Master's in Data Science and Data Analytics: A List of Programs

Introduction

I recently decided to go back to grad school to work on a Master's in Data Science/Data Analytics.  There are lots of programs to choose from, and many different factors to consider: location, reputation, curriculum, cost, online/on-campus, etc.  While there are many lists available online, here is the list of schools I looked at with information about each program based on my information gathering.

The Programs

Some of this data may get out of date as time goes on. Also, some programs are more complete in their information than others. Please verify the information gathered below with the official information on the website.  This information below is provided merely to get you started in thinking about what you may be interested in looking at.

In alphabetical order, here is a list of programs I looked at:

CUNY (City University New York School of Professional Studies)

• Website:
• https://sps.cuny.edu/academics/graduate/master-science-data-analytics-ms
• Degree:
• MS Data Analytics
• Admission Requirements:
• BA, 3.0 GPA, personal statement, resume, two letters of recommendation, skill assessment, admissions interview
• Time to complete:
• 36 credits, 12 courses
• Part time student: 2-3 years
• Cost:
• Tuition is $425 per credit, $1275 per course (online program = in-state tuition)
• approximately $18,000 after fees
• Curriculum:
• Business focus: very little.  Electives available (e.g., project management)
• Technical focus: very technical.  Topics include security and architecture, web analytics, networks, Hadoop/Mahout, Github, Python, R, MapReduce, SQL, NoSQL, graph databases.  More focus on data science as opposed to traditional BI/analytics.
• Online Time:
• 100% online
• Live classes:
• Yes, but students are not strictly required to attend the weekly meetings.  No grading for attendance.  All meetings will be recorded. 
• Campus Time:
• None
• Faculty:
• Practitioners, not researchers.  Members of data science/analytics business and technology community.
• Application Due:
• For Fall 2016, due by July 15, 2016

Northwestern

• Website:
• http://sps.northwestern.edu/program-areas/graduate/predictive-analytics/index.php
• Degree:
• MS Predictive Analytics
• Admission Requirements:
•  The Graduate Record Examination (GRE) is not required, but strong scores bolster chances for admission.  Letters, resume, statement of purpose, transcripts.
• Time to complete:
• Students are required to complete 12 courses to earn the degree. It is designed to be completed in two to three years of uninterrupted part-time study (one to two classes per quarter), although students are allowed five years to finish the program.
• Cost:
• $49,368 
• Curriculum
• Business focus: not much.  Project management, Theories of leadership
• Technology focus: very technical.  SQL, NoSQL, R, Python, SAS.
  Topics include marketing analytics, risk analytics, text analytics, web and network data science, variable selection, PCA, clustering, GLM, Poisson, survival, ARIMA
  database management, ML, data visualization.  Focused specifically on predictive analytics, as opposed to all around BI.
•  Online Time:
• 100% online.
• Live classes:
• Yes, but you can watch recordings.  Looks like you can choose a day to do a live sync with professors for office hours.
• Campus Time:
• None
• Faculty:
• Unknown.
• Application Due
• Fall - due by July 15, 2016

 

Southern Methodist University (SMU)

• Website:
• https://datascience.smu.edu/
• Degree:
• MS Data Science
• Admission Requirements:
• GRE scores are required for admission to the program. The GRE requirement can be waived if you have five or more years of industry experience in a related field or a previous master's degree.
• Time to complete:
• Students in the program complete 32 credits, with 30 credits of core coursework and a 2-credit immersion experience, which will take place at SMU. 
•  Students can earn the Master of Science in Data Science in 18–24 months.
• This is a rigorous program for highly motivated students. In addition to the weekly class sessions, you can expect to dedicate approximately 10 hours per week per class to studying and completing self-paced online coursework.
• Cost:
• $54,528
•  $1,704 per credit
• Curriculum:
• Business focus: not much
• Technical focus: more technical.  Statistics, ML, and big data.  Python, Github, Shiny, SAS, MongoDB, XML, network security, SQL, No SQL.  More data science than traditional BI.
•  Online Time:
• All online, except for one weekend campus visit required.
• Live classes? 
• Unknown
• Campus Time:
•  There is an extended weekend experience which takes place on the SMU campus in Texas when students have the chance to meet in-person with classmates and faculty for collaborative, hands-on workshops and informational sessions with networking and relationship building opportunities.
• Faculty:
• Unknown
• Application Due:
• There are three cohort start dates each calendar year. January, May, and August.  For the September 2016 Cohort, Priority Application Deadline - May 16, 2016.  Final Application Deadline - July 11, 2016.  Classes Start - August 29, 2016

