How to Upskill in IT to Transition to Data Science - European Leadership University


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How to Upskill in IT to Transition to Data Science

Data science, though not new, is a recently defined career and a lucrative one at that. With the average pay for a data scientist standing at $117, 345 a year compared to $85, 460 for an IT professional, more people are quickly moving from IT to Data Science. And it is not just about the money; automation is expected to replace many human jobs soon. So, remaining relevant in tech means moving to jobs which are not likely to be taken up by machines.

For any IT professional feeling left behind, here’s what to do to upscale to Data Science. But first:

Why Is There a Demand for Data Analysis Expertise?

Business and data cannot be separated; every electronic message sent, website visited, or transaction made creates new data. Today, that data is an invaluable commodity; and every serious business is seeking to leverage it to increase profits and reduce costs. As a result, more and more companies are demanding for Data Scientists.

Data Scientists help businesses understand data, draw insights from it, and make more informed decisions. With in-depth data knowledge, math and statistical prowess, programming experience, and business skills, Data Scientists:

  • Understand business problems and provide viable solutions
  • Helps business build stronger customer relationship
  • Enhance productivity and drive more sales
  • Improve customer experience

Practical Tips to Upskill and Land a Data Scientist Job

IT professionals or any person with programming or a STEM background can venture into Data Science. Here are smart pieces of advice to upskill:

Learn Data Science Vocabulary

Learning the terms Data Scientists use to refer to different things will accelerate the learning process. Note down unfamiliar words and look them up on Google to understand what they mean. Know all the terms used for each technique, action, or idea. In the beginning, find articles that are written for beginners to understand the basics. Visit useful sites like Stack Overflow, Kaggle, and Reddit to connect with other learners.

Start learning Python

Python is the most common programming language in Data Science. So, it makes sense to learn it. Find short Python courses on EdX, Codecademy, and more. Spend an hour or more everyday practicing coding.

Enroll For a Few Data Science MOOCs

With better Python skills, take a couple of Data Science MOOCs, online courses and if possible, attend relevant workshops. Dive into learning Data Analysis Tools like Hadoop, Apache Spark, and SQL Database.

Find a Basic Project to Tackle

Look for a problem to solve or a project to tackle by engaging with people in the community. Find a simple idea and build on it.

Work on Personal Branding

Build a personal brand and portfolio. Without a degree in Data Science, the skills learned, and the small projects handled are critical.

Network and Start Applying for Data Scientist Jobs

For better chances of getting a Data Scientist job, engage in online and in-person networking. Create a LinkedIn profile and be open to new opportunities. Build a vast network, and apply for as many jobs as possible. With every failed interview, learn how Data Scientist interviews are conducted and which questions are asked to improve on the next one.

Final Thought

With the current high demand for professionals with data analysis skills and the expected increase in demand in the future, a career in Data Science seems sustainable and worthwhile.

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