Data Science - Minor (2024)

The Minor in Data Science is open to ALL students! The Data Science Minor is designed to equip students to become proficient in the principles of computation, statistical inference, and data management and their applications in a specific domain/field.

Its interdisciplinary and visionary curricula allow flexibility and accessibility for any student who wants to enhance their academic competency and employability in data-informed careers.

The minor consists of 6 courses plus a 1-credit capstone. After completing the three data science foundational courses the minor offers a choice of four tracks to allow students to differentiate and complement their career pathway, thus accommodating a broad range of student goals and backgrounds.

Students must maintain a GPA of 2.0 in the courses applied to the minor. No courses with a grade of D can be counted toward the minor.

Students are advised to check with their major department for any restrictions on counting courses for both the Data Science minor and their major.

Course Requirements

Students are required to complete six courses and a mini capstone. Students must maintain a GPA of 2.0 in the courses applied to the minor. No courses with a D can be counted toward the minor.

+ - Data Literacy Click to collapse

  • Data 101: Data Literacy (01:198:142/01:960:142) must be taken, (no waivers)

+ - Statistical Inference Click to collapse

  • Statistical Inference for Data Science (01:960:291)
  • {The 01:960:291. Statistical Inference for Data Science requirement has been expanded to include any of the following: Statistics II (01:960:212), OR 01:960:384 Intermediate Statistical Analysis (Formerly 960:380), OR 33:136:385 Statistical Methods in Business.}

+ - Data Management Courses Click to collapse

  • Data Management: choose one of the following:
    • Data Management for data science (01:198:210), or
    • Data Management and wrangling with R (01:960:295), or
    • Fundamentals of data curation and management (04:547:221)

+ - Domain Courses Click to collapse

Domain classes (select one and check department for prerequisites)

RU-NB SchoolDepartmentCourse # TitleCapstone
SAS (01)COMPUTER SCIENCE (198)439 Introduction to Data Sciencedefault, 198:310
SAS (01)ECONOMICS (220)322 Econometrics01:220:323, to be taken after, not concurrent with 322
SAS (01)ENGLISH (359)207 Data and Culturedefault, 198:310
SAS (01)GENETICS (447)303 Computational Genetics for Big Datadefault, 198:310
SAS (01)GEOGRAPHY (450)320 Spatial Data Analysisdefault, 198:310
SAS (01)GEOGRAPHY (450)321 Geographic Information Systemsdefault, 198:310
SAS (01)GEOGRAPHY (450)330 Geographical Research Methodsdefault, 198:310
SAS (01)PHYSICS (750)345 Computational Astrophysicsdefault, 198:310
SAS (01)POLITICAL SCIENCE (790)391 Data Science for Political Sciencedefault, 198:310
SAS (01)SOCIOLOGY (920)360 Computational Social Sciencedefault, 198:310
SAS (01)STATISTICS (960)365 Bayesian Data Analysisdefault, 198:310
SAS (01)STATISTICS (960)463 Regression Methodsdefault, 198:310
SAS (01)STATISTICS (960)486 Applied Statistical Learningdefault, 198:310
SCI (04)DIGITAL COMMUNICATION, INFORMATION, AND MEDIA (189)220 Data in Contextdefault, 198:310
SCI (04)INFORMATION TECHNOLOGY AND INFORMATICS (547)321 Information Visualization

default, 198:310

SEBS (11)BIOTECHNOLOGY (126)486 Functional Genomicsdefault, 198:310
SOE (14)ELECTRICAL AND COMPUTER ENGINEERING (332)443 Machine Learning for Engineersdefault, 198:310

+ - Capstone Click to collapse

Capstone Courses (1 course):

  • Data Science Capstone Project (01:198:310) - default, or
  • Data Science and Econometrics (01:220:323)

+ - Tracks Click to collapse

Choose from one of the following four tracks.

Track 1.

This track targets students with existing programming experience. It requires courses in statistics, data-centric programming, data management, and data analysis. Note that the courses 01:198:461 and 01:198:462 have prerequisites that include courses in addition to those required for the minor.

  • Regression Methods 01:960:463 (3) and
  • Choose from one of the following Machine Learning courses
    • Machine Learning Principles 01:198:461 or
    • Introduction to Deep Learning 01:198:462

Track 2.

This track targets students with a quantitative background but perhaps little programming experience. It can be pursued without any additional prerequisite courses beyond those in requirements I, II, and III.

  • Applied Statistical Learning01:960:486 (3) and
  • Choose from one of the following
    • Information Visualization 04:547:321 (3) or
    • Data in context 04:189:220 (3) or
    • Regression Methods 01:960:463 (3)

Track 3.

This track is intended mainly for Economics majors or Quantitative Economics minors. In any case, completion of the intermediate economics core courses (01:220:320, 321, and 322) is required, as these courses are prerequisites to Advanced Analytics for Economics, 01:220:424. Calculus II (01:640:152) is a prerequisite.

  • Machine Learning for Economics 01:220:424 (3) and
  • Choose from one of the following
    • Information Visualization 04:547:321 (3) or
    • Data in context 04:189:220 (3)

Track 4.

This track will allow students to develop skills in human-centered aspects of data science. Introduction to computer concepts (04:547:201) is a prerequisite for the following courses.

  • Information Visualization 04:547:321 (3) and
  • Data in context 04:189:220 (3)

View Data Science Minor Pathway

Data Science Minor Declaration

Requirements: To declare the Data Science minor, students must successfully complete the Data Literacy course, Data 101 (198:142/960:142), with a grade of C or better.

