MIT has recently launched Statistics and Data Science MicroMasters program. The program is a series of online MIT graduate courses offered via EdX. It officially starts in the fall of 2018.
Coursera has partnered with the University of Michigan to launch a new master’s degree entirely online through the Coursera platform. The new Master of Applied Data Science degree will launch in Fall of 2019.
Also of interest, Arizona State University and the University of Illinois at Urbana-Champaign both announced masters degrees in computer science (both with data science portions) through the Coursera platform.
Awhile ago, I recorded a Facebook Live on Tips for Data Science Students. It goes along with the following post: Getting the most from your Data Science Masters Program.
Enrolling in a master’s degree program in data science or business analytics is no small feat. It takes a lot of time, determination, and money. It can all be worth it as a more fulfilling and higher paying job might be in your future. However, just earning the degree does not guarantee a job in the future. Here are a few tips to maximize your master’s degree experience and enhance your chances of landing that great job.
Create a Project
This one is big because it helps with all the other tips. Pick a project that is unique to you. It should be interesting and fun. There are tons of open datasets available. The project can be any topic from something big like world education to something smaller like your own coffee consumption (for some of you that might not be small). All that matters is that it involves some data and you work on it. The project will help you learn new things and determine what is enjoyable. It will even give you a good discussion topic for future job interviews.
Determine the portion of data science you enjoy
Is it visualization, programming, modeling or something else (see Getting Started with Data Science Specialties for a list of specialties)? Then tailor as much of your program around that as you can. You will excel more at things you enjoy, and data science needs teams not individuals who think they can do everything.
Attend local meetups or conferences
Depending upon where you attend school, this might be easy or difficult. If your local area does not have a data science group, start one.
If you are ever offered the chance to speak to a group, take it. Whether it is a class, local club, church group, or a backyard barbecue; take advantage of the opportunity. Many people are not good at this skill, and practice will only make you better. Also, university settings are great places to practice. They are safe environments and the worst that is going to happen is a not perfect grade. Don’t wait until the stakes are high to begin your practice.
Make yourself visible to the data science world.
Share the slides from your presentations. Better yet, share the video if available. Make sure when a prospective employer searches for you online (and they will), they can easily see a trail of artifacts that demonstrate your interest in data science. You should probably have a presence on some of the following (you do not need them all): LinkedIn, Twitter, Instagram, Quora, Stack Overflow, GitHub, Youtube, Slideshare, Speakerdeck.
Find some local data science people in your area and connect. Offer to join them for coffee or lunch. Attend their presentations and get to know them. This can be others learning data science as well as more seasoned experts.
What others tips do you have for those currently enrolled in a data science masters degree program?
Georgia Tech University just announced a new online master’s degree in Analytics.
The degree will begin in August 2017 and will be fully online. It will offer 3 tracks:
- Big Data
- Analytical Tools
- Business Analytics (coming at a later date)
The Data ScienceTech Institute (DSTI) in France is starting 2 new master’s degree programs in data science. Both programs are highly innovative and offer a strong industry focus. Classes begin in October 2015, and each program is limited to 30 students. Therefore, if you are interested, it is important to apply as soon as possible.
The other day, the faculty at DSTI were announced. I am honored to say I was selected as one of the faculty. Thus, I will serve as a visiting faculty member for portions of the program.
DSTI offers 2 master’s degree programs:
- Data Scientist Designer – Located in Paris, this 2-year program is part-time and focused on working professionals looking to transition or enhance skills in the data science field. The course will rotate between 2 and 3 days a week.
- Executive Big Data Analyst – Located in Nice along the French Riviera, this program is a more traditional intensive 16-month program targeting full-time students.
If you are in France or Europe or interested in studying in France, the programs from DSTI are definitely worth a look.
I was honored to write a guest blog post for Master’s in Data Science. The site contains a very detailed list of graduate programs in data science. The post I authored is title:
Not to ruin the post, but the 3 questions are:
- What is my Background?
- What are my goals?
- Does location matter?
Head on over to Master’s in Data Science to see all the details about why those are 3 important questions.
The University of Virginia is in the closing stages of creating a Master of Science in Data Science (MSDS) and the eventual goal is to have an undergraduate minor and a Ph.D. program in Data Science. The curriculum for the MSDS contains a nice mix of math, computer science and statistics courses. It even includes coursework in visualization. Also, the program appears to be an entirely new program and not just the renaming of an existing program.
The University of Virginia is definitely taking the correct steps to become a recognized leader in data science education.
The University of California at Berkeley just announced a new masters degree in Information and Data Science (MIDS). The program is targeted to be completed entirely online with the exception of a one week visit to the campus. The program has a approximate cost of $60,000 for the 27 required credits. The curriculum looks good. It includes: machine learning, data analysis, visualization, big data processing, and privacy/ethics. The initial class of students will start in January of 2014.
Due to the large list of Colleges with Data Science Degrees, I receive a number of email inquires with questions about choosing a program. I have not attended any of the programs, and I am not sure how qualified I am to provide guidance. Anyhow, I will do my best to share what information I do have.
Originally, the list started out with 5 schools. Now the list is well over 100 schools, so I have not been able to keep up with all the intricate details of every program. There are not very many undergraduate options, and the list only contains a few PhD programs, so the information here will be focused on pursuing a masters degree.
Start by asking 2 questions:
- What are my current data science skills?
- What are my future data science goals?
Those 2 questions can provide a lot of guidance. Understand that data science consists of a number of different topic areas:
- Mathematical Foundation (Calculus/Matrix Operations)
- Computing (DB, programming, machine learning, NoSQL)
- Communication (visualization, presentation, writing)
- Statistics (regression, trees, classification, diagnostics)
- Business (domain specific knowledge)
After seeing the above lists, this is where things get cloudy. Everyone brings a different set of existing skills, and everyone has different future goals. Here are a few scenarios that might clear things up.
The most common approach is to attempt to build knowledge in all 5 topic areas. If this is your goal, find the topic areas where you are weakest and target a graduate program to help you bolster those weak skills. In the end, you will come out with a broad range of very desired skills.
A different approach is to select one topic area and get really, really good. For example, maybe you want to be an expert on machine learning. If that is your goal, then maybe a traditional computer science graduate program is what is best. In the end, you will be well-suited to be an effective member of a data science team or pursue a PhD.
A third and also common approach is from people that want to help fill the expected void of 1.5 million data-savvy managers. These people do not necessarily want to know the deep details of the algorithms, but they would like an understanding of what the algorithms can do and when to use which algorithm. In this case, a graduate program from a business school (MBA) might be a good choice. Just make sure the program also involves coverage from the non-business topics of data science.
I think NYU is the best example of a school that can help a person achieve just about any data science goal. The NYU program is a university-wide initiative, so the program is integrated with many departments (math, CS, Stats, Business, and others). Therefore, a student could possibly tailor a program to reach a variety of future goals. Plus, New York has a lot of companies solving interesting data science problems.
There you have it. It does not narrow the choices down, but it should help to provide some guidance. Other factors to consider are length of a program and/or location.
Good Luck with your decision, and feel free to leave a comment if you have and good/bad experiences with any of the particular graduate programs.