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Become a data scientist

In the second of a series of posts about taking on new roles, we look at how to become a data scientist.

The basics

Here’s a short summary of what a data scientist is and does.

Data scientists use mathematical, programming and analytical skills to turn large amounts of information (aka big data) into useful, useable insights. As a data scientist, you might work in an engineering role, setting up and maintaining large datasets, or a more analytical role, spotting and communicating trends within that data. Because of demand for data scientists, salaries are above average and the job ranks as the best in America.

Step by step

Taking on any new role can be daunting. Here’s a step-by-step guide for starting with data science.

1. Improve your mathematical skills
2. Learn a programming language
3. Find out how databases work
4. Hone your communication skills
5. Join the data science community

Key skills

Find out what abilities you need to be a data scientist.

Mathematical skills
If you know multivariable calculus and linear algebra, you’ll “have enough background to understand almost all of the probability, statistics and machine learning for the job,” says William Chen, Data Scientist at Quora.

Technical skills
You’ll need: the ability to program in a language like R or Python; knowledge of SQL to construct databases; an awareness of tools like Hadoop to process large datasets; and a familiarity with data mining and machine learning techniques.

Problem-solving skills
Being able to break down problems, develop hypotheses and prove/disprove them with data are key skills. You’ll be “set a high-level objective, for which you need to determine the best course of action,” says Nuno Castro, Director of Data Science at Expedia.

Soft skills
Once you’ve gleaned insights from data, you’ll need to present them in a way that others can understand and use to take action. Effective communication and data visualisation skills are a necessity, as is an awareness of business and industry trends.

Dos and don’ts

Discover what to do and not to do as an aspiring data scientist.
Do

✅ Explore data science roles – from data engineer to data analyst
✅ Ask for advice – there’s plenty of bootcamps and meetups
✅ Read widely – devour books and blogs

Don’t

❎ Believe the “sexiest job” hype – data science requires hard work
❎ Stop learning – there’ll always be new tools to master
❎ Forget about soft skills – they’re as important as technical ones

Inspiration

Meet the people excelling at data science.

DJ Patil, Data Scientist

DJ Patil, Data Scientist
DJ Patil was one of the first to coin the term “data scientist” and put the profession on the map with his Harvard Business Review article. He was the Chief Data Scientist at the White House Office of Science and Technology Policy until early 2017 and co-authored Data Driven: Creating a Data Culture with Hilary Mason. (Image: Joi Ito.)

Hilary Mason, Data Scientist

Hilary Mason, Data Scientist
Hilary Mason is the founder of Fast Forward Labs, which “helps organizations accelerate their data science and machine intelligence capabilities.” She’s served as Bit.ly’s Chief Scientist, been named as one of Fortune’s 40 Under 40, and spoken extensively on data science. (Image: Stiftelsen.)

Get started

Get practical help to become a data scientist with courses from top organisations.

Get an intro to R or Python
Introduction to R for Data Science
Learn to Code for Data Analysis

Explore the depths of data
Big Data Analytics
Big Data: Measuring and Predicting Human Behaviour
More Data Mining with Weka

Apply data in new ways
Using Open Data for Digital Business
Using Data to Improve Healthcare
Data Tells a Story: Reading Data in the Social Sciences and Humanities
Social Media Analytics: Using Data to Understand Public Conversations

Category Digital Skills

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