- 16th April 2018
- Posted by: Manolis
Yesterday LinkedIn released its list of top skills for 2017.
Statistical analysis and data mining retained the second spot they held last year while data presentation entered the top 10 for the first time.
Both of these skill categories fall under the heading of data science, a term coined in 2001 by William S. Cleveland, a Professor of Statistics at Purdue University, when he advocated for the merger of computer science and statistics.
What Data Scientists Do
Data scientists utilize their knowledge of statistics and modeling to convert data into actionable insights about everything from product development to customer retention to new business opportunities.
The field has experienced tremendous growth. Check out these trends dating back to 2011.
• Demand for deep analytical talent in the U.S. projected to be 50-60% greater than supply by 2018, leading to a shortage of 140,000 to 190,000 people as well as 1.5 million managers and analysts.
Because the field is growing so fast, employers are challenged to acquire talent with previous experience, creating an opportunity for college students and mid-career professionals to develop these skills and get hired into a position.
Skills Needed To Succeed
Data scientists also possess a unique combination of technical, analytical, and presentation skills, making them hard to find.
They understand statistics and applied mathematics. They can test hypotheses with experiments they design. They know enough programming to engineer methods for sourcing, processing, and storing their data. And they communicate their findings through data visualizations and stories.
Some of the languages and applications they use are SQL, R, Python, SPSS, Tableau, and Hadoop.
Common Ways To Enter The Field
The majors that lend themselves to data science are statistics, mathematics, economics, operations research, and computer science. Some schools have even started to offer specialized programs tailored to data science.
Most of the data scientists I know completed PhDs, although it’s not required.
There are three common pathways for those who want to make a transition.
First, enroll in a master’s program. The university setting grounds students in the underlying theory. It’s the most expensive and most time-consuming option but also provides structure and connects students with employers that recruit on campus.
Second, leverage MOOCs. The self-taught approach is the scrappiest, although finding projects through which to apply learning and an eventual job rests entirely on the student.
Lastly, participate in a bootcamp. Typically taught by practitioners in an accelerated timeline, this approach relies on experiential learning, so projects are built into the program to reinforce learning and staffing managers have relationships with employers to help place students at companies.
Be careful to differentiate data scientists from the related roles of data engineers and data analysts.
Springboard provides a useful synopsis, stating that data engineers rely more on engineering skills, data scientists rely on mathematics and statistics, and business analysts rely on communication skills and domain expertise.
Tips For Making The Switch
• Start where you are by injecting data into whatever work you currently do
• Develop your skills using any of the many free resources available
• Search for projects to apply your learning
• Pitch your current company on a transfer once you have a portfolio of work to showcase
• Possibly go back to school for a degree program or join a bootcamp after exhausting resources available online
• Find a mentor, ideally someone who has made a similar transition