Flexible Data Science Bootcamp

Take the first step to becoming a data scientist today.

Talk to an education advisor

Let's start by getting to know each other. During this 15 minute call, we'll make sure your background, goals, schedule, and learning preferences align well with the course. We'll also answer any questions about how the bootcamp works, career prep, and financing options.

Benjy is ready to discuss your goals and clarify any program details.

Start the prep course

Learn what it’s like to be a Thinkful student with this 3-week prep course. The prep course is a microcosm of the whole bootcamp, covering a number of topics as a standalone introduction to Data Science and culminating in a data-driven capstone project. In total, you will be expected to commit 20-25 hours to the course work.

Hold yourself accountable with three 1-on-1 mentor sessions each week with an experienced data scientist.
Learn programming in Python, the toolkit data scientists use daily, and core statistics concepts.
Join a community of students on Slack and access 40+ hours of weekly office hours over video chat.

Enroll in the prep course Prep course details

Prep cost is $500.
This cost is deducted in full from the bootcamp tuition if you are accepted.

Join the Data Science Bootcamp

After the prep course, the program manager will evaluate your comprehension of the concepts along with important factors like discipline and work ethic.

Total bootcamp tuition is $8,550 for 6 months. You can find more information on the payment plans below.

View Financing & Scholarships

Inside the course

All the resources you'll need to succeed.

1-on-1 mentorship

You'll meet with your mentor three times each week. In the meetings, you'll navigate difficult concepts, learn best practices, and plan your upcoming work to align with your interests. Together, you'll customize your path by specializing in natural language processing, econometrics, or data visualization — to name a few. Your mentor isn't your only teacher. With dozens of mentors hosting open Q&A sessions every day, you can join in and get your questions answered at any time, from multiple perspectives.

Project-based curriculum

Employers want to see real work, not exercises and test results. That's why the curriculum guides you to learn the concepts then dives straight into building unique projects using the tools the industry demands. Learning happens when you connect new concepts and with hands on experience and feedback.

Career Prep

From the first month onwards, you'll get interview prep and practice coding, statistics, and visualization challenges to start on the path toward job placement. Before you know it, you’ll be polishing your resume and signing your cover (and offer!) letters.

Community

Join a community of students, mentors, and alum to get your questions answered in real-time, share your success, vent your frustration, and meet people who share the same goals.

Workshops and Q&A sessions

Every student has acesss to 40+ hours of group mentorship every week. Q&A sessions allow students to ask questions and get help while workshops encourage mentors to highlight a specific topic in a project.

Look inside the course

See what you’ll learn.

Your email

Unit 1 - Data and Analysis

Concepts covered

Matplotlib, SQL, SQLite, Data Cleaning, Data Visualization, Seaborn, Experimental design, A/B Testing

Project(s) you'll build

SQL Challenge - Solve questions about AirBnB data using SQL queries with a database that you'll set up locally.
Data Cleaning & Validation - Practice data cleaning & validation using data from WELLCOME Trust on open access publishing.
Your First Research Proposal - Using a dataset of your own choice, create your first Research Proposal (also known as an Experimentation RFC).

Unit 2 - Supervised Learning

Concepts covered

PCA, Feature engineering, Naive Bayes, Regression models, Classification models, Least Squares Regression, Multivariable Regression, Class Imbalance

Project(s) you'll build

Prepare a Dataset for Modeling - Using a dataset of your choice, you will explore variables using univariate and bivariate methods.
Build your Own Naive Bayes Classifier - Perform a sentiment analysis on feedback left on a website to determine if it is positive or negative.
Classifier Validation - Test the performance of your classifier from the previous project and learn how to improve it.
Your First Multivariate Linear Regression Model - Build a regression model using FBI UCR Crime data in order to predict property crimes.
Validating a Linear Regression - Validate your property crime model and based on the results create a revised model. Test both old and new models on a new holdout or set of folds.

Unit 3 - Deeper into Supervised Learning

Concepts covered

Similarity Models, KNN, Decision Trees, Random Forest, ID3 Algorithm, Ensemble Modeling, Advanced Regression, Support Vector Machines, Boosting Models

Project(s) you'll build

Model Comparison - Using your own chosen data set build a KNN and an OLS regression and compare them.
Random Forests & Decision Trees - Compare the relative accuracy of random forests and decision trees using a data set of your choosing.
Support Vector Machines Challenge - Translate a weak SVR into a more accurate SVC.
Boosted Models - Give your model a boost in the Boosted Model Challenge.

Unit 4 - Unsupervised Learning

Concepts covered

Unsupervised learning, Basic Clustering, K - Means, Clustering Evaluation, NLP (Natural Langage Processing), Neural Networks, Deep Learning

Project(s) you'll build

Supervised vs Unsupervised Drill - Determine whether a problem is best solved using supervised or unsupervised techniques.
Applying K Means - Use your knowledge of basic clustering to determine variance with changes in K.

Unit 5 - Other Topics in Data Science

Concepts covered

Algorithms, Data Scraping, Big Data, Survey Design, Privacy and Data Science

Project(s) you'll build

Data Scraping - Learn the value of Data Scraping and practice on a source of your choosing.
Survey Design - Create a survey on the topic of your choosing and gather data from users.
Algorithms - Build your own algorithm for some of the models we’ve gone over so far!

