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Institute of Continuing Education (ICE)

Data science student

You might be wondering, what exactly is data science?

Data science isn’t a physical product — it’s a broad term given to studying behaviours, topics, and trends. Data science has a lot of definitions, but it’s best to think of it as a multidisciplinary approach to find stories, insights, and patterns from large data sets. Then cleaning and organising this data in a way that makes sense so you can act on it.

And now, data science is everywhere.

If you’ve uploaded a photo on Instagram recently, if you’ve sent a Snap using Snapchat to one of your friends, or if you left a comment on a YouTube video today — those actions were collected and added to a data set that data scientists are using to make decisions. The ads you get for how to start a business course and the product recommendations you get from Amazon are all a product of data science.

The skill of a data scientist ultimately lies in pattern recognition. Leveraging data on your actions and the actions of others, they extract meaning from those patterns – in this case, a recommendation that feels personalized to you.

Data science is also a broad term used to describe many, more specific subcategories such as data engineering, data mining, mathematics, statistics, advanced computing, and model visualization. It’s also the keystone of artificial intelligence, machine learning, and deep learning.

What do data scientists do?

Data Scientists collect, clean, and explain data. Their main role is to adjust statistical and mathematical models applied to acquire data. In other words, they make data discoveries.

In a world where every action is collected, data scientists are more important than ever.  Aside from crunching big data to enable things like artificial intelligence and advanced medical research, data science has stretched into the private sector as companies look to data as a decision driver.

When applied to business decisions, data scientists have a few concentrations:

● Data scientists turn formal business problems into data questions, so those business problems have data-driven answers.
● They communicate data to less-technical team members and stakeholders through data visualization.
● Data scientists often take other titles, like data architects, data engineers, machine learning engineers, and analytics managers.

But what separates data scientists from the rest of the pack is their ability to code. Data scientists are experts in programming languages like Python, R, SQL, and more.

What skills do data scientists need?

First, data scientists use math skills, like algebra, calculus, and statistics, to build models that extract insights from data. To build these models, they work in Python to clean data sets. They then leverage Machine Learning and predictive modelling to get at insights from the data set. Math knowledge allows data scientists to understand how to effectively use algorithms in their models and iterate on the modelling process.

If this sounds interesting to you, why not take the first-step in your data science career and start developing your skillset with ICE’s online Data Science Programme with Flatiron School? This introductory level course is designed for students with minimal technical background, and will teach you everything you need to know to start tackling your own data challenges using Python, SQL, and more    

This article has been reproduced from the following articles with permission from Flatiron School: 

How to become a data scientist without a degree and How to start learning data science.

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