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Learn Data Science in Python

Become a data scientist


“I was nervous going into the Data Science in Python course. I soon learned that I could always rely on my mentor and Q&A sessions for patient explanations, whether it was about writing APIs or multivariate analysis. They really cared that I learned the concepts and the curriculum and went the extra mile to make sure that I succeeded. The course has helped me grow as a journalist and given me confidence to tackle new challenges.”

Angela Woodall, Data Journalist
Learn Data Science in Python Thinkful student
3
Month commitment if
you study 15 hrs/week
8
Projects added to your
personal portfolio
10
Years of average
experience per mentor

Classes start every Wednesday

$500 per month
Enroll now

Love your first mentor session or your money back.

  • Meet with an experienced data scientist every week for 45 minutes over video chat who will keep you motivated and help you learn faster
  • Make better decisions with data by learning the data scientist toolset and how to analyze and manipulate a diverse dataset
  • Customize your learning path with your mentor based on your career goals and learning style
  • Study from anywhere in the world at your own pace. On average, students finish the coursework in 3 months after studying 10-15 hours a week

Questions? Talk to Noel today

Noel, your Education Advisor, is ready to answer questions and discuss your goals.

Join the data revolution
Analyze large datasets to make better decisions

Data science is the analysis of problems critical to our lives: from helping predict the spread of highly infectious diseases to guessing which movie you're likely to enjoy on Netflix. In this course you'll learn to answer these questions and more. As you build your data science toolset, you'll manipulate Citi Bike Station Feed data and run the numbers on life expectancy using UN data. Your findings will presented through 7 projects presented online. This course is designed specifically for programmers looking to understand the fundamental tools and analysis techniques data scientists use every day. If you don't have experience with Python or scripting, we recommend enrolling in Intro to Python and Web Development course first.

A clear path to success
Life as a Thinkful student

1-on-1 mentorship

Work with an experienced data scientist to learn best practices, get feedback on your work, and fix difficult bugs in your code.

Project-based curriculum

Learn by analyzing a variety datasets from weather reports, population data, housing data from NYC.gov, and the New York Times API.

Group sessions

Join 40+ hours of Workshops and Q&A sessions every week. You'll have unlimited access as a Thinkful student.

Active community

Chat with other students, mentors, and alumni on Slack to get help instantly and learn best practices.


Your first day

Meet your mentor, access the curriculum, and join a community of 3000+ students and mentors on Slack. At Thinkful, the relationships you form with your mentor and peers help you learn faster.

During the course

Read less and build more. Each course is focused around projects to emulate real work and increase memory retention. And if you get stuck, ask your mentor or jump into daily Q&A Sessions.

Graduate on your schedule

Every Thinkful course is self-paced so there's no need to quit your job. Typically, students finish the Data Science course in 3 months with an active GitHub portfolio. All graduates receive lifetime access to the course curriculum.

Look inside the course
See what you’ll learn.

Unit 1 - The data science toolset

Concepts covered

Command Line, control flow, data types, CSVs, Pandas, Git, SQLite, relational databases

Project(s) you'll build

  • Population data from the Earth Institute. Learn to read and write files in Python and manage data using Pandas
  • Simple Weather database. Learn the basics of relational databases and how to write, query, join, and filter data with SQL.

Unit 2 - Analyzing data

Concepts covered

Probability, hypothesis testing, univariate analysis, probability distributions, probability densities, linear regression, logistic regression, data cleaning, multivariate analysis, time series, ARIMA analysis, plot autocorrelation, Naive Bayes, Markov models

Project(s) you'll build

Unit 3 - Answering questions with data

Concepts covered

SQLite, APIs, scraping, generators, iterators, functions, profiling and testing, regular expressions

Project(s) you'll build

  • How New Yorkers bike. Learn how to download data (in this case, Citi Bike) from the Internet using Python. Learn to clean, profile, and store the data into a SQLite database.
  • Analyzing temperature data. Learn to clean, profile, and store data into a SQLite database.
  • Do Wealthier Countries Provide Better Education? Perform a regression analysis of educational life expectancy with GDP to identify whether there's a correlation between the two.

Unit 4 - Predicting the future

Concepts covered

Classification, regression, prediction, decision trees, random forest analysis, Naive Bayes, k-Nearest Neighbor, clustering, support vector machines, PCA, LDA, accuracy, precision, recall, F1 scores, Monte Carlo Simulation, Artificial Neural Networks

Project(s) you'll build

  • NYC.gov Housing dataset. Use machine learning algorithms to analyze housing data in Manhattan. Perform a comprehensive analysis using the concepts discussed in the unit. Be sure to describe any decisions that could be made or actions that could be taken from this analysis.
  • New York Times developer API. Apply Naive Bayes training and classification algorithms to implement a classifier which, given the text of an article from the New York Times, predicts the section to which the article belongs to.

Unit 5 - Wide world of data science

Concepts covered

Deep learning, natural language processing, MapReduce and Hadoop, mapping data, geospatial analysis, geostatistics, information visualization

Project(s) you'll build

  • Capstone project. Explore important topics in the world of data science. Here, you'll conduct your own independent analysis on a question of your choosing.
Success stories
Apply your data science skills on the job

Classes start every Wednesday

$500 per month
Enroll now

Love your first mentor session or your money back.

Talk to an education advisor

Have any questions? Talk to Noel.


Noel Duarte, Education Advisor

If you have questions about pricing, scholarships, financing options, or just general inquiries about how Thinkful works – hit up Noel. He's happy to assist you!

Schedule a call Email Noel
Noel Duarte, Education Advisor

Data Science in Python reviews

See what students are saying


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by Matt Hofmann

April 19th, 2016

Data Science in Python

coursereport small logo via Course Report

I had a powerful learning experience with Thinkful's Data Science track. I was grateful for the mentor's extensive background, and he brought in a valuable perspective on the course materials. The curriculum itself was challenging, and forces the user to learn how to interpret the online documentation for the various libraries used. This is beneficial for the long term - a data scientist needs to know current tools but also needs to be able to pick up new tools quickly, and Thinkful delivers on both. Occasionally there were some old links and some vague directions in the curriculum, but the Thinkful slack channel, the mentor, and the admins were quick to respond with help.

reviews star reviews star reviews star reviews star reviews star

by Jesse

April 9th, 2016

Data Science in Python

coursereport small logo via Course Report

After getting a good grasp of the Python programming language, I don't know where I could start learning about the Data Science due to its broadly immense field of study. Not until I found Thinkful. I enrolled in Data Science in Python, because I thought it would introduce me to this field and have a better idea on the scope and analytics commonly used.

I got more than what I bargained for as the curricula goes through teaching data import and extraction from numerous sources, and once that is done, delves right into the many, many analytical methods which honestly took a bulk of my time to learn and understand. I did wish I get to learn more. My mentor, Rowan Copley, is a quirky yet awesome individual that is always available for help both during and not during mentoring sessions. He also provides great books to read or up-to-date news on this field to keep me motivated throughout the course.

However, in the end, I get to build up a great looking repository of my journey. So thanks Thinkful!

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by Jonathon Jenkins

March 9th, 2016

Data Science in Python

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I'm enjoying the Data Science using Python course. It's teaching me about statistics and data data science basics along with learning to program in python.

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