No prerequisites • Remote • Flexible schedule • Job guarantee
There are no prerequisites to start Foundations to Core. You’ll learn key concepts, master Python programming and pass the skills survey, which you can take at any time, before starting the core curriculum. If you're already comfortable with Python, you'll start the core curriculum immediately.
The 500+ hour curriculum includes articles, videos, practice exercises, career-related coursework and 3 capstone projects. The final capstone provides you the option to specialize as a generalist, business insider or continue advancing in machine learning. Studying 20-25 hours per week, you should complete it in 8 months or less.
Learn the fundamental data science and probability concepts before getting hands-on with introductory to advanced Python programming units. Students typically finish in 6-8 weeks part-time, however you can choose to complete them faster. Progress to the core curriculum by passing the technical skills survey, which can be taken at any time.
Data scientists spend a lot of time on data wrangling (i.e., acquiring raw data, cleaning it, and getting it into a format amenable for analysis), usually with the help of semi-automated tools. In this unit, you'll learn the most common tools and workflows in Python that simplify and automate this complicated process.
Data science is not just about the math, the algorithms, and the analysis. It's also about telling a good story. In real life, data scientists don't work in a vacuum—there's always a client, internal or external, waiting to act based on the results of their work.
In this unit, you’ll practice the concepts you've learned so far by creating a story out of a data set. You’ll come up with interesting questions you can ask of your data set and use plotting techniques to reveal insights you can use to create a narrative.
Statistics is the mathematical foundation of data science. Inferential statistics helps data scientists identify trends and characteristics of a data set. Not only are these techniques useful for exploring data and telling a good story, but they pave the way for deeper analysis and predictive modeling.
Machine learning (ML) combines aspects of computer science and statistics to extract useful insights from data. It’s what lets us make useful predictions and recommendations, or automatically find groups and categories within complex data sets. In this unit, you’ll learn the major machine learning algorithms (supervised and unsupervised).
As a data scientist, no matter how many algorithms you design or how much data you crunch, ultimately you’ll be writing software. Some companies expect their data scientists to contribute directly to the code base, while others have engineers around to help translate prototype code to production.
In this unit, you’ll learn how to be a good citizen of the code base, with a focus on writing better code, testing and debugging, and working with production systems.
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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 for 6 months after completing the program.
Data Science Foundations to Core is a 6-8 month program. Most students devote 15-20 hours a week to complete the course.
Every tuition option comes with a job guarantee. If you don’t get a data science job within six months of graduation, you get a full refund. Read the eligibility terms and conditions here
My mentor Danny Wells was an incredible sounding board while I was learning Python, during my capstone ideations, and throughout their execution. The mock interviews forced me to prepare my pitch and served as a great recap of everything I’d learned up to that point. And I can’t possibly overstate the benefit of doing it all remotely while maintaining a job.
Springboard had the four factors that were the most important for me: the ability to learn remotely, the ability to have a flexible schedule so I could still work part-time, dedicated mentors, and Career Services. They did a really good job providing me a structured way to apply for jobs all over the country and ultimately helped me find something that piqued my interest.
Secure your spot now. Spots are limited, and we accept applicants on a first come, first served basis.
The Data Science Foundations to Core course has no prerequisites. It includes foundational units helping you learn key concepts and master Python before progressing to the core Data Science Career Track curriculum.
If you’re already familiar with Python, you won’t be required to complete these units and will start in the core Data Science Career Track. During the enrollment process you’ll complete a technical skills survey which will help us gauge your ideal starting point.
Spots are limited and we accept candidates on a rolling basis. We have a multi-step application process. The first step involves a 5 minute questionnaire to learn about your prior educational and work experience. You’ll then have a brief phone call with one of our Admissions Directors to discuss your experience before taking a technical skills survey to determine your best starting point. You may begin in the foundations curriculum or skip straight to the core course depending on the results.
The course includes a 500 hour core career track curriculum plus 40 hours of foundational units. Students are expected to graduate in 8 months working 15-20 hours per week, however you may choose to dedicate more time and complete sooner.
A career transition into data science is exciting, but involves focused and consistent effort. We are thrilled to have your back in this journey and ask for an equal commitment from you. In order to be eligible for this job guarantee, you should:
Note that while our different specialization tracks prepare you for a career in a specialized field, we cannot guarantee that your first data science position will be in that field.
Read the full eligibility criteria and terms here.