Transitioning into 'Data Science'

We reflect on our conversations with Data Scientists to compile our top tips for CS students, analysts, data engineers and statisticians looking to move in to a Data Science role: 

Advanced mathematics is absolutely necessary for Data Science. 

Matrix calculations, derivatives, eigenvalues, set theory, functions, vectors, linear transformations are vitally important to understand the theory behind statistical methods and programming. 

That considered, before embarking on your next online course or Machine Learning book it’s essential to revisit all of the key concepts. The majority of educational establishments instruct students to reach a set level of competency in these methods if they are to graduate, but the good news is that it won’t take too much of your time to brush up or seek out this knowledge.

You will find an array of resources to start, one great guide is Manga Linear Algebra, it offers comprehensive examples and work-arounds, its graphic and provides a solid grounding before getting to grips with more advanced algebra problems.

You will find innumerable resources online, and books are the best learning aids of all.

A common issue when trying to get into a field such as Data Science is becoming crushed with the weight of information, from having too many resources at hand. There are a wide variety of MOOCs, online courses, YouTube videos, etc., but the best use of the most valuable resource that we have, “time”, is to pick a book and start from the basics up to new concepts, and then keep filling the gaps with other books.

Learning Data Science should be viewed as a building blocks game

First, you would select the toy model you would like to build, then:

  • Open all the plastic bags and lay all the different pieces on a flat surface, so you can see all the different parts.
  • Understand how each part can be used. Learn about the characteristics: dimension, colour, weight, shape.
  • Start building small chunks until you’ve mastered all the uses.
  • Finally, after you’ve followed the instruction manual and built the model you’ve wanted, take all the pieces apart and start experimenting.

Learn what most all the blocks are, learn how to use them and then when you want to create more complex stuff look for the missing parts that you don’t have.

Computing skills are essential, not just for Data Science but for tomorrow’s world. Computer Code attributes for more than 80% of our lives today. Code is in our smartphones, websites, cars, televisions, health system, public transportation, manufacturing of goods, etc.

Almost every job/profession in industry is directly impacted by some program that enables the input, transform and print process of information. Learning about programming and how code works is not only to make software, apps or create a great website. Learning how to program will give you the advantage to understand how technology impacts our lives. 

Think systematically and understand where problems and complications in deep analytics may arise. 

Critical and analytical skills are also very important. Critical thinking Is used to find solutions to different kinds of problems. This is one thing that is not mentioned in most of the Data Science resources. The ability to find the correct angle to approach a problem will lead you to identify not only which tools to use for any problem, but will sometimes lead you to the most efficient solution.

Seek out broad resources

Everyone likes a YouTube talk, everyone shares good keynotes about leaders. There are many videos on visualisation packages (seaborn, ggplot, matplotlib) and software (tableau, excel) that can help create wonderful crisp charts. So, avoid getting saturated with too many options. 

Moreover, with ‘Data Science, the most important thing is how data and the inherent signals are delivered. Sometimes the simplest tools will generate a clear, relevant outcome that will enable you to construct ‘stories’ to convey business cases.

Alex White