Rock stars. That’s what some people are calling data scientists. And right now, data science is the sexiest tech career field, at least according to LinkedIn in a recent report of top skills for 2017. Since the term was first coined in 2001, data science has come to mean a large group of activities which combine statistical analysis and data mining.
Anyone who is considering a career in data science needs to understand first, the myriad of things such a career involves, the type of education and training required, and exactly what the job market holds. And because the field is growing so fast, students and mid-career professionals both have an opportunity to move into data science careers, if they get the right education and training.
The Field is Broad and Varied
There is no single definition of data science, as it varies with industry, specific business, and what the purpose of the data scientist’s role is. And different roles require different skill sets, therefore the educational and training path is not uniform. Data scientists can come from many fields “ math, statistics, computer science, and even engineering. But the role the scientist is to play is now generally broken down into two large categories:
- Type A: Data science for people “ data collection and analysis to support decision-making based on the evidence
- Type B: Data science for software “ for example, the recommendations one might get for books or movies from Amazon or Netflix, based upon past behaviors
It is a definite prediction that these two categories will be further fragmented as the field continues to expand. But for all data scientists, there are certain broad skills that must be mastered:
Common Skillset
1. Problem-Solving
This is the very core of all science. No matter what tools, technology and or techniques related to statistical/machine learning arise, they are just that “ tools and technology to solve problems. And how do people learn to solve problems? Only by experience. Problem-solving skills are mastered by doing. In fact, many data scientists state this is the only skill to be concerned with “ all else is secondary.
2. Statistics and Machine Learning
Both of these complex fields must be mastered if anyone intends to solve data science problems. Machine learning is a field that grew from AI and statistics. But there is no machine learning without statistics, so the two are inextricably wed.
3. Computer Skills
- Programming: This is an obvious must. A data scientist cannot apply any theory if s/he is unable to code unique algorithms and build statistical models.
- Distributed Computing: This is the ability to work with big data, and no data scientist can now cut it without being able to train models over many multiples of virtual machines. While not all businesses have huge datasets, it is only a matter of time. Mastering parallel algorithms come with the territory.
- Software Engineering: This will not be a necessary skill set for Type A data science. But, you will need strong mastery if you plan to be a Type B scientist, dealing with ML and algorithms.
3. Data Cleaning and Prep
Data quality is a constant issue, and businesses have complex data storage infrastructures. You will not be working with clean datasets and you have to be willing to do the dirty work “ otherwise, your models will not be accurate and bad decisions will be made. All of your technical know-how will be useless without this critical activity.
4. Communication/Business Acumen
Operating in data science means operating in an environment of people, interacting with those who are not techies and do not understand the details of your job. They only understand results. They will provide you with problems, you will mine the data, provide the analytics, and make recommendations for action.
Learning
You have lots of options when it comes to mastery of the skill sets you need. But before you embark upon your learning, one thing must be understood. Just like doctors, teachers, accountants, etc., there are always new developments in data science, and these come quickly. You must be passionate about this field because you will never be able to stop learning. While there are no formalized methods of continued professional development, there is advanced college coursework, workshops, books, papers, etc. that you can tap into. Here is a short synopsis of options.
1. Getting the Basics
There are certain college majors that lend themselves to data science preparation “ math, statistics, computer science, operations research, even economics. And some colleges now offer programs geared to data science.
2. Grad Programs
Enrolling in a Master’s program is the next step, of course. This will provide the theoretical basis. Of course, they are expensive and they take the time to complete. This option, though, will provide a sequenced learning structure and also connections to potential employers who recruit on campuses.
Many data scientists have PH.D.’s. This, of course, provides not only theory but a very practical culmination project “ that dissertation. And there is plenty of help and dissertation writing tips out there that will ease the pain that former dissertation writers experienced. A Ph.D. makes you far more employable.
3. MOOC’s:
This is free coursework which is both theoretical and practical, and if you pick the right sequences of coursework, you can gain the skill sets you need.
4. Bootcamps:
These are usually taught by practitioners in the field, and the practical experience can be invaluable. These are usually accelerated, and specific projects are built into the curriculum. Sometimes, employment can come from these.
The Job Market
There is no doubt that the field of data science is exploding. Here are a couple of stats that can be pretty motivating:
- Demand in the field is expected to grow by 50-60% by 2018. This will mean a shortage of somewhere between 140,000 “ 190,000.
- Data science has been ranked the best job in America “ high earnings potential, high demand.
There are also some hurdles:
1. Bias Toward Experience: Unfortunately, even though the field is under-staffed, companies still prefer to employ data scientists with some commercial experience. They tend to want them up and running quickly, and they are also a known quality.
But there are more enlightened employers who look first for problem-solving abilities and test that during the interview process. A newbie has a shot in this type of selection process.
2. Openings for Junior Positions: There are more of these than senior ones, obviously. For a recent grad who is young and looking for his/her career opener, this is ideal. For the older entrant into the field, however, this can be a challenge. Generally, despite all of the talk of no age bias, there is. If an individual is to be brought into a junior level position on a team, companies tend to want them younger in age.
3. Lack of a Portfolio: If you don’t have projects to point to, then you are a data scientist in theory only. For this reason, you may want to do some freelancing, contribute to open-source projects, or participate in Kaggle competitions. Kaggle is a community of data scientists who compete with one another to solve problems of data science. Results are provided to businesses of all sizes and sectors. Scoring high in these competitions can land a position.
Conducting That Search
The First steps will be the obvious ones. Prepare your resume and include links to your LinkedIn profile and to any source where your work can be seen. Maybe you have written blog posts that have been published; perhaps you participated in an open-source project. Anything that can point to your skills beyond your just stating them will be important. Beyond these steps, a here area other considerations:
- Networking: Join groups on LinkedIn; join Meetup; participate in Kaggle competitions and stay in touch with colleagues with whom you worked in open-source environments. Prepare a 30-second elevator pitch that you can use whenever anyone asks, What do you do? you never know who is on the receiving end of that pitch.
- College Career Office: Stay in touch even though you have graduated. Attend job fairs if there will be attendees who may be looking now or in the future for data scientists.
- Recruiters: Get your paperwork out there to as many tech recruitment pros as possible. And it doesn’t hurt to touch base with them every so often to keep your name fresh in their heads. Set up meetings with them if possible, and hand your documents over personally, as well as digitally. Many recruiters who place high-level, high-salaried people actually insist upon meeting with promising applicants.
- Niche Job Boards: Stalk these relentlessly. Simply Google data science job boards, and you will find 7-8 immediately.
Conclusion
Data science is not a field to move into without strong passion and patience. Mastering the skills for this career field will require time, commitment, and persistence. Don’t go into this field for the good money “ you will fail. Go into this career, because you have a passion for problem-solving and you get amazing satisfaction when you do solve a problem. When this type of activity brings such focus that you lose track of time, you know you are in the right field.
