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.
If you work at a university and are considering starting an undergraduate program in data science, then today’s post is for you.
- A Guide to Teaching Data Science  – focuses on increasing 3 skills (create, connect, compute) within Statistics departments to develop data science
- Teaching the Foundations of Data Science: An Interdisciplinary Approach  – A study and analysis of teaching an introductory course on data science with a cooperation between MIS and CS.
- A Data Science Course for Undergraduates: Thinking with Data  – Overview of an undergraduate course in a liberal arts
environment that provides students with the tools necessary to apply data science. very detailed many great topics plus R and SQL
- Embracing Data Science  – Statistics needs to learn from data science to make courses more relevant
If you know of any other papers, please leave a comment below.
Karl Schmitt, Director of Data Sciences at Valparaiso University, has started a blog to share his experiences with building an undergraduate data science program. The blog is titled, From the Director’s Desk. Karl is regularly posting about textbooks, curriculum, visualizations and learning objectives from the perspective of an educator. Tons of great resources!
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)
Today, we are lucky to have Daniel Levine of RJMetrics provide a guest post. RJMetrics created an extensive report detailing The State of Data Science. I asked Daniel to provide some results as they relate to the current education of data scientists.
Recently, RJMetrics released a benchmark report that looked to answer many of the questions people have about today’s data scientists, such as how many data scientists are there, what degrees do they have, and what skills do they posses.
From LinkedIn data on the 11,400 data scientists working now, we can get a much better sense of what types of data scientists companies are hiring, and how senior data scientists differ from their junior counterparts.
While it was typical to see data scientists report multiple degrees, when we looked at the percentages of all distinct bachelor’s, master’s, and doctorate degrees, we found that 42% finished their education with a master’s.
The high number of data scientists that receive graduate degrees (79%) is indicative of the increasing demand for specialists and a desire from data scientist for advanced training.
So what does this distribution look like as you climb the corporate ladder? You may assume that the higher the position, the more PhDs; but in fact, across Junior, Senior, and Chief Data Scientists, we saw the highest ratio of PhDs to Master’s at the Senior level.
We speculate that the drop from 43% at the Senior level to 35% at the chief level actually reflects how long those individuals have been in the field. In a study by Heirick & Struggles titled, “Understanding Today’s Chief Data Scientist,” they found that chief Data Scientists “average nearly 15 years of post-degree commercial (PDC) experience.” What we’re likely seeing in this data is the “first crop” of Chief Data Scientists who earned this title in the field, not in the classroom.
When we looked at what data scientists studied during their education, we found that besides Business Administration/Management, they were mostly STEM-focused.
We believe that Computer Science is so popular because a data scientist without CS skills is at an extreme disadvantage because they won’t be able to extract the data well enough to properly analyze it. DJ Patil and Hilary Mason, in their book Creating a Data Culture, went as far as to say, “a data scientist who lacks the tools to get data from a database into an analysis package and back out again will become a second-class citizen in the technical organization.”
In analyzing 254,600 records of skills, we found the most popular skills to be more generic than we’d expect. Popular buzz term like “big data” and “hadoop” didn’t crack the top 10, while programming languages like “r” and “python” are extremely popular among data scientists.
When the data was sliced by seniority, we saw a major difference between Junior, Senior, and Chief levels. To make these differences easier to digest, we compared each level to the same common denominator: the average data scientist.
Again, the chief data scientists data is of particular interest. These C-suite professionals are more likely to list skills like “business intelligence,” “analytics,” “leadership,” “strategy,” and “management” among their skills than both junior and senior data scientists; but less likely to list skills on the more technical side, like “python” and “r”.
While it’s true that chief data scientists may be simply emphasizing skills that are more relevant to their position within the company, we also speculate that many chief data scientists assumed these roles by virtue of being in the field longer or having additional qualifications, such as a business degree. Therefore, it is also possible that some chief data scientists never actually learned many of the skills listed by more junior people.
If you’d like more analysis about this data and a more detailed explanation about our methods, you can check out the full State of Data Science.
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.
Sound appealing? Probably not! Unfortunately, this is the sad reality for many children in Sub-Saharan Africa. Even worse, this sad reality is only for those children lucky enough to even attend school. In the world today, there are 58 million out of school children, and 43% of those children will never start attending school.
FFunction, a Montreal-based data visualization studio, and UNESCO Institute for Statistics (UIS) recently launched 2 interactive data visualizations. Both are creative and innovative ways to present information.
- Out of School Children – Explore how gender, income, and location affect a child’s education
- Left Behind – View how and why African girls struggle to obtain an education
For more on the topic, see my entire guest post on the DataKind blog, Data Visualization for Good – Education in Africa
DataQuest is a recently launched online data science learning platform for python. The site consists of a gamified series of missions that increase in difficulty as your skills progress. Here are a few other features of the site.
- Sample Code
- Live, Interactive Browser-based Coding Environment
- Step by Step Instructions
- Instant Feedback
- Helpful Forums for Q&A
The site is still under development and the founder, Vik Paruchuri, is looking for help developing more content and missions for the site. If that is something of interest to you, get in touch with Vik via the DataQuest website.