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Dr. Paul Laskowski is an adjunct assistant professor at the University of California, Berkeley, where he teaches courses in data science. He earned his AB in applied mathematics from Harvard University and his PhD in information management and systems from UC Berkeley.
Dr. Laskowski studies the interaction between computer networks and economic incentives. To understand how modern network industries differ from more traditional ones, he creates game-theoretic modeling frameworks that incorporate network topology as a critical input—he is especially interested in applying these to contemporary policy debates, including digital content distribution, future internet architectures, and net neutrality.
Before coming to Berkeley, Dr. Laskowski was the lead developer for Project INDIGO, which stands for Information Diffusion and Growth. This project was started by Marshall Van Alstyne of the University of Michigan’s School of Information as a tool for the development and communication of information models. In recent years, the tool has grown into a general development environment for agent-based simulations.
OnlineEngineeringPrograms.com: What is something you wish the public understood about data science?
Dr. Laskowski: Part of data science is working with numbers, but an even bigger part is interacting with stakeholders in different parts of an organization. Data scientists can work with any data, but they need the right kind of data to be effective. Ideally, they should be involved right at the point when data is being generated—for example, at the planning of a new product launch.
A data scientist can answer many types of questions, but those questions must first be transformed into precise data-science questions. It can take many rounds of back-and-forth with a stakeholder before a question can be properly answered, and the process of refining the question can itself help a stakeholder to clarify their thinking.
Finally, systems that process data have ethical implications, touching on issues like privacy, fairness, and misinformation. Something we emphasize at Berkeley is that data scientists need to be well-versed in these areas. To perform their job responsibly, data scientists need clear lines of communication to decision makers who can affect how data is collected and utilized.
OnlineEngineeringPrograms.com: What advice would you give to aspiring data science students?
Dr. Laskowski: A lot of students are attracted to data science because they see cutting-edge advances in image processing, language models, or other rapidly developing areas. These are certainly exciting applications to work on, but I find that students often rush into picking up a textbook about, say, deep network architectures.
If you are preparing for advanced data science study, the best thing to do is focus on the fundamentals. From my perspective, that includes becoming a fluent programmer and building a strong intuition for calculus and linear algebra. It’s not important that you can crank through integrals (we have computers for that), but the ideas within calculus and linear algebra are critical to understanding modern data science models. If you skip over those fundamentals, your knowledge of models will tend to be brittle and you won’t know what to do in new situations. On the other hand, if you slow down and build a solid foundation first, you will be well prepared to learn new techniques whenever you need them.
OnlineEngineeringPrograms.com: What does the future of data science look like to you?
Dr. Laskowski: New technologies are coming online that will dramatically change the way we live our lives. They can help us solve some of the biggest challenges facing humanity, and they also carry significant risks. It’s critical that data scientists rise to this challenge by developing a vocabulary to discuss how technology interacts with human values. This will enable them to become responsible leaders who will recognize when data practices become destructive and formulate arguments to change them.
While admissions processes vary by institution and degree desired, here is a compendium of some common application materials to gain entry into a distance-based data science program:
Please note that a candidate interview or submission of a writing sample may also be required.
Thomas Edison State University
Thomas Edison State University based in Trenton, NJ partnered with the Institute of Statistics Education to offer an online bachelor of science (BS) in data science and analytics. This program is 100 percent online with no expected campus visits.
The major admission requirements to the program include a high school degree, GED, or a secondary home school diploma that meets the requirements of the applicant's state of residency, a completed online application, and official copies of transcripts (if any). Applicants must be at least 20 years of age or older.
Comprising 120 credits, the program includes instruction in machine learning tools; neural nets & regression; dimension reduction, clustering & associative rules; forecasting analytics; interactive data visualization; and introduction to social network analysis. TESU also has several electives available to students in this program, including introduction to database queries (SQL and R); regression analysis; introduction to Python programming for analytics and financial risk modeling, among others.
Graduates of the program will be able to utilize key technologies in data analytics and science, including machine learning, data mining, visualization techniques, and predictive modeling & statistics, apply knowledge of quantitative modeling techniques and statistical data analysis to solve real-world problems.
Southern New Hampshire University
The Southern New Hampshire University of Manchester provides an online bachelor of science in data analytics, a field closely allied with data science. Students in this program learn to solve real-world analytical problems and master professional analytical tools. Combining facets of information technology, mathematics, and business using data mining, optimization, and simulation, the program helps students in succeeding in all aspects of a data analysis role.