School of Arts and Science (SAS) students can add the Data Science to MyMajor

Other Rutgers- New Brunswick Students (non-SAS students): For students in other schools, it is essential to complete the required forms corresponding to your school to include the Data Science major in your academic journey officially. Here are the links to the respective forms:

  • School of Environmental and Biological Sciences:
  • School of Engineering:
  • Rutgers Business School (RBS):
Data Science - Minor (2024)


Is a minor in data science useful? ›

A minor in data science can make students more valuable to employers by enhancing their critical-thinking skills and improving their decision-making in careers as diverse as business, engineering, science, industry and government.

Can a average student learn data science? ›

Every student who doesn't have a degree in computer science can also opt for an Online course program. The course teaches you exactly what you need to be a data scientist. So, you see, with an internet connection, some time, & a laptop, you can be data scientists!

Is data science a difficult major? ›

Data science demands a robust set of technical skills. This includes proficiency in programming languages like Python or R, a strong foundation in statistics and mathematics, expertise in machine learning techniques, and the ability to handle large datasets using tools like SQL or big data technologies.

Is it hard to break into data science? ›

Breaking into data science can be especially difficult if you have majored in a field such as sociology, psychology, and the like. While your skills—hard and soft skills—matter eventually, you should remember that you are competing with those who have an undergraduate or advanced degree in a related field.

Why not to choose data science as a career? ›

Data science is a field with constant challenges. A data scientist needs to have patience and persistence and be ready, re-prioritize, and make changes to the project they are working on at any time. They need to constantly learn, upgrade their skills, and keep up to date with the latest developments in their field.

Is a minor actually useful? ›

There are some clear benefits to earning a minor. Undergrads can complement their major and develop interdisciplinary strengths with a minor. What's more, a minor lets students explore fields outside their major. A minor might even help you out after graduation as you look for jobs.

Is 30 too late for data science? ›

It is never too late to begin a career in data science; your previous employment experience, regardless of role or industry, is a strength and asset that new entrants lack. Consider Gyansetu to embark on your journey into the world of data science.

Is 35 too old for data science? ›

For certain data analytics roles or disciplines (like data science), having more experience presents a clear advantage over younger, less-experienced candidates. But even for entry-level roles, being older doesn't have to be a barrier to success.

Is data science a lot of math? ›

Math is an important part of data science. It can help you solve problems, optimize model performance, and interpret complex data that answer business questions. You don't need to know how to solve every algebraic equation—Data Scientists use computers for that.

Which is harder CS or data science? ›

A comparison of CS vs DS reveals that the latter has a steeper learning curve as it combines statistics, computer science, and mathematics. You require a solid and comprehensive knowledge base to create solutions for different needs.

What is the hardest thing in data science? ›

Data quality:

One of the greatest challenges facing data scientists is ensuring that the data they work with is of the highest quality. Low-quality data can result in inaccurate or incomplete insights, making it difficult to draw meaningful conclusions.

Is data science a stressful job? ›

The sheer volume of data that needs to be analyzed can also be overwhelming, leading to high levels of stress. Additionally, the need to stay updated with constantly evolving technologies and tools adds to the pressure.

What percentage of data science projects fail? ›

Indeed, the data science failure rates are sobering: 85% of big data projects fail (Gartner, 2017) 87% of data science projects never make it to production (VentureBeat, 2019)

Is data science hard for beginners? ›

Data Science is a vast field, and in the beginning, it might feel overwhelming to grasp all the fundamentals of it. But with hard work, focus, and a strong learning roadmap, you will realize that it is just another field and not hard to learn the skills required to get into Data Science.

Is data science harder than engineering? ›

Hence, Data Science is neither harder nor easier than Software Engineering, as both courses demand different skill sets and educational backgrounds for fulfilling the desired responsibilities. Data Scientist or Software Engineer: Which one is right for you?

What can you do with a minor in data analytics? ›

Search for opportunities
  • Business Analyst.
  • Operations Research Analyst.
  • Market Research Analyst.
  • Consultant.
  • Data Scientist.

How useful is a degree in data science? ›

Data science degrees provide students with the technical skills they need to analyze data and develop actionable conclusions from those assessments. The coursework of such programs typically emphasizes programming, statistics, math and some elements of social science.

Do I really need a data science degree? ›

While it's not necessary to have a data science degree specifically, you would need to hold a Bachelor's or a Master's in, for example, computer science, statistics, economics, finance, physics, or other fields that will be easy to pivot toward data science after.

Is data science enough to get a job? ›

Getting a job as a data scientist isn't easy and you need to be very persistent to succeed in this field. One does not become a data scientist overnight. It takes a lot of learning, experience, and understanding of the concepts, especially if you want to start a career in data science as a fresher.

Top Articles
Latest Posts
Article information

Author: Kieth Sipes

Last Updated:

Views: 5959

Rating: 4.7 / 5 (47 voted)

Reviews: 94% of readers found this page helpful

Author information

Name: Kieth Sipes

Birthday: 2001-04-14

Address: Suite 492 62479 Champlin Loop, South Catrice, MS 57271

Phone: +9663362133320

Job: District Sales Analyst

Hobby: Digital arts, Dance, Ghost hunting, Worldbuilding, Kayaking, Table tennis, 3D printing

Introduction: My name is Kieth Sipes, I am a zany, rich, courageous, powerful, faithful, jolly, excited person who loves writing and wants to share my knowledge and understanding with you.