by Cindy Merilas, Data & Analytics
‘Data scientist’ has been dubbed the “hottest profession of the year” once again, according to job listing data. For the fourth year in a row, being a data scientist has been ranked the “best job in America”, with 6,510 current open positions paying on average a base salary of $108,000, according to Glassdoor’s 50 Best Jobs In America for 2019. Glassdoor’s criteria and selection process saw professions assessed and determined by weighing up three key factors: earning potential, job satisfaction and the number of job openings. Who knew being able to crunch data would become a skill so highly sought after? Apparently, it has been recognised for its potential for some time now. Demand for data scientists has been growing steadily since it was first dubbed “sexiest job of the 21st century” by Harvard Business Review back in 2012, and today there are more than 3,200 openings every month – a meaningful figure when one considers America’s unemployment rate was around 7 percent in July 2019.
Those companies hiring data scientists are not insignificant either: Amazon, Walmart and Apple are among the top 20 hirers of data scientists – signalling that for those seeking opportunities with “sure thing” companies, the opportunities are endless as a data scientist. In fact, IBM recently published a study that claimed the world needs 28 percent more data scientists around the world by 2020 to meet increased demand. According to LinkedIn, there has been a 56 percent increase in job openings for data scientists in the U.S. over the past year alone.
Almost every industry needs experts to analyse data and keep the tech world turning. In sectors such as finance and marketing, data analysts get paid to find gold nuggets of information in streams full of meaningless figures, while the automation, medical, agriculture and IT sectors also need a constant array of data scientists on hand to analyse consumption trends, employee data and more. Amazon for example recently hired a data scientist to figure out why healthcare costs for employees are constantly increasing but employee health isn’t improving overall. The opportunities within the field are endless.
According to those already working in the industry, to “make it” in the field one doesn’t necessarily need to start out as a data scientist. In fact science students with a philosophy background can be groomed to become data science experts, all that is needed is a ‘scientific mindset’. But getting the right qualifications certainly does help.
A Bachelor’s Degree in either IT, computer science, math, physics or a related field is absolutely required to become a data scientist, followed by ideally a Master’s Degree in data or a similar field. Following this, many opt for the more traditional University route to gain data science qualifications, however specialised training is becoming increasingly available online, making data science training very accessible to computer scientists and IT specialists worldwide.
Simplilearn’s data science with python course co-developed with IBM, for example, helps wannabe-data scientists master their data science and analytics techniques using Python programming, which is generally considered the best high-level, general-purpose programming language. In the online 68-hour course, students learn the essential concepts of Python programming and gain deep knowledge in data analytics, machine learning, data visualization, web scraping, and natural language processing, enabling them to kickstart a career in data science shortly after completing the course.
With dedicated mentoring sessions from a team of industry experts, Simplilearn’s interactive course uses Jupyter notebooks labs to lead students through four real-life industry-based projects in the domains of telecom, finance, etc, meaning that the lessons are applicable to the professional world. Skills gained throughout the online course include: the ability to perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave; expertise in machine learning using the Scikit-Learn package; and the ability to perform high-level mathematical computing using the NumPy package and its large library of mathematical functions, among other things.
Sutirtha Sahu, a student who took the course recently, said, “The course material is very well designed — starting with basics for beginners and then moving towards more advanced concepts. The tutors are very supportive, too, and try to keep the sessions quite interactive. They are always ready to repeat a particular idea until the majority of the learners understood the concept… Overall, a perfect experience and I recommend to learners who want to keep themselves updated with market requirements.”
The projects undertaken in this course add a great value to anyone. There are eight industry projects covered from Amazon, Walmart, Comcast, IBM, NYC 311, MovieLens, Stick Market and Titanic.
Recommended for anyone with a vested interest in the field of data science, for analytics professionals seeking to gain an understanding of Python, IT professionals interested in pursuing a career in analytics, or graduates looking to build a career in analytics and data science, Simplilearn’s course in data science is one surefire way to take advantage of the booming industry’s opportunities.