As a student of the Data Science Program, you’ll prepare to become the data professional our evolving world needs: a holistically trained expert with the vision and skills to use data to solve problems, unite communities, prevent disasters, transform industries, and most importantly, improve lives.
The Master of Data Science program gives our students a deep set of core competencies to see what tomorrow can be, and shape it every day:
Prepare students for courses in statistics, ML, data management and engineering
Empower students to apply computation and inferential thinking to tackle real-world problems
Enable students to start careers as data scientists by providing experience with data tools and techniques
Contact information
Co-Director of Data Science Academic Programs Professor of Informatics and Computing
Data science is the mining, collecting, analyzing, managing, and storing data to help make data driven decisions in e-commerce, finance, government, healthcare, science, social networking, telecommunications, politics, utilities, smart meters, education, aerospace, etc. By collecting, analyzing, managing, and storing data, businesses can run more efficiently and make data-driven business decisions.
To prepare for a career in data science, students need to be proficient in math, statistics, and computer programming such as Python or R. Students need to understand the data to analyze and interpret the data in a meaningful way. To visualize the data, data scientists often use Tableau, Hadoop, or Apache Spark.
According to KDNuggets, “data scientists are highly educated – 88% have at least a master’s degree.” Their undergraduate background is in computer science, statistics, social science, or physical science.
The main difference between a data scientist and a computer scientist is that a computer scientist develops software and data scientists use the software developed by computer scientists to analyze and interpret the data and identify trends.
If you like to mine, collect, analyze, manage, and store data, perhaps you should pursue a master’s degree in data science degree as data scientists mine, collect, analyze, manage, and store data to help make data driven decisions. Data scientists have a good understanding of the data by asking and answering questions as they do their analysis. They are adept in pulling data from multiple sources, cleaning up the data, and analyzing the data to help make sound business decisions. By analyzing the data, the data scientist can make suggestions as to how to improve the process or how to make the process more efficient. When presenting the data to stakeholders, the data scientist designs, creates, and builds data models and data visualizations to make the data easier to understand.
To be competitive in the job market, a large majority of companies are looking for students who have a bachelor’s degree coupled with a master’s degree in data science. The most common data science job titles are data scientist, data architect, data engineer, business analyst, or data analyst.
If you like to build new things, perhaps you should pursue a master’s degree in computer science as computer scientists design, create, test, document, and debug code, software, and mobile applications. Often computer scientists collaborate with other computer scientists and their teams in developing a larger piece of software, application, or computer system.
To be competitive in the computer science job market, you need at least a bachelor’s degree in computer science. Students who have a master’s degree in computer science are paid more, have more responsibility, and more room for advancement in a company. The most common computer science job is software development engineer, software developer, Java developers, systems engineer, or network engineer.
It is expected that students who have degrees in data science and computer science will be in high demand for at least the next five to ten years. By earning a master’s degree in data science or computer science coupled with your undergraduate degree will give you an edge on the job market.
The Master of Data Science degree is interdisciplinary in computer science, information science, informatics, statistics, engineering, and other disciplines. It prepares students to pursue a data science related career as a data scientist, data analyst, data architect, etc. or admission to a Ph.D. program.
To earn the Master of Data Science degree, you must successfully earn 30 graduate-level credit hours. The program takes two years to complete. As a Master of Data Science student, you have the option of focusing on one of the following four distinct tracks: (1) Applied Data Science; (2) Big Data Systems; (3) Computational and Analytical; and (4) Managerial Data Science.
Data Science is in the STEM field (science, technology, engineering, or mathematics). Since the Data Science program is interdisciplinary and an applied program, international students are eligible for a STEM OPT Extension.
The Data Science program gives our students a deep set of core competencies in multiple areas—including programming, statistics, data analytics, machine learning, data wrangling, data visualization, communication, business foundations, and ethics that increase their marketability in the industry. The learning outcomes of the MS Data Science Residential degree are the knowledge and skills acquired in the program that are transferable to successfully use data to solve problems, which include:
Data preparation and presentation
Exploratory data analytics & visualization
Model fitting and inference
Efficient and scalable data processing
The Master of Data Science degree requires a student to successfully complete 30 credit hours. Master’s students must be enrolled full-time each semester. Typically, it takes students two years to complete the Master of Data Science program.
