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Numpy, Pandas, Matplotlib

Mat Leonard, Andrew Paster, Jennifer Staab, Luis Serrano, Juan Delgado, Juno Lee, Mike Yi, Grant Sanderson, and Ortal Arel

Let's focus on library packages for Python, such as : Numpy (which adds support for large data), Pandas (which is used for data manipulation and analysis) And Matplotlib (which is used for data visualization).

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What's inside

Syllabus

Anaconda is a package and environment manager built specifically for data. Learn how to use Anaconda to improve your data analysis workflow.

Jupyter Notebooks are a great tool for getting started with writing python code. Though in production you often will write code in scripts, notebooks are wonderful for sharing insights and data viz!

Learn the basics of NumPy and how to use it to create and manipulate arrays.

Learn the basics of Pandas Series and DataFrames and how to use them to load and process data.

Learn how to use matplotlib and seaborn to visualize your data. In this lesson, you will learn how to create visualizations to depict the distributions of single variables.

In this lesson, you will use matplotlib and seaborn to create visualizations to depict the relationships between two variables.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores the use of Python for data science tasks including data manipulation, analysis, and visualization

Taught by experienced instructors in the field of data science

Incorporates hands-on labs and uses Jupyter Notebooks, which is a widely used tool in data science

Covers the fundamentals of NumPy, Pandas, and Matplotlib, which are essential libraries for data analysis and visualization

Provides a strong foundation for beginners who want to start with data science

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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 Numpy, Pandas, Matplotlib with these activities:

Review foundational concepts of Python data types and control flow

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Provides an opportunity to refresh and strengthen the foundational knowledge of Python, which is essential for success in the course.

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Review notes or online resources on Python data types (e.g., int, float, string)
Revisit the concepts of control flow (e.g., if-else, loops)
Solve practice problems or coding exercises to apply these concepts

Participate in a study group to discuss course concepts and assignments

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Encourages students to actively engage with the material through collaborative learning, fostering a supportive learning environment and improving retention.

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Form or join a study group with peers
Meet regularly to discuss course topics, assignments, and projects
Share perspectives, ask questions, and provide support
Reflect on the study group experience and identify areas for improvement

Practice exercises on data visualization with Pandas and Matplotlib

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Provides additional practice to reinforce students' understanding of data visualization techniques, improving their proficiency.

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Find online resources or exercises for data visualization with Pandas and Matplotlib
Solve the exercises to practice creating different types of visualizations
Experiment with different visualization parameters to explore their effects
Compare and contrast different visualization techniques for different datasets

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Explore advanced tutorials on NumPy array manipulation

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Encourages students to go beyond the course material and delve into more advanced concepts, fostering independent learning.

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Identify specific areas of NumPy array manipulation to focus on
Find online tutorials or articles covering these topics
Follow the tutorials and complete the exercises provided
Apply the learned techniques to enhance data analysis projects

Create a visual presentation explaining the concepts of Pandas DataFrame

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Encourages students to actively engage with the material by creating a visual aid, solidifying their understanding and improving retention.

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Review the concepts and structure of a Pandas DataFrame
Choose a visual presentation format (e.g., slides, infographics, animations)
Develop the visual content to explain the key concepts of a DataFrame
Present the visual presentation to peers or mentors for feedback

Start a data analysis project with libraries learned in the course

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Engages students in practical application of learned libraries to strengthen understanding and retention.

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Define a data analysis problem to solve
Gather and clean the necessary data
Apply NumPy and Pandas for data manipulation
Use Matplotlib for data visualization
Interpret results and derive insights

Mentor a beginner in Python and libraries used in the course

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Provides an opportunity for students to solidify their understanding by teaching others, fostering deeper retention and strengthening communication skills.

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Identify a beginner in Python who could benefit from guidance
Provide guidance on the fundamentals of Python and the libraries covered in the course
Offer support and answer questions as the mentee progresses
Reflect on the mentoring experience and identify areas for improvement

Contribute to an open-source project related to NumPy or Pandas

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Provides practical experience in applying the libraries in a real-world setting, fostering collaboration and deeper understanding.

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Identify an open-source project that aligns with the course topics
Explore the project's codebase and identify areas where contributions can be made
Submit a pull request with proposed changes
Participate in code reviews and discussions with project maintainers

Career center

Learners who complete Numpy, Pandas, Matplotlib will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of data analysis tools and techniques, such as those in this course, to extract insights from data, build predictive models, and develop solutions to business problems. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to examine and interpret data, identify trends and patterns, and make recommendations based on their findings. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to build and deploy machine learning models. This course can help you build a foundation in the skills and knowledge needed to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use their knowledge of data analysis tools and techniques, such as those in this course, to collect, analyze, interpret, and present data. This course can help you build a foundation in the skills and knowledge needed to succeed as a Statistician.

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Quantitative Analyst

Quantitative Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to develop and implement mathematical models to analyze financial data and make investment decisions. This course can help you build a foundation in the skills and knowledge needed to succeed as a Quantitative Analyst.

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Financial Analyst

Financial Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze financial data and make investment recommendations. This course can help you build a foundation in the skills and knowledge needed to succeed as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use their knowledge of data analysis tools and techniques, such as those in this course, to assess and manage financial risk. This course can help you build a foundation in the skills and knowledge needed to succeed as an Actuary.

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Market Researcher

Market Researchers use their knowledge of data analysis tools and techniques, such as those in this course, to collect and analyze data about consumer behavior and trends. This course can help you build a foundation in the skills and knowledge needed to succeed as a Market Researcher.

See salaries and explore the career path for Market Researcher

Business Analyst

Business Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze business data and make recommendations to improve business performance. This course can help you build a foundation in the skills and knowledge needed to succeed as a Business Analyst.

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Computer Programmer

Computer Programmers use their knowledge of data analysis tools and techniques, such as those in this course, to develop and maintain computer programs. This course can help you build a foundation in the skills and knowledge needed to succeed as a Computer Programmer.

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Operations Research Analyst

Operations Research Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze and solve business problems. This course can help you build a foundation in the skills and knowledge needed to succeed as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Software Engineer

Software Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to develop and maintain software applications. This course can help you build a foundation in the skills and knowledge needed to succeed as a Software Engineer.

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Database Administrator

Database Administrators use their knowledge of data analysis tools and techniques, such as those in this course, to manage and maintain databases. This course can help you build a foundation in the skills and knowledge needed to succeed as a Database Administrator.

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Data Engineer

Data Engineers use their knowledge of data analysis tools and techniques, such as those in this course, to design, build, and maintain data infrastructure. This course can help you build a foundation in the skills and knowledge needed to succeed as a Data Engineer.

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Systems Analyst

Systems Analysts use their knowledge of data analysis tools and techniques, such as those in this course, to analyze and design business systems. This course can help you build a foundation in the skills and knowledge needed to succeed as a Systems Analyst.

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Reading list

We've selected 13 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 Numpy, Pandas, Matplotlib.

Python for Data Analysis

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Provides a comprehensive overview of Python's capabilities for data analysis, covering essential topics such as data structures, data manipulation, and statistical analysis. It valuable resource for both beginners and experienced Python users looking to enhance their data analysis skills.

Numpy, Pandas, Matplotlib

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What's inside

Syllabus

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Activities

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Reading list

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