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.
Got a question you would like answered? Let me know in the comments.
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?
Andrew Ng, co-founder of Coursera and Deep Learning Expert, is launching a new specialization on Coursera. Details can be found at DeepLearning.ai or the Deep Learning Specialization Page. The specialization consists of 5 courses. They are free to audit and watch the videos. There is a fee to get graded assignments and receive a certificate of completion. The first course just started this week, so it is great time to start learning some deep learning.