During the first three semesters, students take nine (9) credit hours per semester and three (3) to nine (9) credit hours during the fourth semester. The student’s advisor, program director, and the Director of Graduate Studies must approve exceptions. During the summer between Year I and Year II of their studies, students often take an internship.
Graduate Grading Standards
Only eligible courses with grades of C or higher will count toward the necessary credit hours for graduation. All grades, including unsatisfactory grades, are used in computing cumulative GPA.
Any grades lower than C will not meet satisfactory academic progress. Grades of C-, D+, D, D-, or F are considered unsatisfactory/failed. The failed/unsatisfactory grade will be calculated within the cumulative GPA, but the credits will NOT count toward graduation requirements.
Students are responsible for the additional cost (tuition and fees) of any repeated course due to unsatisfactory grades earned. Unsatisfactory grades (C-, D+, D, D-, or F) will not be removed from official transcripts even if the student repeated the course in a future term to reach a satisfactory grade.
If an unsatisfactory grade is earned in a core requirement, the core course (or program requirement equivalent based upon Data Science Track program requirements) must be repeated in the next available term.
Good Academic Standing
Luddy graduate students must remain in good academic standing throughout their time in their graduate studies. Students must continue to make satisfactory academic progress towards timely completion of their degree. All graduate students must maintain a cumulative GPA of 3.0 or above to meet graduation requirements for degree completion.
Academic Probation
Grades are reviewed after each academic term. Students who are placed on probation will be notified in writing via student’s IU Email. Probationary status may result from one or more of the following:
Earning a cumulative GPA below 3.00.
Earning a grade of C- or lower which places cumulative GPA under 3.00.
Unsatisfactory academic progress to degree including repeated withdrawals and/or multiple unsatisfactory (D+, D, D-, F, W, I) grades.
Students who are placed on academic probation, under a cumulative GPA of 3.0, at the end of their second academic term (typically spring) will have to meet with their assigned academic advisor for an academic probation status discussion on their eligibility for pursuing future term internship opportunities and eligibility.
Luddy Graduate Programs Probationary Process
Students will be required to meet with their assigned academic advisor after being notified of their academic probation status to discuss future term academic progress and planning.
Students on academic probation will have an academic hold ( V00 – academic probation with impact) on their record until they are removed from probationary status.
Students can regain good academic status by earning a cumulative GPA 3.0 or above in the following term as outlined in their official probationary notice.
Failure to meet the academic probation cumulative GPA standards in the following term may result in dismissal from the program.
Students who are on a Student Academic Appointment (SAA) may not be eligible for SAA during the terms they are actively on probation. SAA recipients must maintain good academic standing and a cumulative GPA of above 3.0. Academic Probation may cause termination of SAA.
Incomplete Grade
An “Incomplete” indicates a student’s work is of passing quality as of the end of the term, but a portion of the course has not been completed.
Eligibility to earn the grade of Incomplete (I):
The faculty member must agree to grant an incomplete.
Students must be in good standing in the course (C or above) at the time the Incomplete grade is requested.
Granting Incomplete grades are at the discretion of the faculty leading the course.
All conversations for consideration of an Incomplete grade should be discussed with the faculty member directly.
One or more incomplete grades may impact a student’s future enrollment and/or academic standing. An outstanding Incomplete grade may impact the student’s eligibility for graduation until their record is cleared of all “Incompletes” in both elective and required course work.
Please consult with the faculty member granting the incomplete for any course fulfillment or grade update questions. If students fail to complete outstanding course deliverables within the agreed upon timeline granted the instructor has allowed, the Incomplete (I) grade will automatically reflect an F (failed) grade. Students can also review university guidelines for policies regarding process, limits, and methods of your Incomplete grade.
Data Science is shaping the future. According to the U.S. Bureau of Labor Statistics Report, by 2031, the employment rate for data scientists will grow by 36% from 2021 to 2031. According to Dr. Martin Schedlbauer, a Data Science Professor at Northeastern University, “data science careers are in high demand and this trend will not be slowing down any time soon, if ever.”
