Data Science Bootcamp

Become a data scientist. Land a job or your money back.

  • Build data science skills and master machine learning techniques through our 100% online project-based curriculum

  • Get 1-on-1 mentorship + coaching to fast-track your career as a data scientist

  • Graduate from our data science course in 6 months, part-time

  • NEW! AI for Data Professionals learning units

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Our Data Science graduates have been hired by:

A data science bootcamp on your schedule, backed by our Job Guarantee

Launch your data science career in just 6 months part-time. Our flexible, human-guided curriculum featuring advanced specialization means you can learn when you want with support as you need it.

Build job-ready, in-demand skills

Build job-ready skills with 28 mini-projects and 3 capstones and an advanced specialization project that suits your career goals.

Get real human support at every step

Work 1-on-1 with an expert mentor, industry career coach and student advisor when you need guidance from course start to new job.

Backed by our Job Guarantee

We believe in you and our program, so if you don't land a data science job or you'll receive a full refund.

Drive your career through data


Average salary increase of Springboard data science students who provided pre- and post-course salaries

December 2023

In this data science bootcamp, you will learn:

  • The six steps of the Data Science Method

  • Problem identification and data wrangling

  • Analysis, modeling, and documentation

Plus, you’ll learn the tools and languages data scientists use:

In just 6 months, you'll learn to master big data to solve big business problems and transform your career.

Why data science is a good career choice:

  • The Bureau of Labor Statistics predicts that the employment of data scientists is projected to grow 38% from 2021 to 2031, much faster than the average for all occupations.

  • According to Indeed, the average salary for a data scientist in the United States is over $128,000 per year. This high salary results from the demand for data scientists and the value they bring to organizations.

  • According to a study published by McKinsey Global Institute, the U.S. economy could be short as many as 250,000 data scientists by 2027.

Make better data-driven decisions with DataCamp

We’ve partnered with DataCamp to develop this bootcamp. You’ll take courses on SQL and complete a case study using your new-found knowledge to demonstrate the skills you’ll need in your data science career.

Join an industry-leading program

We’ve helped thousands of students learn skills and land jobs. It’s why we’ve been consistently recognized as an industry leader.

Best Bootcamp


Best Bootcamp


Best Bootcamp


What you’ll learn in this data science bootcamp

We partnered with industry insiders, including DataCamp, so you can learn the skills employers look for. The curriculum features a combination of videos, articles, and hands-on projects to help you succeed as a data scientist. Over six months, you’ll not only master core data science skills, but you’ll also learn AI tools to uncover data patterns and extract insights.

Topic 1: Pre-Work

Before moving on to the core sections of the curriculum, you’ll work through exercises that will help you familiarize yourself with Python, the most popular programming language for data science tasks, and get a crash course in statistics from Khan Academy.

Topic 2: What is Data Science?

In this opening unit, you’ll receive an overview of the Data Science Method and learn the skills needed to thrive in the field. You’ll hear about your day-to-day work duties including data cleaning and building models from those in the field. 

  • Learn about key data science skills

  • Understand the six steps to the Data Science Method 

Topic 3: Problem Identification

To start a data science project, you need to know the problem you need to solve, clearly define it, then break the problem down into manageable pieces.

You’ll work through the first step of the data science method — identify the correct problem to solve and set goals for a project.

  • Work through SMART problem statements

  • Fill out problem statement worksheets

Topic 4: The Python Data Science Stack

Python is a must-have programming skill in the data science world. You’ve already laid the foundation in pre-work, but this unit will teach you the language in-depth and also help you leverage pandas for data cleaning and manipulation. 

  • Follow coding best practices in Python

  • Learn Python data types, foundations, and standard libraries

  • Learn Pandas

Topic 5: Applying the Data Science Method

This unit gives you an introduction to the steps of the Data Science Method (DSM) and closes with a guided capstone project where you’ll present to stakeholders.

  • Familiarize yourself with the six steps of the Data Science Method

  • Learn problem identification, data wrangling, exploratory data analysis, pre-processing and training data development, modeling, and documentation

  • Complete a guided capstone encapsulating steps in the DSM and presenting findings to executives

Topic 6: Data Wrangling

This unit explores wrangling — or how to clean, organize, and structure raw data — in a hands-on way by having you wrangle data for your second capstone.

  • Submit ideas and a project proposal

  • Review data types, build data profiles, and develop and understand your data's features

  • Wrangle data for your second capstone

Topic 7: SQL and Databases

You’ll learn the inner workings of Structured Query Language (SQL) to query relational database management systems. Querying helps you understand the data contained in the databases. You’ll work through DataCamp courses and then a case study.

