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4 tips when starting your career in data science (leaving academia behind)

Daniel Butler
Principal Data Specialist

Taking the step from academia into a career in data science can feel more like an almighty leap. 

However, putting in the research and planning before doing so can ensure it's a smooth transition and ensure you stay one step ahead of the competition.

Want to know how... Read on...

Just because your guided learning may have stopped after completing your Masters or PhD, it doesn't mean you need to stop brushing up on your skills.

First off, it's time to get acquainted with Kaggle - this is your home for data science. 

It allows you to enter contests and compete with your peers to crowdsource the most accurate machine learning solutions and is also a great place to take additional tutorials and has forums in which you can talk with other like-minded individuals. 

For example, you can upload your code to the cloud and receive feedback from the community directly regarding your work. This can be really useful in progressing.

Arguably a critical skill to have is the ability to analyse data using a programming language, one of the most popular being Python. It is one of the best data science tools and knowing about machine learning, deep learning and pandas will set you apart from other applicants by a mile. 

So what can you do...

There are courses that you can take to learn more, including one in Applied Data Science with Python Specialization at the University of Michigan. 

Exploring Coursera will help you to find the right course for you and will ensure you are able to develop your skills in a way that will be most beneficial to your career.

And there's more:

On the programming front, another option is 'R' - an open-source programming language. It is used for statistical computing and graphics and is widely used amongst statisticians and data miners. 

It helps in the development of statistical software and data analysis. Knowing how to programme in R and use R for effective data capture will propel you into a career in data science.

This is just the start:

At the early stages in your career you'll need to hone your skills in statistics, industry-specific tools such as Spark and Hadoop, as well as cultivate an ability to communicate your ideas; both verbally and with data visualisation tools. 

I speak with budding data scientists every day and love to take the opportunity to advise on where to direct their self-learning, or even give them an idea of some of the opportunities available for people at a junior level. Get in touch with me today, I'd love to tell you more.