What Skills Do You Need To Be a Data Scientist?

Data scientists are the oil refineries of the 21st century. They take raw materials with no obvious value and process it into precious fuel.

According to Indeed.com, between December 2017 to December 2018 there was a 29% increase in job postings for data scientists. According to LinkedIn, being a data scientist is the most promising job in America. On their platform alone, they saw a 59% increase in job postings that are related to data science. With such high demand, many ask what does it take to become a data scientist. Here at iTrain, our Certified Data Science Specialist course helps prepare anyone seeking a career in data science. In this post, we list down skills you need to have to become a data scientist.

Basic Programming Skills

To be good at something, you need to speak the language first. The most common programming languages used by data analysts and data scientists are Python and R. Python will be familiar to anyone from a programming background while R was built for statisticians by statisticians.

To help guide you to select which language is suitable for your needs, read our blog post on Python or R here. In the future, programmers may need to master a third option called Julia. It currently has more than 2 million users worldwide and it’s language that’s built around data science. Even large organizations like the New York Reserve are seeking ways to integrate it into their organization.

Statistical Knowledge

While most of the heavy mathematics are done using libraries of your preferred language, knowledge of statistics will help you in the process. According to Analytics Vidhya, good data scientists need to know:

• Descriptive  Statistics
• Inferential Statistics
• Predictive Models

Data Scrubbing and Wrangling

This is the point where we go back to the refinery analogy. Raw data is nothing but random numbers. It’s when you clean the data that you can render it usable. The human eye can only do so much when it comes checking the data for errors and duplication.

This is where data scrubbing and wrangling tools become necessary. Some of the free tools that data scientists could master are OpenRefine (formerly known as Google Refine), Trifacta Wrangler, and the open source Drake.

Data Visualization

What’s the point of having data if you can’t sell it? And what better way for you to sell than to visualize it for the masses to see. Check out how news companies like New York Times or Quartz use data to tell stories. For languages like R, data visualization is built into it because of its origins in research and academia. R has an extensive library to choose from that only requires only a few lines of code to operate.

Python, despite its infancy in data science, has also increased the number of visualization libraries. Check out Seaborn or Bokeh for Python-based tools.

Then there are tools that require no coding. The most obvious would be Excel (which of course has its limitation on dataset size). Then there’s Tableau which has a limited free version but you could also apply for a free full copy if you’re in academia.

With the list of skills so far, you can technically become a data analyst.

Artificial Intelligence: Machine Learning and Deep Learning

Artificial intelligence is the closest to the popular imagination of what is data science. To keep it simple, AI is a computer programme that mimics human intelligence. Machine Learning (ML) and Deep Learning (DL) are subsets of AI that learns from pre-existing data in order to predict future outcomes. ML learns by being told a set of patterns or details and it picks up objects from a large data set. For example, the computer is told what an apple looks like and it picks up apples from a large data set of images. DL, on the other hand, learns by receiving a large data set then picks up patterns and details. Back to the apple example, the computer isn’t told what’s an apple. But it learns common qualities or details on its own so that it could identify an apple, without being told that it’s an apple in the first place. Here’s an example of how a DL computer by Google learned how to identify cats without being told what are cats.

You can learn more about ML and DL in our article about demystifying the two here.

If you’ve mastered all the skills above, you’ll be on your path to be a data scientist. In practice, the skills do not end here as data science is still an evolving subject. But it’s a good start.

Seeking a career in data science? Or are you planning to prep your data team to become data scientists? Our CDSS certification might very be the answer you need. iTrain offers in-demand digital technology certifications and has trained thousands of IT teams. Contact our iTrain course consultants at +603-2733 0337 or email info@itrain.com.my to find out more about our training courses that will help your good self or organisation stay ahead of the curve.

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