 

UC Berkley

• Website:
• https://datascience.berkeley.edu/
• Degree:
• Masters in Information and Data Science (MIDS)
• Admission Requirements:
• GRE is required.  No more than five years may have passed between the GRE or GMAT test date and the application deadline.  Resume, transcripts, statement of purpose, etc.
• Time to complete:
• The  program consists of 27 units. Students can complete the program on one of three paths: full-time, accelerated, or part-time.  The full-time path is designed for working professionals and can be completed in 20 months, with two courses per semester.  The part-time path allows students to drop down to one course per semester and complete the program in no more than 32 months.
• 9 courses
• Cost:
• $2,222.22 per unit, plus a $525 semester fee. Tuition is charged per unit.
• $59,999.94 total cost for tuition.
• Curriculum:
• Unknown
•  Online Time:
• While all courses are delivered online.
• Live classes:
• Unknown
• Campus Time:
• Students are required to attend at least one, 3-4 day immersion on the UC Berkeley campus. 
• Faculty:
• Unknown
• Application Due
• The Master of Information and Data Science program starts three times throughout the year (January, May and September).

 

University of Maryland, University College (UMUC)

• Website:
• http://umuc.edu/academic-programs/masters-degrees/data-analytics.cfm
• Degree:
• MS Data Analytics
• Admission Requirements:
• transcripts, statement of purpose, etc.  No GRE required.
• Time to complete:
• 36 credits are required, 6 courses, 6 credits each.
• Cost:
• $24,984
• Curriculum:
• Business focus:  much more business than other programs.  Strategy.
• Technology focus: technical.  Big data, BI, visualization
• A required grad info course.
• A balance of business and technical courses.  More BI than data science focused.
•  Online Time:
• Unknown
• Live classes:
• Unknown
• Campus Time:
• Unknown
• Faculty:
• Unknown
• Application Due
• Unknown

 

University of Washington

• Website:
• http://datasciencemasters.uw.edu/
• Degree:
• MS Data Science
• Admission Requirements:
• GRE required.  Statement of purpose, transcripts, letters of recommendation, etc.
• Time to complete:
• Nine 5-credit courses, for a total of 45 quarter credits.
• Cost:
• $44,775
• Curriculum:
• Unknown
•  Online Time:
• none.  All is on-campus.
• Live classes? 
• Campus Time:
• Classes held in the evenings on the UW Seattle campus.  Classes one or two times a week in evening.
• Faculty:
• Unknown
• Application Due:
• Applications due April 22

 

University of Wisconsin

• Website:
• http://datasciencedegree.wisconsin.edu/data-science-program/
• Degree:
• MS Data Science
• Admission Requirements:
• Letters, transcripts, statement of purpose, etc.  No GRE needed.
• Time to complete:
• 2 year program , 12 courses. 
• Summer, fall, spring semester schedule
• Cost:
• $825 per credit.  36 credits required
• $29,700 for degree
• Curriculum
• Business focus: ethics, decision theory
• Technology focus: R, Python, SQL Server, and Tableau, classification, visualization, network, web analytics, PowerBI, GIT, data warehousing, big data, Hadoop, Pig, Hive.
• Virtual lab with all software preloaded.  Less technical than some but more than other programs.  Gives BI and business background. 
• No electives. 
• Online Time:
• 100% online
• Live classes:
• Unknown
• Campus Time:
• None
• Faculty:
• Mix of math, business, computer science researchers.
• Consulted with practitioners to make sure topics are relevant.
• Application Due:
• Application is rolling.  August 1st is latest for Fall 2016.

