Class is in session
Jump Into Data Science in Python
9.3
(3 ratings)
3 Weeks
·Cohort-based Course
Stop clicking through Excel and start comfortably coding in Python with this live cohort-based course brought to you by Yale Faculty.
Class is in session
Jump Into Data Science in Python
9.3
(3 ratings)
3 Weeks
·Cohort-based Course
Stop clicking through Excel and start comfortably coding in Python with this live cohort-based course brought to you by Yale Faculty.
Hosted by
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Dr. Elena Grewal and Sarah McGowan
Yale Faculty, ex-Head of Data Science @ Airbnb, ex-Bain, Stanford University
Course overview
Real World Data Science in 3 Weeks
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.
Who is this course for?
01
New Data Scientists
You want to transition your career to data science, but don't know where to start
02
Curious about Coding
You've thought about learning to code, but the classes didn't seem appealing
03
Analysts / Researchers
You use Excel and know there is something more powerful out there
What will you do?
Learn Python Basics
Get started in Python using the data scientists tool of choice - notebooks - and learn how to write your code like a professional data scientist.
Load and Clean Data
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.
Visualize 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.
Use Statistics
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.
Take the Next Step
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!
Jump Into Data Science in Python
9.3
(3 ratings)
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Meet your instructor
Dr. Elena Grewal
Dr. Grewal was the Head of Data Science at Airbnb. At Airbnb, she spearheaded Data University which trained hundreds of non-technical people in data skills. After seven years on the rocketship, she left Airbnb and started a data science consultancy with Sarah called Data 2 the People where they do data science work for values-aligned projects in politics, criminal justice, education, and climate change. She has a Ph.D. from Stanford University and a B.A. from Yale University.
Sarah McGowan
Today, in her work with Elena at Data 2 the People, Sarah uses Python to help clients optimize processes, scenario-plan, measure progress, and answer pressing organizational questions. Prior to co-founding Data 2 the People, Sarah was a Consultant at Bain & Co. Sarah is passionate about sharing data science skills with others and created and teaches a top-rated course at the Yale School of the Environment with Elena. Sarah has a B.E. and B.A. from Dartmouth College.
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Course syllabus
01
Module 1: What is Python?
- Class Intro
- Why use Python?
- Set up Python notebook
- Intro to Python and Pandas (Variable types, data structures, getting data in and out, modules, subsetting)
- Best practices for syntax & commenting
02
Module 2: Data Tidying
- Reshaping
- Merging
- Filtering & logic
- String splitting
- Casting
- Calculated fields
- What do I do if I need help?
03
Module 3: Data QA
- Duplicates
- Missing values
- Summary Statistics
- distributions, trends, and outliers
- P-hacking
- Relationships between pairs of variables
04
Module 4: Visualization and a Taste of Statistics
- Variable transformations
- Plotting
- Tables
- Regressions and T-tests
Course Work
3 weeks, 15 hours per week
At-home Tutorials (Monday/Wednesday - Sept 25th, 27th, Oct 2nd, 4th)
2-3 hrs
Kick off new topics at your own pace with tailor-made videos and quick practice exercises. Ask instructors questions as they come up.
Live Workshops (Tuesday/Thursday - Sept 21st, 26th, 28th, Oct 3rd, 5th, 10th
6:00PM - 7:30PM EST
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 😊
Course Schedule
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Learning is better with cohorts
Active learning, not passive watching
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Get reimbursed by your employer
Take advantage of education funds
Many employers have education budgets set aside for employees to attend courses, seminars, or conferences.
Even companies without explicit education benefits may still subsidize learning
Most managers will find ways to keep their teams growing and developing and can approve one-off expenses, even if they don't have dedicated education funds
Email Template:
Click here for an email template to ask your manager for reimbursement
Frequently Asked Questions
What happens if I can’t make a live session?
How much can I really learn in 3 weeks?
I work full-time, what is the expected time commitment?
Are there any pre reqs for this course?
Are there any hardware or software requirements?
Who should NOT take this course?
What’s the refund policy?
What if I still have questions?
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