Data Science Jobs Preparation Guide

Data science jobs preparation guide is to help people aspiring to take up jobs in data science.

You have heard that already, haven’t you? – ‘Data Science is the New Gold’,  the hype surrounding data science, the awesome projects emanating from the field and the mouth-watering pay or salary associated with this field of career is tempting enough to make people aspire or switch career to data science. Data science popularly called the ‘New Gold’ is the toast of small and multinational companies who now rely on insights from data to make decisions in order to be ahead of the game. Data is been generated in our 21st century in billions more than any other time in history due to frequent interactions between people and technological devices.

Data science jobs are now opening and it is paramount to know or get prepared for possible areas employers look out for in hiring data scientists so as to help you scale the interview. Here is a list of prerequisite areas:

Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL.

Foundational Knowledge in Basic Statistics: You don’t need to study statistics or have an in-depth understanding of statistics, but you need a basic understanding of statistical terms like Hypothesis, Mean, Median, Standard Deviation, Range, Regression, P-Value e.t.c will help you better understand somethings you may come across in data science. Consider taking an online crash course in basic statistics.

Data Cleaning or Wrangling: Interviewers or recruiting companies sometimes give you access to their unstructured data and expect you to clean it up; arranging it in neat tables and removing outliers. Demonstrating that you have what it takes to perform thorough cleansing their data is a signal that you worth been employed.

Data Visualization: You would be expected to work on a dataset and communicate your findings to the Management in pictorial or graphical formats. Since most people find it time-consuming reading figures, an alternative is to have the skill to put the figures in charts or graphs that can easily be visualized and understood. Therefore, knowing data visualization packages is very important.

Communication Skills: This skill is often overlooked by most aspiring data scientists, yet data manipulation or visualization is not an end in themselves. Most times a data scientist will be required to communicate findings to the board of directors, the marketing or the product development team. Therefore, knowing how to communicate well is an area to improve to land a data scientist job.