 

Villanova

• Website:
• http://analytics.villanova.edu/lpppc-msa/
• Degree:
• MS Analytics
• Admission Requirements:
•  Completed online application, resume, two essays, transcripts, recommendations, GMAT or GRE score (recommended).
• Time to complete:
• Earn your degree in as few as 20 months.  The program consists of five semesters—each of which is divided into two terms. You will take one or two courses per term.
• 33 credits, 11 classes
• Cost:
• $37,950
• Curriculum:
• Business focus: done through the school of business.
• Technology focus: statistics , Hadoop, text and web analytics.  Mostly industry BI software.  Has R, but no Python.
• More business focused, all around coverage of everything BI.  Definitely more traditional BI than data science. Designed to expose students to the whole analytics continuum from data collection through analysis through implementation and use.
•  Online Time:
• 100% online format
• Live classes:
• Courses are primarily delivered in an asynchronous environment using a combination of tools such as recorded presentations, discussion forums, and interactive case studies to let students learn according to their own schedule. However, select synchronous elements including online discussion sessions (all recorded so you can watch them on your own schedule) and virtual office hours are also incorporated into each course.
• Campus Time:
• None
• Faculty:
• All are professors, academicians, researchers, usually with business background.
• Application Due:
• Fall Semester: 6/30/16

Applied, Accepted, Committed

The programs above are CUNY, Northwestern, SMU, UC Berkeley, University of Maryland (UC), University of Washington, University of Wisconsin, Villanova.  Which program did I choose, and why?  Your values may differ, but here is what I was interested in:

• Admission requirement:
• no GRE requirement.  I've been there, done that, and I didn't want to take it again.
• Time to complete: 
• not very important to me.  I was not interested in how long it would take.  In fact, some of the longer programs interested me more because they would cover more ground and be more in depth.  They had more classes.
• Cost:
• low cost is important since I am paying for the program myself, but it is not conclusive.  However, it can be a deciding factor in deciding between two similar programs.
• Curriculum:
•  some programs are more business oriented.  Some are more traditional BI focused.  Still others are more data science and technically oriented.  I wanted to be in a program that was very technical.  I felt that this was where I needed the most help in gaining the skills and experience I needed for the sorts of jobs I was interested in.
• Online:
• I wanted to be in an online program, rather than an in person program.  The program needed to fit my schedule since I would be working and I have many other commitments.
• Live classes:
• I needed to be able to watch recorded lectures, not simply attend live sessions at inconvenient times.
• Campus time:
• not critical one way or another.  A short weekend visit would be fine.  But I didn't want the whole program to be on campus.  Ain't nobody got time for that.
• Faculty:
• I wanted to learn from people on the cutting edge in business and technology.  That is, I wanted to learn from people doing and using the analytics skills being taught.  This field is changing fast, and I wanted to learn from those that knew where it was currently at in the industry and where it was going.
• Application due date:
•  not as crucial, as long as I could get the application in on time.

So which programs did I apply to based on the above criteria?  Here is what I did along with the reasons for making my decision to apply or not:

• Applied:
• CUNY
• 12 courses in semester system, taught by practitioners, no GRE needed, least expensive, very technical.
• University of Wisconsin
• 12 courses, mostly technical, no GRE needed, and not very expensive.
• Villanova
• More BI and business focused, but not as expensive as other programs.  Good reputation.  11 classes and no GRE needed.
• Northwestern
• Very expensive, but has great reputation, great technical curriculum, no GRE required.  12 courses in quarter system.
• Didn't apply:
• SMU
• really technical, but really expensive compared with similar programs.
• UC Berkeley
• really expensive, required GRE, not as many classes offered in program.
• University of Maryland (UC)
• inexpensive, but very few courses and the program was more business focused.  6 long courses.
• University of Washington
• expensive and also not online.  GRE required.  Not as many courses in program - 9 courses in quarter system.

Of the four programs I applied to, I was accepted into the three programs I heard back from before I made my decision (I didn't wait for the fourth program to respond back to me).  Ultimately, I chose CUNY to do my Master's degree.  It was the least expensive, very technical, taught by practitioners (not researchers), and it covered many courses and topics I was interested in taking.  It was, in short, the best fit for my interests and needs for a Master's program in analytics/data science.

Conclusion

Again, the above list is not exhaustive.  There are many other data science and data analytics programs that one can apply to.  These are only the ones that I looked at.  Also, simply because I chose CUNY does not mean that another program may be a better fit for someone else.  An on-campus program may be a better fit for some, a less technical program for others.  Some may not be concerned with cost (especially if their company is paying for it), and others may need to finish a program as quickly as possible.  Still others may be more interested in research as opposed to business/practical applications.

The point is, you need to find the program that is best for you, not me.  Find the program that meets your needs and interests.

I hope the above information helps you in that endeavor, wherever you go and whatever you do in your educational and career aspirations.