4 pre-requisites of becoming a data scientist

A data scientist is one of the most in-demand professions today, and for good reason.

Making sense of large amounts of data and developing statistical learning models that allow analysing data more efficiently are essential for companies that want to make more informed decisions, so a data scientist that knows what he’s doing will never be short on job opportunities.

But because the profession requires a person to be able to make complicated predictions and try to make sense of raw data, it requires a very sophisticated understanding of data processing, statistical analysis, and much more.

That’s why in order to be considered, you need to meet certain qualifications.

But what are the criteria to be hired for the position?

Well, read on, and find out the most important skills that a data scientist must have to be successful.

Good Knowledge of Programming Languages
One of the first qualifications that any data scientist must have is having a comprehensive knowledge of various programming languages.

In fact, it could even be said that while a good coder doesn’t necessarily have to be a good data scientist, a good data scientist always is a solid coder as well.

Since the data that will need to be analysed will come in various forms, so you need to be able to read it and work with it to use it in the research.

Some of the languages that a data scientist needs to master include Perl, C/C++, Python and Java. However, out of these, Python will likely be the one that you will work with the most.

With the help of coding, a data scientist can make sense of raw data by cleaning it and organising more coherently, which allows drawing better insights and spotting patterns.

Basic Understanding of Machine Learning
Machine learning is another area that’s tightly intertwined with the duties of a data scientist, so while you don’t need to be an expert, you need to understand the fundamental principles and be able to apply them to your research.

In fact, it is possible to gain a better understanding of machine learning on the job – many companies are willing to hire data scientists and allow them to learn about machine learning principles along the way.

However, it can’t be overstated that it’s still an integral part of the data research process.

The biggest data-driven companies all use machine learning methods to sort through massive amounts of data, using approaches like random forests, ensemble methods, and many others.

While they can be implemented with the help of Python or other libraries, so you can get away without having in-depth knowledge about the algorithms, it’s still important to at least understand the basic concepts to be able to know when to apply which technique for the best results.

Familiarity with Mathematics and Statistics
As you can imagine, math and statistics are both essential in the day-to-day activities of a data scientist.

In fact, many of the best data scientists in the industry have an academic background in statistics and machine learning, although that isn’t critical and you can still become successful in the field even if you have a different degree.

But even then, it’s crucial to be familiar and have solid knowledge of statistical analysis, maximum likelihood estimators, machine learning, and most importantly, an understanding of when to apply which technique to achieve the best results.

Ability to Visualise Data
As you can probably imagine, data science deals with vast amounts of data that need to be processed and analysed. That’s why, beyond knowing how to interpret it, a good data scientist must also understand how to present and format the data to make it convenient to read and easy to understand.

In the end, being able to present the data and get people to notice the most important parts is just as important as gaining the insights in the first place because communicating the message is what gets a company to use the new information.

The best way to achieve that is to use visualisation tools such as d3.js, Tableau, or Matplottlib, all of which can help you take results from your data analysis and turn them into visual elements that are much easier to understand.

With the right visualisations, even complex materials can be simplified to drive the point home and allow the decision-makers to understand how the results can help the company.

If a data scientist can present the information in a way that gets the organisation to take action, his their work will become more valuable, as the company will be able to reap direct benefits from the research and make better day-to-day decisions.

If you are interested in a career in Data Science or AI, please contact one of our team.

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