The Best Path To A Data Science Career

If you are a software engineer who has been let off or someone looking to move into another job role, look no further. The best option is to upskill, get into the field of Data Science and ride on the wave of data science that is taking over the future.

Did you know data science jobs are among the highest paid jobs in the world? Also, a recent LinkedIn report has found that  Big Data Engineers, Data scientists and Machine Learning Engineers rank among the top emerging jobs. The primary reason for the growth of data related roles is the rapid pace of data generation and the corresponding need to make sense of it.

In the article I will explore the steps that you need to take to move into a data science role form your current role.

The Best Path To A Data Science Career

Find Out What The Job Entails


It is critical that you first verse yourself with the advantages and disadvantages before you make the shift. As different positions within the field of Data Science such as data engineers, data scientists and data analysts has varied requirements and responsibilities, you should pick the role as per your core competency.

For instance, the job roles of a data analyst and data engineer require expertise in maths, statistics and visualization. Communication skills are also a must for them. Additionally, data analysts also need to possess business knowledge for actionable insights.

KRA Analysis

Data engineers are required to fulfill different set of responsibilities. For this they need mastery over data storage and system implementation, database management and administration, and  programming.

Tool Kit

Usually for most roles the hard skills required like mastery over programming languages such as SQL, Python & R, are similar. However, there is a significant difference in the kind of soft skills needed. So, this constitutes the first step in making the switch and identifying the best role for you.

Identifying The Required Skill Set

The next step involves you to take a stock of your skill set and measure if they are fit for the job. As you know, any job roles in any data science is technical at the core. So, an interest and aptitude in that area is required to ensure job satisfaction.

Matching The Right Job With The Right Talent

A love for data is a prerequisite for any data related roles . Data science involves time consuming tasks such as  data pre-processing, cleaning and handling. You should remember this before you decide to shift to this field.

As coding is at the core of this job role, expertise in programming is needed. Coding skills are required at every step from handling cloud operations to developing a pipeline. Also, you should have an in depth knowledge of the industry’s best practices and code preservation for uptime. 

Roles and Responsibilities

The primary responsibility of a data engineer is to ensure the smooth flow of data in the pipeline. Data engineers thus needs to have a wide view of operations so as to make sure that their teammates can access the data at minimal cost.

On the other hand, data scientist mainly deal with deriving insights from data with the help of algorithms, models and other methods. They need to ensure that non-technical employees and other stakeholders are able to comprehend these operations by presenting to them in a consumable form. When you consider switching, these requirements  should be at the fore of your mind.

Getting Familiar With The Technology And Its Architecture

If you are new to the field of Data Science, your first step should be to identify the tools that are used by those who are already in this field. Considering that there are several competing products and variety of solutions that are in the industry today, data scientists have a large number of arsenals at their disposal.

Gain Knowledge Of The Industry To Select The Best Tools

You should know which are the most useful tools, so that you can pick the right tool for the right job role.  As each pipeline and workflow is different, there is a need for the data engineer to find out the best tool in order to derive data from different types of datasets. This consists of big data frameworks such as Spark, Hive, Hadoop, Kafka and model frameworks like PyTorch, Tensorflow to name a few. Additionally, you also need expertise in SQL, as data engineers often finds their primary duty in the domain of database administration.

Proficiency In Mathematics and Soft Skills

As a data scientist you need to keep yourself updated with the latest happenings in AI technology. The main reason for this is that everyday we are seeing new innovations unleashed by AI which helps us to address existing problems using cutting edge algorithms and models.

You also need proficiency in statistics and mathematics to be a data scientist. IAlso, mastery over programming languages such as SQL, Python and R are important to the workflow of a data scientist.

End Note 

Before you make the switch to a new field such as Data Science, pay attention to the above mentioned criteria. As the skillsets of a software engineer includes knowledge of programming languages, and analytical knowledge, it transfers well into the data science field and is thus a good idea for software engineers looking to shift.


About Natasha


Natasha Manuel is an information analyst at SpringPeople. In her 7+ years of experience in the edu-tech industry, Natasha has led many corporate learning projects and delivered several high impact training programs. She is passionate about technology and its role in effective learning solutions

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