Facebook

What is a data scientist?

What is a data scientist?
According to professional networking site Linked-In the role of data scientist assumed the second top spot in its Global Top Skills ranking last year. While Cloud Computing took first place and data visualisations (or presentations) entered the top 10 for the first time Linked-In’s research is backed up by others ranging from Politecnico di Milano School of Management (MIP) to recruitment agencies actively filling jobs in the IT field.
Data science is a relatively new role and quite in demand at the moment. Online technology magazine Silicon Republic quotes David Pardoe from international job finder Hays Recruitment:

“The fundamental goal of data science should be to help humans make either quicker decisions or better decisions. You could assume that this is truer for some industries than others, but I would suggest it is true of all industries, even those where decisions are automated or seemingly happen without human intervention (eg online shopping/retail). Even in those industries, a human needs to determine how the machine will make the decision.”

Politecnico di Milano School of Management (MIP), the big data analytics and business intelligence observatory, say their findings of 280 data scientists show advances in tech have boosted data scientist roles by 57%. All the developments are promoting growth in different sectors not least finance, media and the pharmaceutical industries as they make better use of their data. Hence the need for data scientists to get to work. Alessandro Piva, director of the research observatory, said:

“As big data analytics grips the world of business and as companies increasingly understand the merit of using this valuable information in their decision-making processes, the role of the data scientist is increasing both in popularity and in availability”

“This is one example of a job created in recent years by huge advances in our understanding of tech and our ability to interpret masses of data that often used to stagnate in unread files – or sometimes even failed to be collected.”

Business Analytics Infograph

Politecnico di Milano School of Management (MIP), the big data analytics and business intelligence observatory, say their findings of 280 data scientists show advances in tech have boosted data scientist roles by 57%. All the developments are promoting growth in different sectors not least finance, media and the pharmaceutical industries as they make better use of their data. Hence the need for data scientists to get to work. Alessandro Piva, director of the research observatory, said:

“As big data analytics grips the world of business and as companies increasingly understand the merit of using this valuable information in their decision-making processes, the role of the data scientist is increasing both in popularity and in availability”

“This is one example of a job created in recent years by huge advances in our understanding of tech and our ability to interpret masses of data that often used to stagnate in unread files – or sometimes even failed to be collected.”

The infographic from Rutgers Online summarises some of the key issues about a data scientist’s role:

What can you do with a career as a data scientist? infograph

The general trend points to greater roles in large companies to employ more data scientists said Dave Holtz, data scientist at Airbnb. He advises in his blog, Udacity, that there are eight core skills that will get IT graduates the hottest job of the 21st century:

  1. Basic Tools: No matter what type of company you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade. This means a statistical programming language, like R or Python, and a database querying language like SQL.
  2. Basic Statistics: At least a basic understanding of statistics is vital as a data scientist. You should be familiar with statistical tests, distributions, maximum likelihood estimators, etc.
  3. Machine Learning: If you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven, it may be the case that you’ll want to be familiar with machine learning methods…More important is to understand the broadstrokes and really understand when it is appropriate to use different techniques.
  4. Multivariable Calculus and Linear Algebra: You may in fact be asked to derive some of the machine learning or statistics results you employ elsewhere in your interview. Even if you’re not, your interviewer may ask you some basic multivariable calculus or linear algebra questions, since they form the basis of a lot of these techniques…Understanding these concepts is most important at companies where the product is defined by the data and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.
  5. Data Munging: Often times, the data you’re analyzing is going to be messy and difficult to work with. Because of this, it’s really important to know how to deal with imperfections in data…This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
  6. Data Visualization & Communication: Visualizing and communicating data is incredibly important, especially at young companies who are making data-driven decisions for the first time or companies where data scientists are viewed as people who help others make data-driven decisions…It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.
  7. Software Engineering: If you’re interviewing at a smaller company and are one of the first data science hires, it can be important to have a strong software engineering background. You’ll be responsible for handling a lot of data logging, and potentially the development of data-driven products.
  8. Thinking Like A Data Scientist: Companies want to see that you’re a (data-driven) problem solver…It’s important to think about what things are important, and what things aren’t. How should you, as the data scientist, interact with the engineers and product managers? What methods should you use? When do approximations make sense? http://blog.udacity.com/2014/11/data-science-job-skills.html

Business magazine Forbes said some of the recent trends show huge growth in the data science field.

  • Demand for deep analytical talent in the U.S. projected to be 50-60% greater than supply by 2018, leading to a shortage of 140,000 to 190,000 people as well as 1.5 million managers and analysts.
  • Data scientist proclaimed to be the “sexiest job of the 21st century.”
  • In Q1 2015, the number of job postings for data scientist grew 57% year-over-year while searches for data scientist grew 73.5% for the same period.
  • Data scientist was ranked the best job in America because of its high earning potential, abundant career opportunities, and number of job openings.
  • Related search queries have increased six times over the past five years.

Take a look at the jobs advertised on Linked-In:
https://www.linkedin.com/jobs/data-scientist-jobs/?country=ie&keywords=data%20scientist&locationId=ie%3A0

Check out MIUC’s new BSc in Information Systems and Data Science staring in Autumn 2017:

BSc in Information Systems and Data Science

By Mary O’Carroll

Please follow and like us:

Be the first to comment

Leave a Reply

Your email address will not be published.


*


Shadow

Enjoy this Website? Please spread the word :)

  • Facebook
  • Twitter
  • Google+
  • LinkedIn
  • Instagram
  • YouTube