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?
This is not intended to be mapped to a set of college courses. It is intended to be a listing of necessary skills for a data scientist. For a definition of data scientist, see this previous post.
- Calculus – not directly important to data science, but the knowledge is important to understand the statistics and machine learning
- Matrix Operations
- Regression – Linear and Logistic
- Bayesian Statistics
- R – stats
- Octave – machine learning
- Basic Programming – Java, C/C++, and Python seem to be good language choices
- Machine Learning
- Database Knowledge – not limited to just relational databases
- Data Visualization – how to make data look good: maps, graphs, etc
- Presentation – story telling, be comfortable explaining data to others
Do you have anything to add/remove from the list?
Please spread the word about why data science is important. If you are excited, others will be too. If you are not sure what to say, here is a list of possible topics.
What can you tell people about data science?
What are some other things you could tell people about data science?
STEM stands for Science, Technology, Engineering and Mathematics. Due to the difficulty of STEM degrees, it appears many students abandon the degrees in college. While this fact is not surprising, it is still concerning. Our country and world need more good people with STEM skills.
A STEM degree is not essential to becoming a data scientist, but many data scientists have STEM backgrounds. Thus, I thought this information fit well with the Data Science Education Week theme.
How do we convince students to not abandon the STEM degrees?
One solution is to put less emphasis on grades. Grades in STEM courses are typically the lowest on campus, and this causes some students to switch degree programs in order to get better grades. Second, tell young people about some of the cool STEM projects available. Lots of people in Science and Math work on really interesting projects. If you can, tell the world about your projects.
What are some other ways to keep students in STEM programs?
Below is a nice infographic with various numbers about STEM students.
Thanks to Online Engineering Degree for the infographic.
Coding (a.k.a. computer programming) is not the primary function of a data scientist, but some coding skills are necessary. Modifying machine learning algorithms or scaling/altering data are both good examples of when writing a few lines of code could be very beneficial. Well, if you have desire to learn to code, then there is no time better than the present. A handful of companies have recently launched products that will help with just that task.
- Udemy – not specific to coding, but there are many computer programming classes available
- Code School – The courses here are focused on web development. If you want to learn the ruby programming language and eventually Rails, this may be a good place to start. Plus, you can currently get access to all courses for $25 per month.
- Code Lesson – Courses are not free, but the range of courses is nice. Also, the courses are structured to fit the evening/weekend schedule. Update: CodeLesson does offer free courses, see here.
- Codecademy – Probably the most interesting site on the list. If I did not know how to code, I would probably start here.
- Coursera – Soon they will be offering CS 101. I have not seen a syllabus, but it may serve as a good resource for learning to code.
- Of course, there is always the option to go to college. Nearly every college or university offers at least a class or two about programming. This is probably the most expensive route, but if you thrive in a classroom setting, then this is a good option.
With all the options available, there are others too, 2012 might be the best year ever for learning to code.
Are you aware of other sites devoted to helping people learn how to program?
OpenIntro is an organisation that was started to create a free and open source introductory statistics textbook. The book is available as a free PDF download, or it can be purchased in paperback from Amazon for less than $10. If you want to learn statistics or need a little refresher, check it out.
Data Science Courses
This is a nice collection of data science related courses offered at various colleges and universities. It is on a wiki page so you are free to add links.