Students get instruction in areas such as introduction to structured database environments, the role of data analysis in organizations, gathering requirements and collecting data, data policy & decision making, emerging technologies and big data, data analysis techniques, applied data analysis; and elective courses such as public speaking and presentation skills, project management, and public relations.
At the end of this data analytics degree online bachelor's program, graduates will be able to explore a wide range of career options across private, nonprofit, and public sectors, including business analysts, data analysts, management analysts, and operations analysts.
This 100 percent online program also offers a concentration in project management.
Arizona State University’s W.P. Carey School of Business offers a completely online BS in business data analytics program preparing students with the requisite skills, knowledge, and experience required for creating and managing big data initiatives.
To get accepted into the program, applicants must have an SAT Reasoning score of 1230 or an ACT composite score of 25 or a high school class ranking in the top 8 percent or an overall high school GPA of 3.4 in competency courses, a 3.0 cumulative GPA for transfer students, official or unofficial transcripts of all coursework, among others.
Consisting of 120 credits, the program includes courses such as introduction to information systems, business data mining, introduction to business data analytics, big data analytics in business, enterprise analytics, and business data warehouses & dimensional modeling.
Graduates of this program will be well-equipped to take up positions such as business intelligence analysts, computer network administrators, computer network analysts, data management specialists, and database administrators.
Washington State University’s 100 percent online BS in data analytics program prepares students for a rewarding and successful career in this emerging field of big data. Students in this program learn the techniques and tools needed for collecting, managing, exploring, and analyzing large, complex datasets.
Admission requirements include official transcripts, a GED transcript (if any), official SAT or ACT scores, and a current resume. Students must also be proficient in math.
The program comprises 120 credits including courses such as data analytics systems & algorithms, introduction to data analytics, calculus, program design & development, data structures, data analytics ethics, probability & statistics, and introductory linear algebra.
The program opens up various employment opportunities for students in areas such as manufacturing, eCommerce, transport, education, healthcare, government, insurance, and environmental management.
Harvard University’s Extension School offers an online graduate certificate in data science helping students learn the latest trends in data analysis, research, and innovation with hands-on learning and experienced instructors.
The program consists of four courses, taking a year-and-a-half to complete. In addition to a required course in data science, students choose between electives such as advanced scientific computing (stochastic methods for data analysis, inference & optimization); big data analytics; database systems; and big data in healthcare applications.
Students through this program learn to leverage their knowledge of key areas, such as game theory, statistical quality control, exponential smoothing, seasonally adjusted trend analysis, or data visualization, and master key facets of data investigation.
Columbia University provides an online certificate of professional studies in data science preparing students to change their career paths or expand their career prospects by developing foundational data science skills. Credits earned in this certificate can be applied towards the MS in data science program.
The program requires completion of a minimum of 12 credits of coursework in areas such as algorithms for data science; probability & statistics; machine learning for data science; and exploratory data analysis & visualization.
Admission requirements include an undergraduate degree, a grade point average of 3.0, prior quantitative coursework, prior introductory to computer programming coursework, official transcripts, three letters of recommendation, and a personal-professional statement.
Indiana University’s graduate certificate in data science can be completed entirely online allowing students the flexibility to build a schedule of courses that suits their lifestyle. The program helps students in acquiring new skills in topics such as cloud computing, data analysis, health & medicine, data mining, and statistics.
Made up of 12 credits, the program includes courses such as elements of artificial intelligence, big data applications, engineering cloud computing, and security in networked systems.
Credits earned in this certificate program can be applied towards the online master of science in the data science program.
The University of New Hampshire offers an online graduate certificate in data science exposing students to the latest cutting-edge coding languages, algorithms, and visualization tools through applied case studies and online instruction. Ideal for working professionals, the program allows students to continue working while they learn on their schedule.
The major admission requirements to the program include electronic and paper transcripts, two letters of recommendation, a personal statement, a current resume, and TOEFL, IELTS, or equivalent examination scores for international students.
Comprising 12 credits, the program includes courses such as introduction to applied analytic statistics, programming for data science, data architecture, and data mining and predictive modeling.
Graduates of the program will be ready to take up roles such as business analysts, computer scientists, data analysts, data scientists, database administrators, financial analysts, and statisticians, among many other such roles.
