How to become Data Scientist ?

In a simplified definition, Data Scientist is a professional who can work with a large amount of data and extract analytical insights. But a Data Scientist is more than that! A successful Data Scientist should combine technical and analytical skills with business thinking and communication skills. His role is to analyze and understand the data, set the rights questions, and communicate his results in a clear way to an audience that may not be familiar with the algorithms used for the analysis.

Who can I study data science?

There are three major pathways to obtain data science education:
     1) Massive Open Online Courses (MOOC)
     2) University degree/certificate
     3) Bootcamp training
The comparison between these pathways has made based on the following criteria: cost, mentoring, time/flexibility, program length, and finally, job placement rate.

1) Massive Open Online Courses (MOOC) like (EdX, Udemy, or Coursera online courses) are low-cost or even free programs. They are very flexible as you can follow them at any time that is convenient for you, but there is no interaction with a professor that can guide and answer any other question that can be created during the learning process. That means if you have any other extra query you need to spend a lot of time looking around to find the answer. However, the biggest issue for these programs especially the short one is that are not totally structured. Meaning that the majority of the students end up following short parts of a Data Science program that all of them together do not have the benefits of a well-structured program from the beginning to the end.

2) A university degree/certificate (AUEB, Demokritos, etc) in data science has the benefit of a structured program that costs more than the MOOC but you have support from a professor and usually a teaching assistant. However, these programs last 12-24 months and miss the link to the industry. They offer excellent academic knowledge however a lot of employees are not happy with the practical skills that a junior Data Scientist has for an entry position and of course, they are missing business thinking.

3) The bootcamps on the other side are flexible, during the program there is support from a professor and a teaching assistant, they last 3-6 months and they are more connected to the job market. Therefore the job placement rate is higher following a Data science bootcamp. However, they are intensive courses meaning that it is needed to spend several hours on practice with data and questions from the industry. In a bootcamp you learn to have a business thinking for your data and you learn all the soft skills need it like teamwork and presentation skills.

As a young Data Scientist, you should keep in mind that having a mentor can boost your career five times more than people without a mentor(s). Therefore find a mentor and being a member of a Data science network can support your career steps. If you want to be Data Scientist you should not forget that you should keep learning new methods in machine learning and artificial intelligence while questioning and talk with passion for Big Data!

By Sophia Verouti