Cohort 2

Stop clicking through Excel and start comfortably coding in Python with this live cohort-based course brought to you by Yale Faculty.

Dr. Elena Grewal

Sarah McGowan

Jump Into Data Science in Python

Develop the most useful day-to-day data science skills by working closely with the instructors during this intensive 3-week course. Have fun alongside a group of curious students (welcoming to women and non-binary students!). This course will change your mind about coding being boring or too hard. The class is a "flipped class", meaning you'll watch tailor-made videos at your own pace, and we'll convene 2 times a week (from 6:00-7:30pm EST) for hands-on practice. Throughout, we'll be here to answer questions and give you personal feedback. We are here for you. This class is best for students with familiarity with datasets (eg. Excel tables), and a solid understanding of high school math, but we don't expect any coding experience. The class is based on the top-rated class Dr. Grewal and McGowan teach at the Yale School of Environment.

Real World Data Science in 3 Weeks

New Data Scientists You want to transition your career to data science, but don't know where to start

Curious about Coding You've thought about learning to code, but the classes didn't seem appealing

Analysts / Researchers You use Excel and know there is something more powerful out there

Who is this course for?

What will you do?

Get started in Python using the data scientists tool of choice - notebooks - and learn how to write your code like a professional data scientist.

Learn Python Basics

The best data scientists know that understanding where your data comes from and finding all the "dirty secrets" of the data can make or break your analysis. You will learn key tools to do this.

Load and Clean Data

There is nothing more powerful than a chart that tells you exactly what you need to know. We cover different types of data visualizations, including maps.

Visualize Data

Start to separate the signal from the noise and reveal what your data is telling you. While we won't teach much statistics, we'll get you started.

Use Statistics

Finally, we review how to solve your own problems when you hit roadblocks and what topics you can learn next. Data scientists are constantly learning, so this last part is important!

Take the Next Step

<ul><li>Class Intro</li><li>Why use Python?</li><li>Set up Python notebook</li><li>Intro to Python and Pandas (Variable types, data structures, getting data in and out, modules, subsetting)</li><li>Best practices for syntax &amp; commenting</li></ul>

Module 1: What is Python?

<ul><li>Reshaping</li><li>Merging&nbsp;</li><li>Filtering &amp; logic</li><li>String splitting</li><li>Casting</li><li>Calculated fields</li><li>What do I do if I need help?</li></ul>

Module 2: Data Tidying

<ul><li>Duplicates</li><li>Missing values</li><li>Summary Statistics</li><li>distributions, trends, and outliers</li><li>P-hacking</li><li>Relationships between pairs of variables&nbsp;</li></ul>

Module 3: Data QA

<ul><li>Variable transformations</li><li>Plotting</li><li>Tables</li><li>Regressions and T-tests</li></ul>

Module 4: Visualization and a Taste of Statistics

Course syllabus

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Meet your instructor

Kick off new topics at your own pace with tailor-made videos and quick practice exercises. Ask instructors questions as they come up.

At-home Tutorials (Tuesdays, Wednesdays, Fridays)

Reinforce your learning with guided problem sets that build on everything you've learned. We'll work in small group with a lot of instructor attention. When you're done, you'll get detailed, personal feedback from us on the final work you submit - we won't call it grading 😊

Live Workshops (Mondays and Thursdays)

Course Work

Course Schedule

This course builds on live workshops and hands-on projects

Active learning, not passive watching

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

Learn with a cohort of peers

Learning is better with cohorts

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