IBM Data Science Certificate: A U.S. News Guide

Data science is changing the way organizations approach marketing, e-commerce, strategic planning and more. At the same time, there aren’t enough people trained to do the job. Only recently have universities offered data science degrees. That’s why people who already work in data science or are interested in entering the field may benefit from proof of expertise that comes with the IBM Data Science Professional Certificate.

The IBM Data Science Professional Certificate isn’t intended to make you an expert in data science. Instead, it helps those with little to no background gain a foundation and build a career. “It targets people without really a lot of prerequisites,” says Ana Echeverri, learning lead in IBM’s Data and AI Expert Labs. “It’s designed to give people the foundational blocks to go into data sources.”

Taking the courses and getting certification can easily require at least 10 months or more of part-time study. So is an IBM data science certificate worth it? Yes, if this is a field you want or need to enter.

“The coursework helped me get an extra edge,” says Manish Shukla, who came to the U.S. in 2018 to pursue a master’s degree from the University of Texas at Dallas. He came across the IBM Data Science Professional Certificate and pursued it as well.

“It helped me get work with real-world data and tools,” he says. “It helped me get a good internship and then converting to full-time (employment).” Shukla is now a data scientist and technical advisor at management consulting firm Palladium.

While the certification gives you the fundamentals, it’s not the same as being a data science expert. “While the IBM Data Science Professional Certificate can certainly help toward roles like these, I should stress that it’s not the key to the door by itself; particularly in terms of data science positions, which are incredibly complex and require multidisciplinary skills,” says Jack Zmudzinski, a senior associate at software development company Future Processing who has several employees with the certification.

The necessary nine courses – including quizzes, hands-on assignments and projects – are available through edX ($411 for the set of nine) or Coursera ($39 a month with no additional charges).

No prior programming knowledge is necessary, although some high school math, including basic probability and statistics, is useful.

See approximate hours and coursework below based on Coursera’s program.

This course explains what data science is and what data scientists do. Practitioners discuss the necessary skills, what the work entails, how data science fits into a business, and the tools and algorithms in daily use. It requires 1.6 hours of video, 11 readings, six practice exercises and seven quizzes for a total of about 10 hours over three weeks.

This course is an overview of tools that data scientists commonly use, whether open source or commercial proprietary ones from IBM. There are introductions to commonly used programming languages in data analysis and a project using Jupyter, an open-source product for interactive data science and scientific computing. It requires 3.7 hours of video, one reading, eight practice quizzes, three graded quizzes and one project for about 22 hours over four weeks.

This course focuses on a single consistent methodology to use in data science problems. This comprises the major steps in practicing data science, including problem formulation, data collection, data analysis, model building, model deployment and incorporation of feedback from the model’s use. It requires 0.9 hours of video, eight readings, five practice exercises and 10 quizzes for a total of 10 hours over three weeks.

Python is a popular high-level programming language. The course gives a foundation to expand programming knowledge into more advanced techniques and to eventually move into machine learning. Exercises using Python include writing basic programs, working with data structures and data analysis, and addressing real-world problems. It requires 1.8 hours of video, two readings, 15 practice exercises, five quizzes and one project for about 28 hours over five weeks.

Basic to manipulating, analyzing and using data is its storage in databases. SQL is a widely employed programming language for most popular databases. This course provides insight to and practice in using SQL in data science. It starts with basic SQL, goes into advanced methods, and finally combines SQL with Python for programs that can access and analyze data. An assignment in week four involves working with real data about the city of Chicago. It requires 2.1 hours of video, two readings, seven practice exercises and 10 quizzes for a total of 13 hours over four weeks.

This course is about employing Python to write programs that can use data. The lessons include data preparation for analysis, simple statistical analysis, basic data visualizations and using statistical means to predict future trends from existing historical datasets. It requires 1.8 hours of video, four readings, 12 quizzes and 25 practice exercises for a total of 25 hours over seven weeks.

