Last Updated on July 1, 2015

You’ve heard the terms “data scientist”, “data wrangler”, “data analyst” and “data engineer”. You’ve heard that a data scientist is the sexiest job of the 21st century. But what exactly do all these terms mean? And what does it mean for you?

We sat down with our Udacity CEO, Sebastian Thrun, and VP of Engineering, Nitin Sharma, to discuss the ins and outs of data science, and how you can get on this wave of the future.

https://youtube.com/watch?v=f6p5y5Jm_5E%3Flist%3DUUBVCi5JbYmfG3q5MEuoWdOw

What is Data Science?

It is the science of systematically discovering patterns in very large data sets to extract useful knowledge and predict something of value. The world is exploding with data. It has been said that 90% of the data in the world today has been created in the last two years. Along with the data being created, our available machine power is also increasing, but the ability to harness that power and analyze the data is not keeping up. This field describes the rigorous process of finding interesting insights from these large amounts of data.

What fields are we seeing data science come into play?

The question might be, what fields are we not seeing data science being used? The movie recommendations you receive through Netflix is based on data science. So are your Google search results. Data science is powering projects in health, finance, retail, and even meterology. It helps analyze the human genome, predict disease susceptibility, and with IBM’s Watson, helps doctors diagnose patients with more accuracy.

What is the different between a data scientist, a data engineer and a data analyst?

You may often hear these terms used almost interchangeably, but Sebastian and Nitin clarify a few important differences. Data scientists invent new algorithms to dissect the data. Data engineers build the tools and platforms necessary to store and organize the data to make it easily accessibly. Data analysts are in the middle; they use existing algorithms and tools to make sense of the data and turn it into meaningful information that can be used to better a service or a product, and in turn, generate revenue for a company.

What skill do you need to become a data analyst?

Last week, we posted an article that delves into this question more deeply, but a few skills aside from the purely technical ones discussed before are:

Understand the strengths and weaknesses of different algorithms

Know how to deal with missing or corrupted data

The ability to communicate your findings to the business audience

Know how to take advantage of the tools available to get the job done

See the problem from a business perspective

Can I get a job in data science if I have no previous work experience?

Data is the future of every company, and any company who wants to compete will need a strong data science team. This field is relatively new and there is a shortage of trained people in the space, so there are lots of opportunities to make a difference. If you can prove your skills with a strong portfolio, you may not necessarily need the 3-5 years of work experience often indicated on job descriptions. Instead of college transcripts, companies are ready to accept new credentials, such as portfolios and git hub repositories as proof that you can work in a data science environment. Remember to point to any practical experience and other proficiencies!

We’ve provided more information on the types of data science jobs available and the skills you will need to land those jobs, as well as a Udacity Data Analyst Nanodegree where you can gain essential experience through our portfolio focused projects.