A Step-by-Step Guide for a Smooth Career Transition to Data Science

We are in a world where everything is moving at a breakneck speed. Industry pundits remark that we have surpassed the digital age to step into the data age – where every decision we make, all we do is based on data. It is also true that a large part of the world is catching up with digitisation, but the duo of cheap internet and mobile phone access to even the most remote areas has ensured that this is happening fast.

We are being ruled by digital and data in all walks of our life. From open banking to social media, smart homes to space travel – it is estimated that 2.5 quintillion bytes of data is churned out every day.

The managing, manipulating and usage of data, has therefore emerged into a field in itself – Data Science. This has taken the hot seat in everything from healthcare to finance, defence to construction – giving rise to data science jobs everywhere. If you are looking for a career transition into data science, looking for a guided program for Databricks Certified Associate Developer for Apache Spark, a comeback to the glitz of IT, or a stepping stone onto the most happening career pathway, you must consider data science. Here is all you need to know.

Background of jobs in data Science:

With critical use cases, it is very natural for jobs in the field of data science to be the most sought after as well. This is a relatively new field, an emerging technology, so there is a massive skill gap between the professionals entering the market from traditional streams of information technology and engineering or computer applications and market demands.

Leading consultant firms report that the demand for data science professionals is on the rise, and there is a whopping 48% gap between the number of jobs available and the professionals engaged in employment. Data scientists are also one of the best-paid folks in town. So you can see a lot of people with backgrounds in statistics, mathematics, informational technology and analytics jumping onto the data science wagon, by the way of upskilling.

Let us help you transition into the exciting field of data science:

Beginners and new entrants: non-science background:

This segment is for beginners with a non-science study background, and an interest in data science.

Remember that software is not about coding alone, and it is not rocket science. It is simple and logical just like the balance sheet or floor design you studied.

Since data science is entering every single industry, you must use your industry-specific knowledge – finance, architecture, pharma, health care – and combine it with data science – it is as simple as that.

Invest and upskill yourself to learn the basics of programming such as R and python or look online for Databricks Certified Associate Developer for Apache Spark – Preparation Toolkit – they are all used extensively in data science. Your goal here is to understand what is going on. Next, understand the tools and applications that are used in data science, specific to your industry. Get a basic understanding of it – where you could be a subject matter expert, and data analyst and then grow up to be a scientist!

Fresh science and math Graduates :

This includes a background in engineering, computer applications, statistics, mathematics and other allied fields. You will understand the logic, flow and algorithmic processes better with your background.

We strongly suggest you pursue a full-time or part-time long-term course in data science, of about 18-24 weeks duration, so you will be able to assimilate concepts and skills in that time.

Programming languages like R, python, packages like SAP and SAS, and Big data tools like Hadoop and Spark are all added advantages. Create an ID on Github and collaborate with other fellow developers to exchange and validate data models and get hands-on experience. Remember that this is largely open-source and works through a strong programmer-data science community.

Software Engineers :

If you are a software engineer, in development or testing, irrespective of the domain and technology, you already know how code works, and where it fits into the larger picture. If you are at the junior developer or analyst level, you could pursue a part-time course in data science, learn at least two Big Data tools and slide into it. If you are in the middle and senior management roles remember that your soft skills are also a huge asset. Prepare a roadmap to become Databricks Certified Associate Developer for Apache Spark.

You could focus on the front end, back end and the business aspect of your enterprise. You could be instrumental in making data-driven decisions and devise the strategic roadmap of your organisation through data analytics.

If you do not like the management side of things, you could evolve into a full-stack developer, which when coupled with data analytics could put you in senior technology-driven roles. Data science is booming! Make the best use of it and kickstart your career in it.