Data Science


The complete admissions process consists of an online application, a take home assessment and two technical interviews with faculty. For the Data Science program, you will complete our take-home challenge to assess your quantitative and programming skills. This assessment focuses on Python, SQL, and statistical data analysis. Two technical interviews will follow. The first evaluates your experience with programming in Python. The second covers topics in probability, statistics, experiment design, and basic modeling. Each step in the admissions process is contingent on passing the previous one.
The first step of the process is a take-home challenge. We suggest you return it within 7 days, but there is no official deadline. After you return the take home, the 2 interviews can take anywhere from 1-3 weeks, depending on interview schedules. After each step in the admissions process, you can expect to hear back from Admissions regarding the next step within 7 business days.
For the the Data Science program, most of our successful applicants have backgrounds in analysis, software engineering, and/or quantitative PhDs. You should be proficient in Python, familiar with data analysis tools and practices, and know college-level math, statistics, and probability. Not sure what “proficient” or “familiar” mean? Try these exercises; if you can complete them comfortably, you are ready to apply. We highly suggest you share your background and experience with an Enrollment Counselor so they can help you find the program that will best fit your goals.
In the admissions process, we look for backgrounds in a quantitative discipline including foundational statistics, probability, linear algebra, and mathematics. We expect a strong working knowledge of at least one programming language (we use Python). The skills we screen for in the admissions are highly learnable for anyone with a background in engineering of any kind, data analysis, or the quantitative sciences.
Common backgrounds include engineers, scientists, and analysts with 0-15+ years of industry experience. Our alumni are diverse — they include former researchers, mechanical and electrical engineers, consultants, and professional poker players.
The Data Science Immersive is an intensive program that runs 12 weeks (full-time) and teaches both the hard and soft skills needed for a career in the data science industry. Students complete 8 weeks of intense structured curriculum, solving problems with real data sets and picking up the practical skills that hiring partners care about. The course also includes 2 weeks to complete the capstone projects and 2 weeks for interview coaching and introductions to partner companies.
The Data Science Immersive at Galvanize is a hands-on, project-based program that trains participants to solve data science problems. Through working with messy, real-world data sets, students gain experience across the data science stack — data munging, exploration, modeling, validation, visualization, and communication. The program is based in Python, and utilizes tools and libraries including pandas, matplotlib, numpy, pymc, and scikit-learn. As data sets get larger, we introduce tools like SQL and NoSQL. Alumni tell us that they’ve never learned more information in such a short amount of time, and many form lasting relationships with friends in the program and among our alumni network.
A typical day begins at 9:30am with a short quiz to test your knowledge of previous concepts introduced in the course. Instruction begins at 10am with a lecture introducing the day’s topic and theory. For the rest of the morning you will be working through a hands-on exercise where concepts from lecture will be applied. The afternoon follows a similar pattern to the morning by beginning with a lecture that dives deeper into the day’s topic. The rest of the day you will be pair programming with another student to complete a longer and more in-depth exercise.

Occasionally there will be a guest speaker or lecturer presenting during lunch or at the end of the day to provide insight into what a day in the life of a practicing data scientist is like or to cover a specialized topic not covered in the standard curriculum.

For Data Science and Data Engineering, Mac Mini work stations will be provided in the classroom for pairing exercises. They are also available to use for individual assignments, although most students use their own machines for their capstone project. Students do not keep the Mac Minis at the end of the program. Laptops are not required for Data Science or Data Engineering but should you plan on purchasing a new one, we suggest using a Macbook.
By the end of the program you can be expected to have a solid grasp on a breadth of machine learning and statistical theory. You’ll also have built numerous data science applications and will know enough to be a successful data scientist in industry. The program culminates with a personal capstone project focused on synthesizing the core concepts learned throughout the course which you will present at our hiring day to companies looking to data scientists.
Absolutely! We encourage you to reapply and continue to study and prepare in the meantime. Your Admissions Officer will provide you feedback and study materials. It’s a good idea to also attend Galvanize events and workshops to brush up on your skills.