The demand for data scientists is high. With a Master of Data Science degree from Indiana University’s Luddy School of Informatics, Computing, and Engineering, you could pursue a career as a:
Business Intelligence Developer
Data Architect
Data Scientist
Data Analyst
Data Engineer
Decision Scientists
Enterprise Architect
Software Developers
Statistician
The Luddy School of Informatics, Computing, and Engineering’s Office of Career Services offers a variety of programs and services to help students find and succeed in internships and full-time jobs. The Office of Career Services will review student’s resumes and cover letters, will hold mock interviews, will assist in negotiating a hiring package, etc.
The Indiana University’s Career Development Center is also available to Luddy graduate students.
In the fall and spring, the Luddy Office of Career Services hosts two large career fairs. Many of the employers who attend these career fairs are looking to hire students for full-time employment or internships. For Luddy Career Outcomes, go to our Career Services Website.
All students must abide by the Indiana University Code of Student Rights, Responsibilities, & Conduct. This applies to scholarship, any role the student may have as an Associate Instructor (AI), relations with colleagues, relations with students, and compliance with academic standards with respect to academic ethics.
If students are not familiar with the concept and best practices of avoiding any hint of plagiarism in American universities, they should become familiar with these standards. The Code provides a series of documents describing the behaviors, ideals, and goals for Indiana University.
Our commitment to diversity, equity, and inclusion is grounded in our aspiration to cultivate intellectual rigor and curiosity among our students and to prepare them to thrive in and contribute to a globally diverse, complex, and interconnected world. This includes creating an inclusive and multicultural educational landscape through the retention and recruitment of diverse students in terms of their backgrounds, identities and experiences, who have been traditionally underrepresented in graduate education. The program promotes a climate of diversity, inclusion, engagement, and achievement, which are integral components of graduate education and beyond.
The Data Science Club at Indiana University (DSC@IU) is a student-run organization affiliated with Luddy. All MS Data Science Residential students are encouraged to actively participate in the club. DSC@IU helps students acquire vital skills that will kick-start their journey into the Data Science world, through various means like mentorships, tutorials, seminars and study groups. The Club organizes networking meetups for students to connect with Alumni, Professionals, and Employers for career guidance.
Moreover, it conducts Hackathons and Datathons to get hands-on experience with real-world problems and brings great opportunities to socialize through fun events. For information about the Data Science Club, email dsclub@iu.edu.
Students who are admitted to the Master of Data Science degree are thought to be ready to start the program with the essential knowledge to be successful in the program. They are not required to take remedial coursework.
However, if a student feels they need remedial work in math and/or programming, they may want to consider enrolling in the Data Science Essentials remedial self-paced package of online coursework that can help you prepare to be successful in the program. The remedial courses available in the Data Science Essentials are: Basic Linear Algebra & Calculus; Basics of Java; Basics of Python Programming; Introduction to C++, Introduction to R Programming; Introduction to SQL; and Introduction to MongoDB. No certificates or badges of understanding will be awarded as these are self-paced modules. This course is offered through the Luddy Office of Online Education (luddyonl@iu.edu). The cost of this course is $150.
Curriculum
The Master of Data Science degree is a 30-credit degree program offered by the Luddy School of Informatics, Computing, and Engineering. The curriculum consists of 15 credits of core requirements and the remaining 15 credits are satisfied by fulfilling the Master of Data Science track-specific requirements and electives.
Curricular Practical Training (CPT) and DSCI-D591 Graduate Internship Course (1-3 credits)
The CPT opportunity enables students to work for a company or organizations in the U.S. as an integral part of the established Data Science education program. Students apply their academic knowledge of the in-demand technical skills, working well in a group, interpreting data findings effectively to an audience in various formats, as well as the soft skills employers seek, prepare our graduates to use their expertise in the industry. Each internship will vary according to the context, industry, responsibilities, and personal experiences of the student.
To demonstrate that an internship is an integral part of the degree, all students (domestic and international) must enroll in and receive course credit for their corresponding master’s program internship course.
International Students - Curricular Practical Training
Curricular Practical Training (CPT) is a work authorization which permits international students with an F-1 visa to partake in an off-campus academic internship that is an integral part of their academic curriculum. U.S. Immigration regulations are extremely complicated, change often, and differ depending on each student’s specific situation. Please consult with the Office of International Studies for the most up-to-date policies, qualifications, and requirements.
Current U.S. Immigration eligibility for F-1 students to apply for CPT are the following:
You are a full-time student, with F-1 status for at least one full academic year (e.g., fall, spring semesters) for 30 weeks of instruction – to be eligible for CPT.
