps
Solving Python coding exercises will help you improve your understanding of the syntax and structure of the language.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online coding challenges or exercises.
- Attempt to solve the exercises on your own.
- Review the solutions and compare them to your own.
Analyze real-world datasets
Show steps
Analyzing real-world datasets will help you develop your data analysis skills and learn how to apply Python to solve real-world problems.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a publicly available dataset that interests you.
- Load the dataset into Python and explore it using data analysis techniques.
- Perform statistical tests to draw insights from the data.
Write a blog post or article about Python data analysis
Show steps
Writing a blog post or article about Python data analysis will help you solidify your understanding of the subject and share your knowledge with others.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a topic that you are passionate about and that you have some experience in.
- Research the topic and gather information from reliable sources.
- Write a well-structured and engaging blog post or article.
Contribute to an open-source Python project
Show steps
Contributing to an open-source Python project will help you learn how to collaborate with others and contribute to the Python community.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find an open-source Python project that interests you.
- Contact the project maintainers and ask how you can contribute.
- Make a pull request to the project with your contributions.
Build a Python project
Show steps
Building a Python project will help you apply your skills to a real-world problem and showcase your abilities to potential employers.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify a problem that you would like to solve using Python.
- Design and develop a Python solution to the problem.
- Deploy your project and share it with others.
Career center
Learners who complete Data Analysis in Python with pandas & matplotlib in Spyder will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists delve into data to extract meaningful insights by applying machine learning algorithms and statistical models. This course provides a foundation in data analysis with Python, which is a key skill for Data Scientists. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Statistician
Statistician
Statisticians apply mathematical and statistical techniques to collect, analyze, interpret, and present data. This course provides a foundation in data analysis with Python, which is a key skill for Statisticians. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Market Researcher
Market Researcher
Market Researchers study market conditions, trends, and consumer behavior. This course provides a foundation in data analysis with Python, which is a key skill for Market Researchers. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Data Analyst
Data Analyst
A Data Analyst may work with large data sets to gain actionable insights that can be used to make informed decisions. This course provides a hands-on introduction to data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests. This course may also provide a foundation for roles such as a Data Scientist, Statistician, or Market Researcher.
Business Analyst
Business Analyst
Business Analysts use data to analyze and improve business processes. This course provides a foundation in data analysis with Python, which is a key skill for Business Analysts. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze and make investment recommendations. This course provides a foundation in data analysis with Python, which is a key skill for Financial Analysts. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to analyze and improve business processes. This course provides a foundation in data analysis with Python, which is a key skill for Operations Research Analysts. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Risk Analyst
Risk Analyst
This course may be helpful for Risk Analysts who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Quantitative Analyst
Quantitative Analyst
This course may be helpful for Quantitative Analysts who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Actuary
Actuary
This course may be helpful for Actuaries who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Machine Learning Engineer
Machine Learning Engineer
This course may be helpful for Machine Learning Engineers who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Data Engineer
Data Engineer
This course may be helpful for Data Engineers who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Database Administrator
Database Administrator
This course may be helpful for Database Administrators who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Web Developer
Web Developer
This course may be helpful for Web Developers who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
Software Engineer
Software Engineer
This course may be helpful for Software Engineers who are interested in learning data analysis with Python. You will learn how to import and organize your data, use functions to gather descriptive statistics, and perform statistical tests.
For more career information including salaries, visit:
OpenCourser.com/course/4t9vfc/data
Reading list
We've selected eight 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
Data Analysis in Python with pandas & matplotlib in Spyder.
Provides a comprehensive overview of data analysis in Python, covering topics such as data wrangling, exploratory data analysis, machine learning, and data visualization. It valuable resource for both beginners and experienced data analysts.
Save
Provides a comprehensive overview of data mining in R. It covers topics such as data wrangling, exploratory data analysis, machine learning, and data visualization. It good choice for beginners who want to learn the fundamentals of data mining in R.
Provides a comprehensive overview of machine learning in R. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for beginners who want to learn the fundamentals of machine learning in R.
Provides a comprehensive overview of data analysis in Python and Jupyter. It covers topics such as data wrangling, exploratory data analysis, machine learning, and data visualization. It good choice for beginners who want to learn the fundamentals of data analysis in Python and Jupyter.
Save
Provides a comprehensive overview of data analysis in Python. It covers topics such as data wrangling, exploratory data analysis, machine learning, and data visualization. It good choice for beginners who want to learn the fundamentals of data analysis.
Save
Provides a comprehensive overview of data analysis in R. It covers topics such as data wrangling, exploratory data analysis, machine learning, and data visualization. It good choice for beginners who want to learn the fundamentals of data analysis in R.
Provides a comprehensive overview of data science, covering topics such as data collection, data cleaning, data analysis, and machine learning. It good choice for beginners who want to learn the fundamentals of data science.
Provides a comprehensive overview of machine learning in Python. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for beginners who want to learn the fundamentals of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4t9vfc/data
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Effort
4 weeks of study, 2-3 hours/week
Level
Introductory
Via
Coursera
Institution
Codio
Instructors
Kevin Noelsaint
Anh Le
Language
English
This course belongs to a
collection
called
Data Science and Analysis Tools - from Jupyter to R Markdown. It's comprised of four courses:
- Data Analysis in R with RStudio & Tidyverse
- Data Analysis in Python with pandas & matplotlib in Spyder (viewing)
- Visualizing & Communicating Results in Python with Jupyter
- Visualizing Data & Communicating Results in R with RStudio
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