Data visualization is critical in analysis and data science as graphical representation makes meaningful data more obvious than pages of numbers, especially with massive amounts of data. But even in smaller-scale groups of data, visualization is useful. Coursework includes use of such visualization programming tools as Matplotlib and Folium to create many types of graphs and also an introduction of geospatial data to place data markers on maps. It requires 1.1 hours of video, seven readings, five practice exercises and 11 quizzes for a total of 18 hours over three weeks.

The use of Python in machine learning – a critical technology in data analysis across all industries – incorporates everything from the previous courses. In exercises, resulting programs intake sample data and use it to make decisions without specific code for each one. Beyond an overview of machine learning topics, the work adds skills and projects that can demonstrate the student’s abilities to others. It requires 3.9 hours of video, five practice exercises, two readings and one quiz for a total of 22 hours over six weeks.

The work in this course includes making API calls to data sources of geospatial data like Foursquare, pulling data from webpages and by parsing HTML code, and then manipulating the data for analysis purposes. The capstone project involves defining a problem, discussing the data needed to solve it and then writing the code to obtain the data, analyze it and display the results. It requires 0.7 hours of video, two practice exercises, three readings, one quiz and a capstone project for a total of 47 hours over five weeks.

Before starting the IBM Data Science Professional Certificate program, be sure it’s right for you.

“Nine out of 10 people are not really interested in becoming data scientists,” Echeverri says. “They hear there are jobs; that it’s cool.” But gaining certification requires significant learning just to get the groundwork that allows for further study and development, she says.

When people ask her about data science, she often suggests that they instead consider careers in data analytics and data visualization. Both are necessary in industry, government, nonprofits and academia but may require less intense work, she says.

One way to better understand whether data science is really something you might enjoy is to take the first course in the IBM series that introduces data science, either by auditing for free through edX or using Coursera’s seven-day free trial. You can also watch videos from data scientists like Ken Jee, who explains what the daily activities are, or Krish Naik, who has a similar video.

To prepare for the IBM Data Science Professional Certificate program you don’t need coding skills, but high school math is a must. Echeverri suggests knowing basic probability, descriptive statistics, inferential statistics and maybe some linear algebra because “it makes the difference between a data scientist that is dangerous and one that is not dangerous.”

Coursera says that for the last few courses of the series, knowledge of calculus is also helpful. Coursera and edX offer many courses in statistics, linear algebra and calculus. Communication and presentation skills are also helpful because ultimately, data scientists find stories in data and tell them to others.

Also consider cost and time. The set of courses costs around $400 or more. Will you foot the bill or would your employer pay, especially if your role expands to include some data science activity?

You’ll also need at least a few hours a week to study and prepare. Coursera and edX estimate students need between three to five hours a week for 10 to 13 months for all the required work. Full-time study can reduce the timetable by many months. Courses also vary in difficulty and demands, and some might take more than the suggested time. However, the courses are self-paced for flexibility.

If your career path involves data analysis with an eye toward machine learning, or if data science interests you, this certificate may make sense for you.

There is significant demand for such expertise. University data science training at the undergraduate and graduate levels is still in its early stages but ramping up to meet demand. Since formal education is still playing catch-up, certificates can help document expertise.

A 2020 survey from job site Indeed of 22 job seekers with the certification showed that 73% thought the certification helped their careers, 32% felt that having it helped them get a job and 27% reported that having it aided them in making more money.

Data scientists have been in high demand for the last few years and continue to be. For several years, Glassdoor has listed data scientist as one of the top three best jobs in America due to the growing need for people with these skills.

LinkedIn co-founder Allen Blue told Knowledge@Wharton, a publication of the Wharton School at the University of Pennsylvania, that almost all data scientists are employed because demand is greater than supply. The past three years saw “massive growth” in data science-based jobs across multiple sectors, he said.

The certificate can help you land a job because there aren’t many credentials that provide proof of this expertise.

Zmudzinski calls the IBM Data Science Professional Certificate “a really useful thing to have, particularly if you’re looking to gain a role as a data analyst, data scientist or machine learning engineer.”

But recognize that an emphasis on IBM’s proprietary tools will have less relevance if you’re applying for a position that will require another company’s products.

For more information about data science careers, consider researching these organizations: