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Data Science Foundations to Core Bootcamp

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

Become a data scientist from scratch. Learn on your schedule with 1-on-1 support at every step. Land a job or your money back.


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Graduate in 7 months

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Go from beginner to job-ready in as little as 7 months

This program was built to give beginners a pathway to a data science career. You’ll start with the Foundations curriculum — learning the skills needed to pass the Data Science Career Track admissions Technical Skills Survey. Then you’ll transition to the Core curriculum. All at no extra cost.

By the end of Foundations you will:

  • Use Python to complete real-world coding exercises and begin your data science journey

  • Get comfortable with aspects of basic probability

  • Solve a real business problem — practice using data science tools to analyze and visualize data

  • Confidently tackle the Data Science Career Track Technical Skills Survey — what you’ll need to pass to start the Core curriculum


Data Science Foundations to Core vs. Career Track

Get everything in the Data Science Career Track and more, all for the same cost when you enroll in Data Science Foundations to Core.

Data Science Foundations to Core bootcamp

Data Science Career Track bootcamp

Previous Python coding experience required

No, you’ll learn Python and more in Foundations


Course length (part-time)

7 months

6 months

Award winning data science curriculum



Springboard Job Guarantee*



1:1 expert mentorship



1:1 career coaching



Lifetime access to your Springboard account



Upfront tuition with discount**



*Terms apply | **Total cost varies based on approved interest rate. Financing only available for U.S. residents

What you’ll learn in this data science bootcamp

Over seven months, you’ll learn the core skills needed to succeed as a data scientist. You’ll start work covering foundational Python, statistics, and probability skills. Once you pass the technical skills survey, you’ll enter Core, and unlock more technical units that encompass intricate topics like data wrangling, SQL & Databases, and machine learning.

Get your syllabus

1: Intro to Data Science & Python

2: Intermediate Python

3: Foundations of Probability

4: Python: Data Structures and Algorithms

5: Technical Skills Survey

6: Your Data Science Toolbox

7: The Apps Project

8: Pre-Work

9: What is Data Science?

10: Problem Identification

11: The Python Data Science Stack

12: Applying the Data Science Method

13: Data Wrangling

14: SQL and Databases

15: Statistics for Exploratory Data Analysis

16: Python Statistics in EDA

17: Machine Learning Overview

18: Supervised Learning

19: Unsupervised Learning

20: Feature Engineering

21: Machine Learning Applications

22: Data Storytelling

23: Specialization Tracks

24: Projects

25: Career Support

Topic 1: Intro to Data Science & Python

In this opening unit, you’ll learn about the common problems that data scientists solve and get your feet wet in Python and basic statistical concepts.

  • Learn why Python is such a popular programming language

  • Start building Python programming skills

  • Learn about computational thinking and its importance in data science

Topic 2: Intermediate Python

You’ll build your Python skills further by exploring object-oriented programming and Python strings.

  • Learn the difference between functions and methods

  • Learn how to interact with a program in Python effectively

  • Explore Python tools like dictionaries and strings

Topic 3: Foundations of Probability

Probability is the science of uncertainty,  and anyone in data science, machine learning, or AI has to be comfortable with it. In this unit, you’ll learn the fundamentals of probability and how it relates to data science.

  • Calculate basic probabilities

  • Study Bayes Theorem

  • Understand the concept of conditional probability

Topic 4: Python: Data Structures and Algorithms

You’ll explore Python further by studying data structures and algorithms to understand how they can be used to solve problems in programmable ways.

  • Learn data structures, including stacks and queues

  • Familiarize yourself with sort and search algorithms

  • Tackle a hands-on project for data structures and algorithms

Topic 5: Technical Skills Survey

The Technical Skills Survey (TSS) will determine if you’re ready to move into the Core units.. You’ll be able to take the TSS as many times as you like. A rundown of the TSS:

  • Features key statistics and Python programming skills

  • Format is 10 statistics multiple choice questions and two open-ended coding questions

  • Once you pass, you will finish two more units before moving on to the Core units

Topic 6: Your Data Science Toolbox

Once you pass the foundational units and the TSS, you will unlock a powerful unit that will take you deeper into the world of data science, including learning the necessary tools to succeed. 

  • Install a Python toolkit and get set up using Anaconda

  • Learn about Git and GitHub

  • Learn how to work with Jupyter Notebooks

  • Discover the benefits of NumPy and Matplotlib

  • Get experience working with Pandas

Topic 7: The Apps Project

This unit encompasses everything you’ve learned into a hands-on project where you’ll look at real-world datasets. You’ll practice using data science tools to analyze and visualize data, and learn how to present your insights. A breakdown of the steps:

  • Approaching a dataset with a business problem in mind

  • Using common data science tools (like Jupyter Notebooks and matplotlib)

  • Downloading and transforming data for analysis

  • Exploring data through the use of plots and statistics

  • Data storytelling

Topic 8: Pre-Work

Before moving on to the technical units of the Core curriculum, you’ll work through exercises on Python, the most popular programming language for data science tasks, and get a crash course in statistics from Khan Academy.

Topic 9: What is Data Science?

In this opening unit of Core, 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 10: 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. In this unit, 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 11: 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 best coding practices in Python

  • Learn Python data types, foundations, and standard libraries

  • Learn Pandas

Topic 12: 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 where you’ll present to stakeholders. Throughout the Core part of the program, you’ll learn each step in depth and then apply what you learned through three robust capstone projects. 

  • 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 13: 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 for your second capstone

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

  • Wrangle data for your second capstone

Topic 14: SQL and Databases

In this unit, 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 using your new-found knowledge of databases. 

  • Learn the landscape of SQL and databases 

  • Write queries in SQL 

  • Work with relational databases in Python 

Topic 15: 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 16: 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 17: 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 18: 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 19: 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 20: 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 21: 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 22: 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 23: 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 Tracker:
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:
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 24: Projects

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

Capstone 1 (Guided Capstone):
You’ll be introduced to 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:
This capstone runs through the steps of the DSM, but you’ll choose your project idea depending on the specialization track you’re enrolled in.

Topic 25: Career Support

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

  • 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

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 three capstone projects. Build an interview-ready portfolio you can show future employers.

While working on the project, you will:

  • Identify a client’s business problem

  • Acquire, wrangle, and explore relevant data

  • Use machine learning to make predictions

  • Create real-world business impact through data storytelling

Past projects from Springboard students
Kristen Colley

Capstone project: Building a Netflix-inspired algorithm for rock climbing recommendations

Frank Fletcher

Capstone project: Computer translation of ASL fingerspelling

Springboard data science grads have achieved life-changing growth. You can too.


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

September 2022


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

September 2022


Enrolled students in the data science bootcamp since 2016.

September 2022

A data science bootcamp with a job guarantee

Invest in yourself with confidence with the Springboard Job Guarantee. If you don’t land a job within six months of graduating, we’ll give you a full refund. Terms apply.

Apply to the next data science bootcamp

The Data Science Foundations to Core bootcamp is a seven-month program. Most students devote 15-20 hours a week to complete the course.

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What are data scientists earning?

These are the average salaries of data scientist in the US.


Data as of November 2022; cross-referenced with Glassdoor, LinkedIn, Indeed, Payscale, Salary.com, BuiltIn, and Comparably.

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