Data Science- The Sexiest Job of 21st Century

Data Science has seen an exponential growth becoming the second-fastest growing profession according to Linkedin.

Many companies aren’t sure what to do with their data science teams, however, they hire a whole team of data scientist, wasting their investment.

Data Science is essentially a method of viewing and analyzing statistics and probability. Many people consider statistics and analysis of data boring. However, it is one of the jobs in which the more you learn, the more you want to learn. The thing that makes data science sexy is that you see it everywhere and used in an almost infinite number of ways.

Core Competencies of a data scientist

Today, the data scientist is not only about performing mathematical calculation or statistics but requires knowledge of a broad range of skills such as the ability to gather data, analyze and present information.

Data Capture

Data capture is an important step for a data scientist. As the term suggests it is basically an act of capturing data by collecting it from different sources using database query language such as SQL.

Raw data isn’t useful in many cases, a data scientist must also understand the data domain so that he can formulate sorts of questions to ask by looking at the data. Knowledge about data modeling and understanding of how the data is connected and whether or now the data is structured is needed to analyze the data concisely.

Analysis

This step is done once the data is captured. It is basically about understanding the complexities of the data. It’s more about performing some basic analysis at first and then trying specialized math tricks and algorithms to make patterns in the data more obvious to draw conclusions that one can’t draw by reviewing the data alone.

Presentation

Stakeholders and people from non-tech background don’t understand numbers well. They can’t see the patterns that the data scientist sees. A graphical presentation of these patterns helps different stakeholders visualize what the numbers signify and what meaning do they hold. It’s important for the data scientist to convey a specific story through the presentation.

What Should I Learn to be a Data Scientist

A data scientist should be familiar with several programming languages to achieve different tasks required for data science. SQL knowledge might be needed to extract data from relational databases. Python can be used to perform data loading, transformation, and analysis tasks. MATLAB or PowerPoint to present information.

It may not be possible to rely on one programming language to carry all the task but Python has been widely used throughout the data science community to load data, transform it, analyze it and even present it to the end-user. A data scientist may have to choose other languages to fill their toolkit.

Most popular languages which are used across this filed is Python, R, SQL, Java, and Scala.

That’s it for now. If you find this interesting, check out my other articles.