You have an offer letter outlining job duties of the training opportunity in your major field of study which is integral to your academic program.
You have registered for an appropriate course which meets degree requirements for academic credit that covers the duration of the training that you seek.
See additional immigration policies and practices for CPT through OIS resources.
Domestic Students
Domestic students who pursue academic credit for an internship experience, seen as integral to their curriculum, are expected to submit offer letters which meet all criteria needed for the Luddy Work Authorization Form review. After enrolling in the academic internship course, no further processing is required for domestic students to begin working at their internship.
Review the Luddy Career Services recruiting guidelines, to understand student responsibility when seeking and applying for internships while representing the Luddy School of Informatics, Computing, and Engineering.
Students are only eligible to accept one offer from an employer per academic semester.
Upon accepting an internship position, all pending applications should be withdrawn or cancelled, and all internship employment seeking should cease.
It is not ethical to continue searching for an internship, nor in accordance with the Luddy recruiting guidelines, after accepting an offer.
Failure to enroll in the approved internship course may delay processing time for CPT review by OIS.
Students are expected to complete the steps as recommended by their Luddy graduate advisor in a timely manner to allow time for CPT processing.
Students who are actively on academic probation, below 3.0 cumulative GPA, or have outstanding Incomplete (I) grades, may not be eligible to apply for CPT/internship credit. Students within probationary academic standing will have to seek an exception from their advisor prior to beginning an internship job search or accepting a position.
All internships provide opportunities to build skills in communication, time management, and team dynamics. The student can reinforce their academic learnings with an understanding of a real-world corporate setting, which will help the student make important decisions about a future career in Data Science.
Upload offer letter to Atlas and any/all supplemental documentation needed to Atlas
Wait for GSO academic advisor to process work authorization and confirm class permission/enrollment
Enroll in designated internship course section(s) from advisor confirmation
Submit CPT Advisor Verification form in Atlas with the following information:
Email: gradvise@iu.edu
Advisor: Luddy GSO assigned academic advisor’s full name
Office/Department: Luddy Graduate Office
Wait for OIS to process and issue new I-20 prior to beginning working at your fall internship
Note: OIS CPT review timeline cannot be expedited
CPT is an experience to be integral to the students’ academic curriculum. The DSCI-D 591 course is created to allow students the opportunity to gain professional work experience in industry and to utilize skills taught in the classroom.
No more than 3-credit hours total can be earned in DSCI-D591 during a student’s academic program.
If DSCI-D 591 is taken to fulfill the capstone requirement for Applied Data Science Track, the student may enroll in any 1 or 2 credits of graduate Luddy courses 500-level or above to fulfill the remaining capstone required credits. (see capstone requirements for more information).
CPT, internships are not permitted in an international student’s final academic term.
International master's students are only eligible for CPT/Internships in their summer and second-year fall term.
Below is a table outlining hourly requirements for Part-Time and Full-Time Internship Work-Authorization and CPT for the total weeks employed at internship for consideration as follows:
Internship Term
Hours Working Per Week
Weeks employed to meet internship hour minimum examples
Summer
Full-Time ( > 21 hours)
8 weeks at 40 hours/week
Summer or Fall
Part-Time ( < 20 hours)
8 weeks at 20 hours/week
Summer or Fall
Part-Time ( < 10-15 hours)
10-16 weeks at less than 15 hours/week
Fall 16-Week
Part-Time ( < 20 hours)
8 weeks at 20 hours/week
Note full-time, 21 hours and above per week, is only permitted in the summer academic term.
Note fall term internships are only permitted for remote internships.
If students are seeking a part-time internship of less than 20 hours per week in the fall term, a work authorization would need to be submitted prior to the end of the first week of the fall term to avoid any late schedule change fees or tuition losses. All course schedule changes are subject to current drop rates at the time of schedule change request.
Academic expectations for graduate students enrolling in an internship for academic credit are held to the same academic responsibility, professionalism, integrity, and rigor as taking any other graduate-level Luddy course students earn towards degree requirements and completion. To demonstrate that an internship is an integral part of the degree, students must enroll in and receive course credit for their internship experience.
Academic Outcome Expectations for DSCI-D591
Students enrolled in the master’s graduate internship courses will be required to submit assignments during their internship experience earning a grade in the academic course. Grades will be reviewed by the faculty leading the internship course.
