Data Visualization with Python Online Course

Three-week Data Visualization with Python Course

Data science is about helping humans understand the story behind the data, and visualizations provide a powerful tool for helping the analyst understand and communicate that story. Students in The Data Incubator's three-week, part-time Data Visualization course discuss the biases and limitations of both visual and statistical analysis to promote a more holistic approach. This course uses the same rigorous methodology as our Fellowship program.

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Experienced teachers

Learn from The Data Incubator's experienced data science instructors dedicated to teaching data analytics.

Jumpstart your career

Learn the most sought-after tools and techniques in the industry to help jumpstart your data analyst career.

Designed for working professionals

This course was designed for the busy lives of working professionals with a part-time schedule and recorded lectures.

Live lectures

Live lectures allow you to ask your instructor questions and interact with your classmates.

Curriculum

Prerequisites:

Elementary programming knowledge

Target Audience:

This course is for you if:

You have taken our Data Science Foundations or Machine Learning course and want to strengthen your data visualization skills
You are interested in presenting data-driven narratives and want to learn essential data visualization skills
You are a researcher, analyst, or data scientist interested in developing data-driven applications
You frequently interpret data for non-technical colleagues

Learning Outcomes:

Upon completion of this course, you will:

Prioritize data elements and trends according to appropriate elements of visual hierarchy
Use visualizations to guide statistical inference, machine learning, or other analyses
Use Python libraries to develop dynamic and data-driven documents visualizations
Communicate quantitative insights to non-technical audiences using explanatory visuals

Course Outline:

Visualization Theory

Grammar of Graphics Visual Data Types Categories of visual cues Generic “algorithm” for making visualizations Practical considerations Perceptually uniform color maps Dealing with multiple scales Storytelling

Exploratory Visualization

Describing a distribution Histograms Box and Violin Plots Indirect influence & constraints Non-obvious patterns in data Autocorrelation Fourier components Interactivity & Jupyter notebooks Layering information

Visualizing Large Datasets

Dimensionality Reduction Downsampling Out-of-Core Visualization

Explanatory Visualization

Interactivity and Dynamic Views Bokeh JSON schemas for grammar of graphics Plotly Vega & Altair BI Reporting/Dashboarding Extensions

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Sign up for the Data Visualization with Python Online Course

The next session of our Data Visualization with Python online course will be held in June 2019. Please enter your information in the form below to sign up for updates about the course and to receive a syllabus. You'll also be notified as soon as registration is available and gain access to special discounts.

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Interested in the course but need help with financing? We've partnered with Climb Credit to help. Climb offers 3-year financing with fixed interest rates and monthly payments. Apply online and get a decision same-day with no impact on your credit. Have questions about how it works? Check out their FAQ.

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Testimonials

"The data science skills I sharpened at The Data Incubator helped me analyze diversity in STEM education, model SaaS stock prices, and compare industry growth rates. The instructor's background in quantitative finance made for relevant use cases that benefit not only data scientists, but also data-driven finance people like myself."

Christian Templeton, Google

Formerly Salesforce, Symantec. Accepted to The Data Incubator Fellowship.

"From day one I was getting my hands dirty working with data using industry-relevant tools. Having completed the program I'm now better equipped to manage engineering and product teams, and able to conduct sophisticated ad-hoc analysis. Everyone at TDI blew me away; truly exceptional."

Michelle Miller, Hack Reactor

Formerly Berkeley, Smithsonian

"Their heavy focus on applied learning meant that I was working on real data and solving real problems right from the start. While lectures were a valuable component of their courses, the real learning took place while working on projects. These projects were practical, engaging, and instructive, and generally great opportunities to get hands-on experience with what I had learned in the lectures."