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Who is a Data Scientist?

Big data has been the prevailing term in the business world in this century. With it comes the need for professionals capable of making sense out of it. That’s where the data scientist comes in.

Globally, about 200,000 to 1,000,000 people are involved in data science related work.
To understand who a data scientist is, let’s first have a look at why data science has become so popular.

Why has Data Science Gained Popularity?

Data in this era has become so expansive that it’s no longer possible to make sense of it using the limited traditional software like Excel.

Besides, it’s no longer a reserve of the academics to analyze data using Stata or SPSS. Data science has now become part and parcel of the business world. Industries ranging from marketing optimization, agriculture, fraud detection, risk management, public policy, marketing analytics, and everything in between rely on data.

Data science makes sense of data through data preparation, predictive modeling, statistics, machine learning, and others. The goal is to resolve issues in individual sectors or even the economy at large.

Application of data science impacts multiple aspects of the economy and society, including machine translation, the digital economy, speech recognition, social science, healthcare, medical information, and many more.

It’s not just limited to the growth of brands, but applies to the entire business process, from initial planning, development, product enhancement, and customer retention to business expansion, and so much more.

Considering the vast expanse of advanced tools that businesses rely on, it’s no surprise that data scientists play a pivotal role. Such tools include:
  1. Google Analytics for marketing departments
  2. Advanced ERPs like SAP and Microsoft Dynamics used in breaking down information and creating visualizations
  3. Business intelligence software like Tableau, Sisense, and Microsoft Power BI
  4. Improved programming languages like Python and R useful in simplifying the complex analysis.
Businesses that utilize such tools require the help of data scientists. Otherwise, they can't make sense of the data.

Who is a Data Scientist?

In simple terms, a data scientist is a new breed of an analytical data expert possessing the technical skills to solve complex problems, as well as the curiosity to explore new problems and their solutions. Such a profession is the combination of a computer scientist, mathematician, and trend-spotter.

They possess competencies relevant to the IT world, plus an understanding of business processes. Due to the pivotal role that data science plays in modern companies, such professionals are currently in high demand.

The job goes beyond mere statistical analysis.

They aim to glean useful information from tons of structured, semi-structured, and unstructured data collected and produced by companies. That’s possible using advanced analytics technologies like predictive modeling and machine learning.

Businesses value data scientists for their ability to detect trends, identify patterns, and uncover relationships in data sets. Working in teams, they identify business risks and opportunities and predict customer behavior. All of that is achieved by mining data using analytics and reporting tools.

Considering the complexity involved in these operations, a data scientist must have the ability to assess technical predictive models.

Roles and Responsibilities of Data Scientists

Considering the innate aspect of discovery and need for curiosity in being a data scientist, the particular responsibilities of such a profession can vary greatly. It’s not the kind of job that has fixed routine involving predictable tasks.

Generally, these are some of the responsibilities you’ll be involved in:
  1. Collecting vast quantities of unruly data and then transforming it into usable formats
  2. Using data-driven techniques to solve business-related problems
  3. Utilizing multiple programming languages, like R, SAS, and Python
  4. Making use of statistical tests and distributions
  5. Keeping up to date with analytical techniques like deep learning, machine learning, and text analytics
  6. Collaborating and communicating with IT and business
  7. Searching for order and patterns in data, plus spotting trends to help the business’ bottom line

Salary and Demand of Data Scientists Across the World

You can expect an average yearly salary of $106,000 as a data scientist, according to the Dice Salary Survey.

Such a high salary reflects the high demand for such professionals.

For instance, the US has a shortage of 151,717 data science professionals, according to a 2018 report by LinkedIn. Since demand outstrips supply, people skilled in data science have access to multiple job opportunities, and employers offer tantalizing remuneration packages to get and retain those professionals.

Another report by Indeed shows that data science professionals are gradually on the rise since this lucrative career path has received increased hype over the past six years. However, even with the increased numbers of college students taking up data science courses, the demand is yet to be fully met by the supply.

A 2016 report by Huffington Post presents the supply-demand gap globally, revealing that about 1.5 million to 3 million data scientists exist worldwide. That’s in comparison to the higher demand of about 1.69 million to 3.19 million data science jobs in companies.

To fill that gap would require more students than the current numbers taking up data analytics and data science courses. Students can also come from adjacent majors like statistics and computer science.

Courses to Take to Become a Data Scientist

Due to the growing need for data scientists, taking it as a career path is a smart move.

Many more businesses realize the need for managing data, and this is expected to grow in the future. Not only will you get many job opportunities and good salaries, but you’ll also have the chance to experiment and be creative at work.

To get into this profession, you need the necessary training as well as hands-on experience in the particular industry you wish to work in.

That can be summarized into these three basic steps:
  1. Earn a bachelor's degree in computer science, IT, physics, math, or a related field
  2. Earn a master's degree in data or a related field
  3. Gain experience in the particular area you intend to work in, such as physics, healthcare, business, or others
Since you’ll work in improving business processes, you also need an understanding of marketing, business, and people. Therefore, you can complement the data science degree with a minor in either of these areas: psychology, business, political science, and various liberal arts degrees.