Exit Letter
Data Science students are required to submit an exit letter from the employer stating you successfully completed the internship. There is no formal template for the exit letter but the required information within the document will be required:
Letter is Prepared on Company Letter Head
Student Name
Dates of Employment
Internship Completion Date
Employer Statement Indicating the Internship was Successfully Completed
Internship Summary Report
Students will need to submit a summary report (1-2 pages) of what they learned for each internship experience.
The summary needs to document what you learned with a strong emphasis on the educational benefits of your internship.
The summary report will not need to include any details that breach your employer's confidentiality.
Guidelines and Academic Outcomes for DSCI-D591
All materials requiring academic grade review will be submitted within the Canvas course and/or as directed by faculty overseeing the internship course.
Any questions regarding course assignment submissions should be directed to the faculty leading the internship course.
Final grades will be assessed at the end of the corresponding internship term with all subsequent academic materials reviewed for consideration.
All materials for grade submission should be submitted by corresponding term academic calendar deadlines outlined by faculty leading the course.
If students do not submit internship course materials within a timely manner, an Incomplete grade may be given.
Students should communicate with faculty leading the course to request an adjusted due date for their academic materials if an Incomplete is being requested or if more time is needed for student’s employer to supply an internship exit letter.
All questions regarding final grades earned, grading expectations, or grades submitted should be directed to faculty leading the course.
GSO Workshops & Information Sessions
Students are strongly encouraged to attend workshops and information sessions hosted by the Graduate Studies Office related to CPT, OPT, internships, graduation, enrollment requirements, and other important student processes. These sessions are designed to provide students with the most current information, important deadlines, and guidance on completing required steps successfully.
Attending these sessions can help prevent delays, errors, incomplete submissions, and misunderstandings that may impact administrative processing timelines, graduation eligibility, or immigration status.
Workshops and information sessions are typically scheduled during the fall and spring terms. Information regarding these sessions is shared with students through their IU email account. Students are expected to regularly check their IU email account for official communications sent from the Luddy Graduate Office and Indiana University.
Applied Data Science
Curriculum
Please see information below for more detailed information of some of the capstone options. If 1 or 2 variable capstone credits are taken to fulfill the capstone requirement, then the student may enroll in any 1 or 2 credits Luddy course to fulfill the remaining credits.
DSCI-D 699 is a variable credit "Graduate Independent Study in Data Science" course at the Indiana University Luddy School of Informatics, Computing, and Engineering. Independent study allows students to conduct tailored research or projects under faculty supervision, often resulting in a written report, database development, or lab experience.
Students are responsible for finding a faculty member willing to serve as a mentor. Before enrolling in the course, students must complete the Independent Study/Research Rotation Request Form which must include a formal student project proposal with faculty approval. Instructions for the proposal can be found at the top of the independent study form. Once the form is approved, students will be given permission to enroll in the course.
Faculty mentors will guide students in this course and are responsible for providing a final grade assessment at the end of the academic term.
This program pairs faculty from across the campus in any discipline with graduate students pursuing a M.S. in Data Science (MSDS) through the Luddy School of Informatics for spring or summer data science focused research initiatives. Students must enroll in FADS for credit to meet capstone requirements. Students must apply for this program.
Students interested in this program should review the website and FAQs for more information.
Students are organized into teams to carry out real world projects in conjunction with project sponsors, while learning about methodologies for data science consulting.
This course offering is reserved for Online Masters Data Science students only. This is a 100% online course typically offered in the fall term only. Seats are limited. Residential students interested in the course may join the waitlist upon enrollment opening. Students on the waitlist will be moved to enrolled in the order of waitlist cue, until maximum course seats are filled.
This opportunity is open to U.S. citizens or permanent residents and requires an application. If students are selected and accept to participate in the training program, Luddy GSO will assist in coordinating enrollment in MGEN-Q581. Enrollment in this program will occur in a student’s second fall term of their program.
ECON-M 504 and ECON-M 514 may be taken to fulfill the Economic Data Analytical domain. These credits carry 4-credit hours, often leaving students with more than the required 6 credits for the domain. Remaining 1-credit hour from these select courses can be used to fulfill either the capstone or remaining elective credit hours with permission from the Data Science Director of Graduate Studies.