  • Learn the landscape of SQL and databases

  • Write queries in SQL 

  • Work with relational databases in Python 

Topic 8: Statistics for Exploratory Data Analysis

Statistics is the mathematical foundation of data science. It allows you to draw useful conclusions from data. In this unit, you'll learn concepts from David Spiegelhalter’s book, “The Art of Statistics.” You’ll read through one or two chapters, work on an exercise, test your knowledge with a quiz, and review takeaway notes.

  • Become equipped with essential conceptual knowledge before diving into application statistics

  • Assess uncertainty through resampling

  • Learn probability theory and hypothesis testing

  • Delve into advanced statistics 

Topic 9: Python Statistics in EDA

Inferential statistics is a set of techniques that helps you identify significant trends and characteristics of a data set. Not only is it useful to explore the data and tell a good story, but it also paves the way for deeper analysis and actual predictive modeling. In this unit, you’ll learn several inferential statistics techniques, then take your learnings and apply the Exploratory Data Analysis (EDA) step to your second capstone.

  • Transfer statistical concepts into practical skills and learn how to implement statistical concepts in Python

  • Take a deep dive into statistical inference, hypothesis testing, and statistical modeling in Python

  • Incorporate learning from data visualization in Python 

Topic 10: Machine Learning Overview

Machine learning combines aspects of computer science and statistics to extract useful insights and predictions from data. In this unit, you'll begin to learn the foundations of machine learning and understand best practices and common challenges when working on machine learning applications.

  • Explore the fundamentals of machine learning 

  • Gain an understanding of the taxonomy of different types of ML algorithms

  • Develop an understanding of best practices and common challenges that data scientists deal with when working on machine learning applications

Topic 11: Supervised Learning

Supervised learning is one of the most commonly used forms of machine learning. In supervised learning, you give the machine your labeled training data and encode procedures for the machine to learn to assign those labels itself. 

  • Develop an understanding of supervised learning and its common applications

  • Be able to perform regression and classification techniques to solve real-world problems

Topic 12: Unsupervised Learning

Unsupervised learning requires minimal human supervision. Unlike supervised learning, the machine looks for patterns in a dataset with no pre-existing labels.  In this unit, you’ll perform clustering techniques and then complete a case study on k-clustering. 

  • Develop knowledge of common clustering types

  • Be able to perform clustering techniques to solve real-world problems

  • Complete a distance metrics exercise and a cosine similarity exercise

Topic 13: Feature Engineering

Feature engineering consists of converting data into a feature matrix to look for patterns and create features from raw data. It’s a vital skill that improves the performance of machine learning models. In this unit, you’ll work through completing exercises and honing the pre-processing and training data development side of the DSM.

  • Perform data transformation for categorical features, image features, and text features

  • Learn best practices for deriving features, handling missing data, and automated feature engineering

  • Apply feature engineering techniques to step four of your second capstone: pre-processing and training data development

Topic 14: Machine Learning Applications

Furthering your understanding of machine learning, this unit takes you behind the scenes of modeling metrics and hyperparameter tuning. You’ll complete exercises on model evaluation metrics and learn which model metric to use based on the business problem you’re trying to solve. You’ll also learn how hyperparameter tuning can make or break your model. You’ll finish up the unit by working on the modeling stage in capstone two.

  • Take a deep dive into the types of evaluation metrics for regression and classification

  • Be able to choose the best evaluation metric for your machine learning project

  • Learn best practices for model optimization

Topic 15: Data Storytelling

A data story is a powerful way to present insights to your clients, combining visualizations and text into a narrative. This final core unit will get your creative juices flowing by suggesting some interesting questions you can ask of your dataset. You’ll also execute the last stage of the DSM (Documentation) by developing a final project report.

  • Learn how to apply presentation techniques for executive (C-suite), technical, and non-technical audiences 

  • Prepare a presentation about a dataset of your choosing

  • Finalize the documentation of your second capstone project

  • Give a presentation about the work you completed for your second capstone

Topic 16: Specialization Tracks

Hone your skills in a specific area of expertise by choosing one of our three specialization track options. You’ll be able to talk to your mentor and career coach before deciding.

Option 1 — Generalist Track:
If you’re interested in gaining a wide range of skills that will help you land a job in various industries (and in various locations), the Generalist Track may be right for you.

Option 2 — Business Insider Track:
If you’re keen to learn how to draw business-focused insights from data and make actionable recommendations that can impact the company you work for, the Business Insider Track may be right for you.

Option 3 — Advanced Machine Learning Track:
If you loved the machine learning units and want to continue to learn advanced machine learning skills, including how to deploy a model to production, then the Advanced Machine Learning Track may be the right choice.

