How to become a Data Scientist in 2021

Harvard Business Review rated it the “Sexiest Job of the 21st Century”.. Enough said! This Data Scientist Career Guide will help you explore the background, job functions and career prospects of a data scientist.

While we’ve all heard of data science, what does a data scientist really do? Well, they sure analyze lots of data and try to find meaning in it.

Who are Data Scientists?

They are the experts responsible for accessing data in the database or data warehouse of their organization, cleaning and formatting it, manipulating it, categorizing it and doing whatever it takes to extract useful conclusions.

Moreover, they build self-service decision-support systems, or feed this data to a machine learning algorithm that will learn from it and predict future results.

We will start by demonstrating the required background to become a data scientist, describe the most common job functions, and look into the prospects of a career in data science, so buckle up!

Background

The beauty of data science is that data scientists come from so many backgrounds.

While most come from computer science, math, engineering and economics backgrounds, there are many who started in psychology, business, sciences and other similar fields!

The skills in this field are truly versatile that even if you major in computer science or engineering, your degree will not equip you with the full stack of needed skills. Today, however, many educational institutions started offering data science programs for interested students.

So how do YOU become a data scientist?

Well, you can go get a Bachelor in Computer Science and cover any gap in skills, get an internship in the field and then join as a full time professional.

If this is not you, you can follow a learning path that can be something like this:

Step 1: Learn the Basics of Programming

A good start would be to learn a beginner-friendly language like python. It teaches you how to think logically and iteratively, and allows you to set foot on a technical career path.

Step 2: Learn the Fundamentals of Analytics

Data Science after all is the science of analytics! You need to build a statistics base for yourself if you want to make it in this field. Start by reading our free and simple guide on Fundamentals of Analytics.

Step 3: Learn Database Management and SQL

SQL is a necessity for the data scientist, as they have to extract, manipulate and analyze data, mostly starting with SQL. Since most of data is found in databases and data warehouses, you need to learn SQL to interact with those solutions. A good start is our free simplified Introduction to SQL.

Step 4: Learn Data Analysis with Python/R

After you’ve grasped the basics of programming, you need to learn a language that can be used for data analysis. The two most popular languages right now are Python and R. Both have numerous libraries and packages made specifically for data wrangling and data analysis.

Step 5: Learn Data Visualization with Python/R

Part of your interaction with data will involve the need to visualize it, in order to be able to read it and derive conclusions from it. This is why you need to learn data visualization with Python or R. This skill will not only help you in the analysis phase, but also in the presentation phase.

Step 6: Learn Machine Learning

With what you’ve already learned above, you can pat yourself on the back! Now complete the achievement by mastering an essential area in data science: Machine Learning. The programming and statistics backgrounds you’ve built above will make it possible for you to grasp complex machine learning concepts.

Step 7: Learn Presentation Skills

You finally acquired the full stack needed to be a data scientist! Now it is time to brush up on those presentation skills; because what’s the point if you cannot communicate your ideas and findings well?

Job Functions

Data Scientists’ responsibilities fall in the areas of reporting, analytics, and machine learning.

Here are some common job functions:

• Understanding client’s business needs and translating them into functional/technical needs.
• Formulating problems analytically and identifying ways to solve them.
• Use tools like SQL, Python/R, and Linux to generate and provide data-driven insights.
• Build analytics tools that support the company’s business needs and contribute to its expansion and growth. These include self-service analytics dashboards, decision-support systems, predictive models and machine learning algorithms.

Career Prospects — Data Scientist

The career prospects are definitely bright for data scientists. Glassdoor Salaries shows that the average national salary for a data scientist in the US stands at $113,000!

As data keeps growing at a rate faster than organizations know what to do with, demand for people who can make meaning out of this data will keep increasing. Data Science is a stable and promising career path to take.

We hope you found this Data Scientist Career Guide Beneficial!

Originally published at https://house-of-analytics.com on March 8, 2020.