So, You Want to Be A Data Scientist

Data Scientist

How do you become a data scientistLet’s take a look at whether you’ve got what it takes. 

Data is everywhere. Whenever you post on social media, watch a video, add something to your online shopping cart, or even text someone, you generate data. Data scientists take that data, along with hundreds of thousands of other sources, and work on it with tools, algorithms and machine learning principles to derive insights which help businesses understand their customers. 

Data scientists tend to have the same interests. Do you: 

  1. Like to analyse things- from the simplest to the most complex scenarios? 
  2. Love mathematics and statistics? 
  3. Enjoy business? 
  4. Like programming?
  5. Like the idea of continuous learning? 


If you answered ‘yes’ to most of these, you might just be the right material.  


Becoming A Data Scientist: Career Opportunities 


Assuming you have earned a degree in statistics, computer science, information technology, mathematics or data science, what professional opportunities await you?   

Data scientists are in demand. Oftentimes, companies like Direct Sourcing Solutions (yes, that’s us!) recruit junior data analysts or junior data scientists for entry-level positions. You may want to check out for openings. Related careers paths such as data analysts, data architects, data engineers, business intelligence developers, machine learning scientists, and more, are further options.  

We asked Jarrod Teo, our Chief Data Scientist, to explain his journey.  

Jarrod has 15 years of experience in building machine learning models for companies including IBM SPSS, Nielsen, Rakuten and Samsung. He is a respected data science speaker and an enthusiastic supporter of the younger generation of data scientists.   

Jarrod told us his learning path breaks down into nine steps:  

  1. Basic statistics, statistical testing and statistical modelling learned at school.  
  2. Coding in SPSS statistics. (Python was not that advanced then and we were also taught R, SAS at university). 
  3. White papers to learn advanced statistical testing, modelling and computer science models (seven white papers a month for three years. 
  4. Teach what was learnt from these three steps. Why? Because through teaching, people will share their viewpoints on what you learnt and further expand your knowledge.  
  5. Learn how to apply your skills to actual business questions and projects. 
  6. Understand how to work in an environment without data, software, hardware or servers, and work with teams with different skillsets. 
  7. Pick up more software skills.  
  8. Enhancing the craft of data science really takes a long while. Be prepared. 
  9. Learn how to speak as a layman. This means converting all the statistics, statistical testing, statistical modelling and computer science models into realistic business applications with real use cases. 


Still see yourself as a data scientistIf so, go, learn, and become one. The opportunities are endless. 




Customer Observer from Direct Sourcing Solutions combines multiple machine learning models to look deeper into your data, revealing which of your customers matter most and, uniquely, which of your products they’re likely to buy next. 

Get to know more about the advantages of this service and learn how we can help your business—Contact Direct Sourcing Solutions today! 

No Comments

Sorry, the comment form is closed at this time.