90 zettabytes. That’s how much data market research outfit IDC projected the world would produce in 2019 and 2020, such an unimaginable figure that The Economist chose to gloss it for readers as an equally staggering — and still incomprehensible — 19 trillion DVDs.
Our Guide to Data Science Certificate Programs
In McKinsey’s new report on the data-driven enterprise of 2025, the authors bring this data and its impact into clearer and more actionable focus. “By 2025,” they write,
“[n]early all employees naturally and regularly leverage data to support their work. Rather than defaulting to solving problems by developing lengthy — sometimes multiyear — road maps, they’re empowered to ask how innovative data techniques could resolve challenges in hours, days, or weeks.”
Such demand for data management and data analytics skills means that more and more professionals are looking for educational opportunities to help them gain the upper hand in driving growth both in their companies and in their careers. Increasingly, these professionals are turning to the field of data science and the growing number of certificates on offer that promise to furnish participants with the data skills to meet tomorrow’s challenges.
But what exactly is data science, who are these certificates for, and what does earning one entail? Below, we’ll dive into the discipline and break down what you can expect from a data science certificate program before revealing our favorites.
What is data science?
When the researchers at McKinsey forecast the continuing proliferation of data-driven decision making, they’re touching on two disciplines — data science and data analytics — that overlap sufficiently to create confusion. Understanding the difference between the two is crucial to understanding whether a data science certificate program is right for you.
Data analytics concerns the tools and techniques surrounding the process of data analysis. Often, but not always, data analytics professionals will use existing tools and techniques on existing databases.
Data science, on the other hand, concerns the development and deployment of new tools and techniques and new ways to gather data for analysis. The analytics involved in data science will thus likely be more advanced and will frequently involve machine learning, a form of artificial intelligence. Oftentimes, data analysts and other data analytics professionals will employ methods and tools developed by data scientists.
In general, data analytics certificate programs will require fewer prerequisites and have less advanced curricula. Accordingly, they will be better for those who have less experience with programming and applied mathematics. If you are interested in learning more, check out our guide to data analytics certificate programs.
What skills are taught in data science certificate programs?
The depth of a data science certificate program’s curriculum will vary depending on the length, intensity, and overall purpose of the program, but participants can generally expect to cover the following:
Fundamental computer science skills, including SQL, Python, and/or R programming languages
Statistical modeling, including regression analysis
Machine learning basics, including elementary algorithms and machine learning libraries like PyTorch and Pandas
Advanced techniques such as deep learning and big data mining
Data visualization techniques, often using Tableau
In addition to these skills, many data science certificate programs also include:
A capstone project
One-on-one mentoring and career advice
Data Science Certificate Program vs. Data Science Bootcamp?
The way we see it, all bootcamps are certificate programs — students who successfully complete one are awarded a certificate — but not all certificate programs are bootcamps. In general, you can expect non-bootcamp certificate programs to distinguish themselves by being:
Shorter: While data science bootcamps can entail three or more months of 20+ hours of instruction per week, a non-bootcamp data science certificate program will generally be shorter, if not in the total length of the program, then at least in the amount of time devoted to learning each week
Less intensive: As would be expected with a reduced run-time, a non-bootcamp data science certificate program will often not dive as deeply into the material as a data science bootcamp would.
Less expensive: As would be expected with a reduced run-time and intensity, a non-bootcamp data science certificate program generally won’t be as costly as a bootcamp, with tuition often running in the hundreds and thousands of dollars, not the tens of thousands you would expect with a bootcamp.
Geared to different audiences: While aspiring data scientists might flock to bootcamps hoping mostly to gain the skills necessary to land an entry-level job as a data scientist or data analyst, the motivations of those participating in non-bootcamp certificate programs are more varied, as we will see in the next section. While the curricula of most data science bootcamps are generally quite similar, the curricula of non-bootcamp data science certificate programs vary according to who exactly the target audience or audiences are. Data science certificate programs also might not offer the extensive career services that data science bootcamps offer.
If the intensity of a bootcamp and the prospect of landing an entry-level job warrants the cost for you, head over to our online data science bootcamp guide to see our top picks.
What are some use cases for data science certificate programs?
As audiences differ, so too do the use cases for non-bootcamp data science certificate programs. While McKinsey is likely right that all professionals will engage in data-driven activities by 2025, there is nevertheless significant variation in how they will do so. This, together with differences in where potential participants in their careers, means that non-bootcamp certificate programs are designed with several utilities in mind.
Exploring a career in data science
Due to their relative affordability and lower time commitment, non-bootcamp certificate programs are a great option for those who are data sci-curious but not ready to take the plunge with a more expensive and time-intensive bootcamp or data science master’s program. While a certificate program might not always make you job-ready — it almost certainly won’t let you jump into a senior data scientist role — it will generally offer high-level exposure to the fundamental concepts and skill of data scientist and the day-to-day existence, so you can prototype a potential data science career path in a risk-free environment.
Adding a skill set to boost job performance
Non-bootcamp certificate programs are also great options for those already working as a data analyst, in business intelligence, business analytics, or computer engineering — or even far-off fields like social work, supply chain management, retail — who aren’t able to sacrifice the time to take a bootcamp or go to graduate school, but could benefit by adding data science skills like machine learning and deep learning, applied statistics, and data management.