Unit 6 - Specializations

This is an opportunity to work with a mentor to cover topics more aligned to your chosen specialization. This can include but is not limited to Natural Language Processing, TensorFlow, Econometrics, Social Genomics/Quantified Self, Time Series and Forecasting, Anomaly Detection, or Visualization.

Unit 7 - Final Capstone Project

Project(s) you'll build

Final Capstone Project - Using what you have learned, you will create a custom project showcasing your new data science skills as well as specialization knowledge.

Unit 1 - Data and Analysis

Concepts covered

Matplotlib, SQL, SQLite, Data Cleaning, Data Visualization, Seaborn, Experimental design, A/B Testing

Projects you'll build

SQL Challenge - Solve questions about AirBnB data using SQL queries with a database that you'll set up locally.
Data Cleaning & Validation - Practice data cleaning & validation using data from WELLCOME Trust on open access publishing.
Your First Research Proposal - Using a dataset of your own choice, create your first Research Proposal (also known as an Experimentation RFC).

Unit 2 - Supervised Learning

Concepts covered

PCA, Feature engineering, Naive Bayes, Regression models, Classification models, Least Squares Regression, Multivariable Regression, Class Imbalance

Projects you'll build

Prepare a Dataset for Modeling - Using a dataset of your choice, you will explore variables using univariate and bivariate methods.
Build your Own Naive Bayes Classifier - Perform a sentiment analysis on feedback left on a website to determine if it is positive or negative.
Classifier Validation - Test the performance of your classifier from the previous project and learn how to improve it.
Your First Multivariate Linear Regression Model - Build a regression model using FBI UCR Crime data in order to predict property crimes.
Validating a Linear Regression - Validate your property crime model and based on the results create a revised model. Test both old and new models on a new holdout or set of folds.

Unit 3 - Deeper into Supervised Learning

Concepts covered

Similarity Models, KNN, Decision Trees, Random Forest, ID3 Algorithm, Ensemble Modeling, Advanced Regression, Support Vector Machines, Boosting Models

Projects you'll build

Model Comparison - Using your own chosen data set build a KNN and an OLS regression and compare them.
Random Forests & Decision Trees - Compare the relative accuracy of random forests and decision trees using a data set of your choosing.
Support Vector Machines Challenge - Translate a weak SVR into a more accurate SVC.
Boosted Models - Give your model a boost in the Boosted Model Challenge.

Unit 4 - Unsupervised Learning

Concepts covered

Unsupervised learning, Basic Clustering, K - Means, Clustering Evaluation, NLP (Natural Langage Processing), Neural Networks, Deep Learning

Projects you'll build

Supervised vs Unsupervised Drill - Determine whether a problem is best solved using supervised or unsupervised techniques.
Applying K Means - Use your knowledge of basic clustering to determine variance with changes in K.

Unit 5 - Other Topics in Data Science

Concepts covered

Algorithms, Data Scraping, Big Data, Survey Design, Privacy and Data Science

Projects you'll build

Data Scraping - Learn the value of Data Scraping and practice on a source of your choosing.
Survey Design - Create a survey on the topic of your choosing and gather data from users.
Algorithms - Build your own algorithm for some of the models we’ve gone over so far!

Unit 6 - Specializations

Concepts covered

Projects you'll build

Unit 7 - Final Capstone Project

Concepts covered

Projects you'll build

Final Capstone Project - Using what you have learned, you will create a custom project showcasing your new data science skills as well as specialization knowledge.

Payment calculator

Payment plans for Data Science Bootcamp.

Are you eligible for a scholarship?

Which financing option?

	Upfront	Month to month	Skills Fund loan	Deferred	Income share agreement (ISA)
How to choose	Most affordable	Pay as you go	Lowest monthly payments	For budgets of $500 / month	Only pay once you start a job
Paid by you on your start day	$8,550	$1,500	$250 Not covered by the loan	$2,500	$1000 Refundable deposit
Monthly payments	-	$1,500 for 5 months	$290 for 36 months, beginning on 2nd month	$500 for 17 months, beginning on 2nd month	10-15% of income for 3 years
Total cost	$8,550	$9,000	$10,665Loan size of $8,750	$11,000	Variable based on income
	Enroll	Enroll	Apply to Skills Fund	Enroll
			Available by application to qualifying US citizens and permanent residents.

Upfront
Pay as you go
Due on start day	$8,550
Monthly payments	-
Total cost	$8,550
Enroll

Month to month
Pay as you go
Due on start day	$1,500
Monthly payments	$1,500 for 5 months
Total cost	$9,000
Enroll

Deferred
For budgets of $500 / month
Due on start day	$2,500
Monthly payments	$500 for 17 months, beginning on 2nd month
Total cost	$11,000
Enroll

Income share agreement (ISA)
Only pay once you start a job
Due on start day	$1000 (Refundable deposit)
Monthly payments	10-15% of income for 3 years
Total cost	Variable based on income
Enroll

Skills Fund loan
Lowest monthly payments
Due on start day	$250 Not covered by the loan
Monthly payments	$290 for 36 months, beginning on 2nd month
Total cost	$10,665Loan size of $8,750
Apply to Skills Fund Available by application to qualifying US citizens and permanent residents.

Loans with Skills Fund are available by application to qualifying US citizens and permanent residents.

Enroll in prep course

Learn more about the prep course