Build job-ready skills with 28 mini-projects and 3 capstones and an advanced specialization project that suits your career goals.
Our Data Science graduates have been hired by:
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 skills with 28 mini-projects and 3 capstones and an advanced specialization project that suits your career goals.
Work 1-on-1 with an expert mentor, industry career coach and student advisor when you need guidance from course start to new job.
We believe in you and our program, so if you don't land a data science job or you'll receive a full refund.
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.
We’ve helped thousands of students learn skills and land jobs. It’s why we’ve been consistently recognized as an industry leader.
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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
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.
Boost your job search. Improve your ideas. Stand out from the crowd. All with AI as your collaboration partner. Our NEW AI interactive learning series gives you access to experts putting AI to work for their businesses, so you can too. All free when you become a student.
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
Capstone project: Building a Netflix-inspired algorithm for rock climbers
Capstone project: Computer translation of ASL fingerspelling
Mentor: Ryan Rosario
Machine Learning Engineer
Mentor: Sameera Poduri
Principal Data Scientist
Mentor: Eric Rynerson
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.
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:
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
Average salary increase of data science students who provided pre- and post-course salaries.
Of job-qualified individuals who reported an offer, received it within 12 months of graduation.
Enrolled students in the data science bootcamp since 2016.
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."
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.
This data science bootcamp is a six-month program for students devoting 15-20 hours per week or dedicate more time and land your new job faster.
Spots are limited, and we accept qualified applicants on a first-come, first-served basis. Start your free application. It takes just 5 minutes to complete.