Data science is in high demand. People go to school to learn it. They get degrees in it. They leave their jobs to do it. But can a person learn data science on their own?
Here’s what we’ll cover:
Want to learn data science, but don’t have the time for a multi-year, full-time degree program?
Luckily, there are many ways to prepare for a data science career. Although you can self-study using free online resources (including Springboard’s data analysis curriculum!), many aspiring data scientists who attempt to learn on their own experience challenges finding jobs, as they don’t have any accreditation or certification to back up their skillset and lack industry contacts.
Data science courses and bootcamps are a happy medium for those looking for both independence and support, as they provide an experienced teacher and cohort setting to offer feedback and accelerate the learning curve.
Data science courses are standalone offerings focused on specific topics, like “Introduction to Data Science” or “Data Science and Machine Learning Essentials,” and languages such as R or Python.
Here are some of the top recommended data science courses from Coursera, Udacity, Udemy, and edX, with Harvard, MIT, and University of Michigan professors. These courses cover the entire data analytics process, popular programming and open-source tools, and machine learning basics.
Data science bootcamps are short-term programs designed to get you ready for the interview process and may include other benefits beyond coursework, like mentorship and recruiting assistance. Bootcamps can be virtual, in-person, or a hybrid model, with both part-time and full-time options. They tend to go over all the fundamentals in a condensed time period and cost anywhere from $1,000-$10,000.
When choosing a bootcamp, it’s important to consider the instructor-student ratio, time commitment (length and number of hours per week), cost, specialty areas, and how much time is spent on individual versus group work. Hands-on projects are very useful for reinforcing classroom teaching and can enhance your online portfolio when you’re applying to jobs.
Data science courses and bootcamps normally cover how to import, clean, and transform data sets; how to model data and find trends using techniques like linear regression and logistic regression; how to use databases; and how to apply machine learning and deep learning concepts, like supervised and unsupervised learning, regression, classification, natural language processing, decision trees, k-means, time series, and neural networks.
After completing a data science program (course or bootcamp), you may still want to deepen your understanding, adding exposure to ETL and workflow automation, SQL practice, and engineering and software development lifecycle management. You should also pick up side projects, through freelancing on platforms like Fiverr and Upwork, working a part-time job, or perhaps volunteering for a non-profit organization with data challenges. But that’s part of the fun of a career as an entry-level data scientist—you are constantly learning new skills!
Some data science programs have prerequisites, like a bachelor’s degree in a STEM field, computer science skills, a few years of experience in a technical profession or high school mathematics (calculus, statistics, probability, linear algebra, etc.), while others accept applicants with no prior background. Make sure to read the expectations carefully before signing up.
Data science bootcamps are 3 to 6-month intensive programs that prepare you for entry-level roles by solidifying your foundation in data science skills and tools for data analysis. In order to learn data science in a short timeframe, you will probably want to dedicate 6-7 hours per day to studying and coding. Courses generally last 2-6 months and certifications combine multiple courses along similar themes.
If you haven’t taken any data science coursework yet, start with an introductory course. You will first want to learn database and programming languages, like SQL, Python, and R (Python is currently the most popular data science language with libraries like pandas, which supply easy-to-use data structures!), and then how to manage data pipelines from start to finish. This includes data wrangling (acquiring, cleaning, and formatting datasets), using inferential statistics to explore trends and characteristics of the data (hypothesis testing, A/B tests, correlation, and regression), and turning data-driven insights into decisions that can influence business and product outcomes through graphing and plotting techniques (data visualization).
If you know the technical languages and skills required by companies on your target list, you can tailor your course or program selection to set yourself apart as a strong candidate. This article provides an overview of the key hard and soft skills companies are looking for in data scientist resumes.
Like any investment, not all data science bootcamps are created equally. To select a great bootcamp, make sure it has a strong community, as this serves a similar function to the alumni network of many top universities. Some programs also offer career services, interview prep, and networking sessions with industry professionals.
Data science bootcamps are a fast-paced, well-structured way to focus on achieving your learning goals and often do not have as competitive of an admissions cycle as a master's degree in data science. On the down-side, they may lack some of the internship opportunities accompanying formal degrees and you may need to supplement your education with other events, meetups, lectures, workshops, and books.
In order to land a dream data science position, you will want to come in with strong existing credentials (work experience and education), as jobs at top companies are still highly competitive. If you have the opportunity to pivot into a more data science-oriented track within your existing company, that can allow you to gain real-world experience before applying externally.
Is data science the right career for you?
Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.
Check out Springboard’s Data Science Career Track to see if you qualify.
Not quite ready to dive into a data science bootcamp?
Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.
Download our guide to data science jobs
Packed with insight from industry experts, this updated 60-page guide will teach you what you need to know to start your data science career.
Ready to learn more?
Browse our Career Tracks and find the perfect fit