Why There Is A Demand For Proficient Data Scientist And How The Data Science Course Looks Like?

By Jyoti Nigania |Email | Dec 19, 2018 | 34161 Views

With college students just recently heading back to campus, for many of them it's time to give a long hard look at the career path they choose to pursue and for many of them, that career is data science.
It's not surprising. An article about data science degrees referenced a study conducted by Forrester Research that determined that most companies are only using about 12% of the data they have on hand, yet data is fast becoming the lifeblood to effectively running a  business these days.
There's clearly a shortage of data scientists to help companies use more of their data, so pursuing a career in the field puts today's students at a distinct advantage when it comes to staying away from the unemployment lines after graduation. Data science is fast becoming one of today's most in-demand careers, and in fact, the prescient Harvard Business Review declared data science as the sexiest career of the 21st century six years ago.

What Does The Core Curriculum Look Like?
For students majoring in data science, a master's degree or higher is becoming a business necessity, given the increasing complexity and innovation taking place in the space. But for students beginning their college careers in undergraduate studies, the path to earning a data science degree has always included courses such as mathematics, statistics, computer science, engineering and, of course, programming languages such as Python.
In order to become proficient in today's particular artificial intelligence (AI) and machine learning-driven marketplace, key new classes may provide training in the following:

Hadoop platform: With the volume of data required for today's solutions, courses in Hadoop help budding data scientists learn how to quickly convey data to various points on a system and perform data exploration, data filtration or data sampling.
Apache Spark: Just as with Hadoop, this is a big data computation framework that helps run complicated algorithms faster.
Machine learning: Many universities offer courses on machine learning, specifically natural language processing (NLP), deep neural networks and computer vision.
Data visualization: To learn how to visualize data through new tools such as PowerBI, courses in data visualization help students learn how to take data and turn it into actionable insights to solve specific business problems.

How Is It Changing?
Despite the continued need to stay up to date on the latest programming languages, we believe that within the next five years or so, most of the code that's written will be created by machines. Machines may not become independent from human programmers altogether, but they will certainly augment much of the work of humans. Machine-learning tools will become more and more efficient at assisting them.

So to meet a changing marketplace, where programming does not play the starring role it once did, today's students need to seek out specialized areas to make themselves more marketable and knowledgeable. For example, specializing in front-end development, mobile or database management provides them with a narrower space in which to dive deep into richer expertise.

As an example of the growing complexity and specialization required in the field, I recently created a proposal for a client seeking to build an AI program. One area of the document, on the tools and technologies to be used, included more than 30 different tools and technologies for this one engagement. Obtaining expertise in specific tools and methodologies can go a long way toward helping students become masters of specific ones.

Aside from studying the basic skills and specialization in specific ones, what is fast becoming the game-changer to becoming a really good data scientist is stepping away from the science.
Data science is all about human interactions teaching software to think like humans. In fact, Stanford University offers its computer science students classes in persuasion specifically, how to persuade consumers or customers to buy certain things, buy into your messages and then build those techniques into the software.

What's key to being an effective data scientist is knowing how software interacts with people, yet this type of skill isn't learned in a programming course or even a statistics course; it's often learned through more liberal arts-focused studies such as English, sociology, psychology or even history. It's wise for today's rising data scientists to become well-rounded, stepping away from the computer lab and experiencing the world through the eyes of the many different people who comprise it.

Also, as really anyone involved in the IT realm of business knows, it's no longer about simply understanding how the technology works at a given company but in truly understanding the business with all its challenges, goals and its mission. Today's data scientists need to have a firm grasp of the business as well as the ability to communicate and solve problems across departments.  This type of understanding can be gained through internship programs, work experience and in understanding specific industries such as retail, manufacturing or health care and then applying technology to help solve specific needs.

There are amazing new opportunities for students seeking careers in data science today, and it's expected that more will continue to pop up. By focusing on expanding human skills, along with the scientific ones, the next generation will be well-prepared to help software become human-like instead of the other way around.

Source: HOB