Topic 17: Projects

You’ll work on three capstone projects to give you the hands-on knowledge of working like a data scientist.

Capstone 1: You’ll be introduced to the six steps of the Data Science Method (DSM) early on in the program, then execute each of these important steps through guidance from your mentor. You’ll practice each step before applying your knowledge to your second capstone.

Capstone 2: Similar to the guided capstone one, you’ll execute the steps of the DSM but with less guidance. You’ll develop a project idea and proposal, find and wrangle data, use exploratory data analysis techniques, pre-process and create a training dataset, build a working model, then document and present your work. You’ll submit each step separately.

Capstone 3: Capstone 3 runs through the steps of the DSM, but you’ll choose your project idea depending on the specialization track you’re enrolled in.

Topic 18: Career Support

Career units throughout the bootcamp will help you create a tailored job search strategy based on your background and goals.

  • Types of industry roles 

  • Job search strategies

  • Building a network and using it to land interviews

  • Creating a high-quality resume, LinkedIn profile, and cover letter

  • Preparing for technical and non-technical interviews

  • Successful negotiation

NEW! AI learning units added to the data science curriculum

Learn to harness the transformative power of AI in the world of data. Find out how AI can help you instantly identify data patterns, actionable insights, and the best business-case decisions. Explore different types of machine learning, plus gain understanding in the ethics of AI with a focus on fairness, transparency, and privacy.  With AI you can become more powerful and a valuable asset to your employer.

Build a portfolio that proves your skills to hiring managers

The best way to learn data science is by working on projects. Complete 28 mini projects and 3 capstone projects. Build an interview-ready portfolio you can show future employers.

While working on projects, you will:

  • Identify a client’s business problem

  • Acquire, wrangle, and explore relevant data

  • Use machine learning to make predictions

  • Learn to create real-world business impact through data storytelling

Past projects from Springboard students

Kristen Colley

Capstone project: Building a Netflix-inspired algorithm for rock climbers

Frank Fletcher

Capstone project: Computer translation of ASL fingerspelling

  • Personalized guidance to move you forward

    Regular video calls with an experienced data science mentor, where you can ask the questions that matter to you

  • Accountability at every step

    Your mentor will help you stay on track and as you tackle your curriculum, project, and career goals.

  • Industry insight on demand

    Get additional 1-on-1 help from experienced data science mentors within our community, at no extra cost.

Mentor: Ryan Rosario

Machine Learning Engineer

Mentor: Sameera Poduri

Principal Data Scientist

Mentor: Eric Rynerson

Data Scientist

Land the job you want with 1:1 career coaching

Our career-focused curriculum, 1:1 calls with your career coach, and mock interviews, will help you land your dream job. You can access these and all our career support services after completing the program.

Your career coaching calls will help you:

  • Create a successful job search strategy

  • Build your data science network

  • Find the right job titles and companies

  • Craft a data science resume and LinkedIn profile

  • Prepare with mock behavioral and technical interviews

  • Negotiate your salary

A data science bootcamp with a job guarantee

Invest in yourself with confidence with the Springboard Job Guarantee. If you put in the work and don't land a job, we'll give you a refund. Terms apply.

Eligibility for the Springboard Job Guarantee: 

  • Bachelor’s Degree

  • Successful completion of all mandatory coursework, core projects and career development tasks

  • Fulfill all post-completion job search requirements — regular networking, job applications and interviewing

Our data science students launch fulfilling careers


Average salary increase of data science students who provided pre- and post-course salaries.

December 2023


Of job-qualified individuals who reported an offer, received it within 12 months of graduation.

December 2023


Enrolled students in the data science bootcamp since 2016.

December 2023

Here’s why people like you choose Springboard

My mentor was AJ Sanchez. Bless this man's soul. Halfway through my first project I freaked out and I was like, "I don't even know what any of this code means." He said, "Don't worry. We'll learn. I'll teach you. That's the point of this."

Hastings Reeves

From Opera Singer

To Business Intelligence Analyst @ Velocity Global


  • 6 months of active coding experience with a general-purpose programming language (e.g., Python, R, Java, C++)

  • Comfort with basic probability and descriptive statistics, including concepts like mean and median, standard deviation, distributions, and histograms

No data science experience?

Become a data scientist from scratch at no extra cost. Our Foundations to Core program is a beginner-friendly course that will help you build your knowledge of data science concepts and master Python programming before you take on the core Data Science Career Track curriculum.

Apply to the next cohort

This data science bootcamp is a six-month program for students devoting 20-25 hours per week, or you can dedicate more time to land your new job faster.


Every tuition option comes with Springboard's job guarantee. Get a data science job or you'll receive a full refund . Read the full Job Guarantee eligibility terms and conditions 

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