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?
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Thanks for the awesome article Ryan!
My name’s AJ and I’m a Data Science Engineering student at the University of Michigan who’s doing a deep dive this year by applying Tim Ferriss’ accelerated learning framework (for more info: http://www.businessinsider.com/tim-ferriss-disss-system-to-learn-anything-2015-3) to Data Science.
Your article highlighted the main takeaways I’ve gotten from online research very nicely. I especially appreciated your point about starting with a project to determine (and then focus in on) the part of Data Science you most enjoy. This is a sentiment echoed strongly by DataQuest’s founder Vik Paruchuri in his article “How to actually learn Data Science” (https://www.dataquest.io/blog/learn-data-science/).
In the coming weeks I’ll be taking much of your advice — joining local Ann Arbor meetups, attending big conferences, creating a blog to share my learning experiences, competing in Kaggle competitions, dropping $1000 or so on a Springboard bootcamp, and much more.
If you have 15 minutes to help a student out, I’d love to connect to ask a few follow-up questions about these recommendations, as well as best learning strategies, common novice mistakes, favorite resources, etc? Even just a brief email exchange or phone call would be super valuable in steering my learning.
Thank you again for writing this and I appreciate in advance any additional time you’re able to offer.
All the best,
Thanks, Yes Vik is great. Anyhow, I will followup with an email.
Great post !!
I have some problem in downloading data sets of tweets for prediction in research .plz help me.
Stackoverflow is a great resource for help. If you ask a clear question, it will be much quicker to respond than I am.
[…] 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. […]
Thank you.its a nice blog
Very informative blog for us.Thanks for sharing.
Thank you for sharing this insightful article on getting the most from a data science education program. As someone interested in the field, I found your tips and recommendations extremely helpful.
I particularly appreciate your emphasis on practical experience and hands-on projects. Theory is important, but it’s through practical application that we truly deepen our understanding and develop the skills necessary to excel in data science. Your suggestion to actively seek out real-world problems and datasets is spot on.
I also agree with your point about the importance of collaboration and networking. Data science is a multidisciplinary field, and working with others who bring diverse perspectives can lead to innovative solutions. Building relationships with peers, mentors, and industry professionals can open doors to new opportunities and help accelerate our learning.
you are welcome
Thank you for sharing this insightful blog post on getting the most from a data science education program. As someone interested in the field, I found your tips and recommendations extremely helpful.
I completely agree with your emphasis on hands-on experience. Theory is undoubtedly important, but it is through practical application that we truly solidify our understanding and develop the skills necessary to thrive in the field of data science. The suggestion to work on real-world projects and collaborate with industry professionals is excellent advice. Not only does it provide valuable experience, but it also helps build a network and exposes us to different perspectives and challenges.
I appreciate your emphasis on continuous learning. The field of data science is constantly evolving, and it is crucial to stay updated with the latest tools, techniques, and advancements.