Data analysis and data visualization are essential skills for data professionals. With the demand for data professionals expected to increase by nearly 30% in the next five years, it is important to start developing new skills you can implement in your career. Python offers some great tools for analyzing data and creating different kinds of visuals to recognize patterns in data and create insights.

In this skill path, we'll introduce you to popular open-source Python libraries for data analysis. Additionally, you’ll learn the fundamentals of data visualization in Python by exploring graph interpretation (statistics) and tooling (Matplotlib and seaborn).

  • Discover powerful libraries like pandas and NumPy for data analysis
  • Create and update dictionaries, create custom functions, and use Jupyter Notebook
  • Build intermediate-level skills like cleaning, correcting, and preparing data for analysis

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What You’ll Learn

In our Data Analysis and Visualization path, you’ll develop the skills to read and interpret data to identify patterns and trends and gain valuable insights. This path covers Python programming fundamentals, data cleaning and processing, and visualization — as well as manipulation, and reading and interpreting data.

After completing our hands-on guided projects and practice problems, you’ll be able to explore and analyze data using common visualization techniques. You’ll also be able to convey insights and tell a story.

Enroll in this skill path if you want to learn how to do the following:

  • Implement the fundamentals of programming in Python
  • Build for loops, work with lists, and use conditional statements in Python
  • Clean and analyze text data
  • Create and update your own dictionaries and functions
  • Build a portfolio project
  • Leverage vectorized operations to improve working with data
  • Visualize and explore data
  • Generate graphs using information design principles
  • Create storytelling data visualizations using Matplotlib
Data Scientist in Python Salary Increase

Data visualization salaries in the U.S. average $85K/yr according to Glassdoor

Data Scientist in Python Job Openings

The U.S. Bureau of Labor Statistics projects 28% growth in data science job skills by 2026.

Data Scientist In Python Job Growth

Data analysts projected 20% growth between 2018 and 2028, according to Indeed

How Our Data Analysis and Visualization Path Works

Learning Python for data analysis and visualization with Dataquest can help you develop the analytical, management and design skills you need to translate large and complex datasets into easy-to-understand visual representations. To most people, a Dataframe, even if it’s clean and uniform, looks like an incomprehensible mess. That’s precisely why we’ve created this skill path⁠—to teach you the necessary data analysis and visualization skills to make data insightful.

To meet the high demand for data professionals who can handle all the data companies are collecting and processing, we teach you the skills you need to position yourself for job growth in the future.

This is why we teach the way we do. And this is why 97% of our learners prefer our teaching method. Because in order to truly gain the in-demand data visualization skills hiring managers are looking for, you need to be actually practicing those skills.

Additionally, with Dataquest, you’ll never learn alone. From our thriving community to our robust support tools, we offer a helping hand to guide you through any challenge or roadblock you may come across.

Here’s a quick glance at our Data Analysis and Visualization path:

  • This skill path consists of the six courses listed below, which cover basic and intermediate topics about data analysis and visualization with Python.
  • You’ll write real code with dozens of practice problems to validate and apply your skills.
  • At the end of each course, you’ll complete a guided project to reinforce your new knowledge and expand your portfolio.
  • When you complete each course, you’ll receive a certificate that you can share with your professional network.
  • Once you complete this skill path, you’ll be ready for more advanced data analyst courses.
  • Engage with our friendly community of data analysts, get feedback on your projects, and keep building your skills.

Enroll in this career path to learn Python for data analysis and visualization!

Data Analysis and Visualization Path Course List

Python for Data Science: Fundamentals Part I
Learn the fundamentals of Python programming and data science.

Python for Data Science: Fundamentals Part II
Learn the fundamentals of Python programming and data science.

Python for Data Science: Intermediate
Learn important Python data science skills.

Pandas and NumPy Fundamentals
Learn how to analyze data using the pandas and NumPy libraries.

Exploratory Data Visualization
Learn how to explore data by creating and interpreting data graphics. This course uses Matplotlib and pandas.

Storytelling Data Visualization and Information Design
Learn storytelling data visualization, information design principles, and advanced graph customization in Matplotlib.

Who Is This Data Analysis and Visualization Skill Path For?

  • People who want a career as a data analyst or data scientist
  • People seeking remote work
  • Anyone who works with data in telecommunication, finance, education, and healthcare
  • Junior data scientists or data analysts who want to advance in their current position
  • Anyone who wants to be able to capture, process, and interpret data
  • Anyone who wants to discover how statistics and probabilities work with data
  • Students who want to develop a competitive portfolio
  • Data scientists or data analysts who want to expand the tools at their disposal
  • Python users seeking variety in their skills 

Qualify for In-demand Jobs in Data Analysis and Visualization

Data analysis and visualization skills are applicable across many different industries and positions. The skills you’ll learn in this path can be used in the following in-demand data jobs:

  • Data visualization engineer
  • Data analyst
  • Financial data analyst
  • Business data analyst
  • Data engineer
  • Data scientist
  • Data conversion analyst
  • Data governance analyst
  • Data visualization developer
  • Data management analyst
  • Healthcare data analyst
  • BI analyst