Managing teams of data scientists
Non-bootcamp data science certificate programs are also a great way for a manager or executive to learn more about the discipline. While some aspects, like capstone projects, might be less relevant, learning the basic vocabulary, processes, and possible outcomes of data science is indispensable for those who need to manage a team of data scientists, expand their company’s data operations, or perform a cost-benefit analysis of doing so.
Our Picks for Best Data Science Certificate Programs
What’s our approach?
Online, you can find countless sites purporting to rank the best data science certificate programs. Often, they’ll support their rankings by laying out the different criteria by which they evaluate and order the programs they feature, separating the “best” from the “rest.”
But while publishing ranking methodologies does lend some objectivity to their program rankings, these sites are missing the bigger issue: ranking in the first place, identifying the “best” programs, presumes that just one kind of student is seeking certification. As the above use cases demonstrate, this is anything but the case.
For our picks, we’ve foregone ranking and focused instead on showcasing a variety of stellar programs that can meet the variety of different needs out there. Our hopes are that interested individuals will be able to find something that works for them.
MIT Professional Education: Applied Data Science Program
With live lectures and mentoring supporting an expansive curriculum, MIT Professional Education’s Applied Data Science Program offers a high-level overview of the key concepts and skills of data science before guiding students through an independent capstone project.
The program has widespread utility, including for those considering a career in data science and machine learning, those who want to spearhead data science and machine learning projects and programs at their organization, and those who wish to harness data science and machine learning to build new products and services.
Length: 12 weeks
Modality: Live, online
Prerequisites: Fundamental knowledge of computer programming and statistics
Foundations for Data Science: Python and Statistics Foundations
Data Analysis & Visualization: Exploratory Data Analysis, Introduction to Unsupervised Learning
Machine Learning: Introduction to Supervised Learning
Deep Learning: Introduction to Neural Networks
Reported Outcomes: None provided
Harvard University: Professional Certificate in Data Science
Harvard University’s Professional Certificate in Data Science, hosted by EdX, offers a self-paced alternative to MIT’s program that is much less expensive and potentially requires a lower time commitment.
For those comfortable learning on their own, the program can provide a basic grounding in the R programming environment as well as an understanding of key statistics and machine learning concepts and the skills to put the theory into practice.
Length: ~144–216 hours of study
Modality: Self-paced, online
R Programming Basics
Data Analysis: Inference and Modeling
Statistical Modeling: Linear Regression
Reported Outcomes: None provided
Cornell Bowers College of Computing and Information Science: Data Science Essentials
While on the pricey side for the amount of instruction provided and modality, Cornell’s Data Science Essentials boasts small, instructor-led classes offering a curriculum designed by Cornell professors and lecturers. Open to anyone interested in learning about the discipline — data scientists, consultants, business executives, etc. — the program provides a grounding in using R to explore and analyze data, interpreting results, as well as data cleaning with Tidyverse open-source software packages.
Length: 4 2-week courses, with an estimated 5-8 hours of study required per week
Modality: Self-paced, online
Exploring Data Sets with R
Summarizing and Visualizing Data
Measuring Relationships and Uncertainty
Data Cleaning with the Tidyverse
Reported Outcomes: None provided
IBM Data Science Professional Certificate
IBM’s Data Science Professional Certificate, hosted by Coursera, offers an extremely low-cost option to quickly assess whether data science is right for you or begin building out a data science skill set. Participants receive access to IBM’s talent network and complete a capstone project to begin filling their portfolios.
While Cornell and Harvard’s programs focus on R, IBM’s program teaches both SQL and Python. You can learn more about the differences between these in our data science programming languages guide.
Length: 11 months with 4 hours of study per week
Modality: Self-paced, online
Data Science Methodology
Python for Data Science, AI & Development
Data Visualization with Python
Machine Learning with Python
Applied Data Science Capstone
26% of certificate graduates report starting a new career.
4.6/5 star rating on Coursera
Cost: Coursera membership of $39/month
University of California – Berkeley Extension: Certificate Program in Data Science
Berkeley’s Certificate Program in Data Science is the most expensive program on our list, but offers a rigor, legitimacy, and flexibility we think might be appealing to some aspiring data scientists. The curriculum is laid out in 5 courses, one core course, one programming course, one machine learning course, and two electives, and students are required to achieve a 2.5 GPA to receive a certificate. Students with a 4.0 GPA receive the certificate with distinction.
In choosing courses to meet these requirements, students have a number of options to craft a data science course of study that will work best for their needs. Students also have access to Berkeley Global Career Services to help them land a position after they finish the program.
Length: 5 semester-length courses, total length varies
Modality: Option vary by course, but include live online, self-paced online, hybrid online, and in-person
Equivalent of 1 semester of college-level statistics
1 programming language
Introduction to Big Data
Data Science Principles and Practice Using Python
Introduction to Databases
Machine Learning and Deep Learning with Spark
Reported Outcomes: None reported
Cost: Estimated at $5,100
In this guide, we’ve given you everything you need to know to begin researching and making a decision on a data science certificate program that will work for you — plus some recommendations to get you started. If you’re interested in learning more about data science but not sure that a certificate program is right for you, check out our guides to the other great educational paths out there:
If you’re interested instead in learning more about data science careers, see our articles on the typical data science career path, the difference between data science and data engineering, and how to get a job in data science.