The Johns Hopkins Whiting School of Engineering based in Elkridge, MD offers a fully online master of science (MS) in data science preparing students for a range of successful careers in data science. The faculty of the program includes data scientists and practicing engineers.
For admission to the program, students need to submit a completed online application form, a bachelor’s degree from a regionally accredited university or college, a minimum GPA of 3.0, and official transcripts.
Made up of ten courses, the curriculum includes courses in data science, data structures, data visualization, algorithms for data science, introduction to machine learning, computational statistics, statistical models and regression, and principles of database systems.
Students through this program will be able to use computer science and mathematics for decoding large sets of data, discover correlations between different data sets, and create techniques and models for solving real-world issues.
A master of science in data science program offered by Maryville University can be completed entirely online without requiring any cam visits. Students in this program can build their proficiency in areas like coding, machine learning, big data, deep learning, and data mining. The faculty of the program includes data science experts.
To get accepted into the program, applicants must have a bachelor's degree from a regionally accredited institution, a minimum grade point average of 3.0, official transcripts from all institutions previously attended, a personal statement, a current resume, and TOEFL scores for international students.
The program consists of 36 credits including courses such as data visualization, experimental design, deep learning, big data analytics, predictive modeling, machine learning, and math modeling among others.
At the end of the program, students will be ready to analyze large data sets, identify data set patterns, and use data for predicting behavior and future outcomes.
Graduates will be prepared to take up roles such as data scientist, financial analyst, data analyst, and computer systems analyst.
Southern Methodist University’s online master of science in data science program is ideal for students who wish to pursue a career in data science and are looking to gain advanced skills in management, mining of data, and analysis. Students will also have the option to customize their curriculum by choosing an area of specialization that aligns with their academic goals: machine learning specialization and business analytics specialization.
The major admission requirements to the program include a bachelor's degree, a basic understanding of programming languages, quantitative skills demonstrated through college-level coursework or work experience, and TOEFL scores for students whose native language is not English.
Made up of 33.5 credits, the program includes courses such as statistical foundations for data science, file organizations & database management, applied statistics, data & network security, and data mining. Students in this program will master the tools and concepts required for effectively mining, managing, and analyzing unstructured data and for clearly communicating solutions and results to inform strategy in organizations.
University of California, Berkeley
The University of California, Berkeley has a 27-unit online master of information and data science (MIDS) program providing multidisciplinary instruction and preparing students to be leaders in this field. While all courses can be completed online, students will be required to attend one 3 to 4-day immersion session on the Berkeley campus.
To be eligible for the program, students must have a bachelor's degree from an accredited institute, a grade point average of 3.0, a statement of purpose, GRE or GMAT scores, letters of recommendation, TOEFL scores for international students, and considerable work experience.
The curriculum includes topics such as introduction to data science programming, statistics for data science, research design and application for data and analysis, fundamentals of data engineering, machine learning at scale, data visualization, and experiments and causal inference.
At the end of the program, graduates can pursue opportunities such as systems engineer, business data analyst, data analyst, data architect, solutions architect, and data scientist.
With technology rapidly changing the field of data science, there’s no surefire career path to becoming a data scientist, but those who have success in this field share some common attributes. For example, effective data scientists are lifelong learners, invested in keeping up with the ever-evolving platforms, programming languages, and machine-learning techniques which help to process large datasets.
While a majority of data scientists have at least a bachelor’s degree, many top-level employers prefer those who have completed a graduate education in the field. Above all, these professionals must have facility with various programming languages; a solid grasp of applied mathematics and statistics; an ability to learn quickly; and personality traits such as grit and curiosity.
More specifically, there are a number of skills which help data scientists to thrive. Some of the qualifications include:
Additionally, some employers ask for specific certifications among data science job applicants. According to CIO.com (2024), some of the top data science certifications include:
Overall, the field of data science can prove a rewarding career, allowing a person to provide high-level insight to organizations, businesses, and governments across the world. While the specific skills required are dynamic and continually evolving, those with inquisitive minds and varied interests are the most likely to succeed in this lucrative field.
These numbers represent data from the U.S. Bureau of Labor Statistics from May 2023—the latest figures available as of May 2024.
Data Scientists
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineeri...
After graduating from UC Berkeley, Jocelyn traveled the world for five years as an English teacher and freelance writer. After stints in England, Japan, and Brazil, she settled in San Francisco and worked as a managing editor for a tech company. She’s recently become interested in the underrepr...
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