In my last post on preparing your data science resume and project portfolio,  I was discussing about showcasing the thought process that one has, the thought process on how to solve business challenge with data, the thought process that potential employers want to see to determine if you can solve their business challenges, Besides the maths & statistics background, the thought process to come up with a solution is very important in assessing if one is suitable to be trained further or accepted into a data scientist role.

In a certain angle, data scientist's most valuable skill is to use data to provide business value. It could be providing relevant insights to solve business challenges or how to reach business objectives or using machine learning models for better business decision making etc. To sum it all up, data scientist is a solution provider using organization’s data as raw materials. And to be an effective solution provider, the thought process, seeking and bringing different resources  in is very important, in my opinion (of course there is the execution part but that might be another discussion).

So I thought I will provide some tips on strengthening that thought  process so that aspiring and current data scientist can provide more value to their employers and lead to better rewards (hopefully).

Reading Widely

I am an avid reader (or at least I think I am). Technical papers, especially those found in arxiv, are definitely part of the reading diet but I also read books on varied subjects such as sociology, psychology, economics, leaderships, biographies etc. I find that reading widely and drawing relations between the different knowledge and fields, helps to strengthen the thought process.

Through reading widely, some of the knowledge gained can be used in projects,  such as understand certain nuances found in the data during EDA. It also helps to create certain hypothesis (which needs to be tested further),  that can help in structuring a workable business strategy.

Reading widely, helps to spark off new ideas (INNOVATION!) on solving business challenges as well, putting ideas from different books together to  create new synergies!

The main idea is to gain and relate the knowledge through reading and bring them into the thought process.

Understand the Domain

I like to read up on Bloomberg Business Week, The Economist or similar periodicals. These are very good places to understand what is going on in the different industries, such as pharmaceutical, telcos, technology,  banking and for countries, understanding the political and economics climate. It can be a story on how the major trends are exerting changes  in the pharmaceutical industry for instances changes in the patent duration or how FRS 39/Basel III is affecting the banking industry.

All  these help the data scientist, especially if they are working in the  related industry, to be prepared in how they should work on the data and  machine learning models, taking into account the major trends affecting  the industry.

Networking

How does networking help in strengthening the thought process? Well, it is  to gather from many people, how they solved their employer’s business  challenges. Understand what were the technical, data and  organization-specific challenges when tackling the project and also if  possible, understand how these challenges are tackled.

Also try to understand how they conduct their Exploratory Data Analysis and incorporate their best practices into your own.

Very seldom, one can work on a project without any hiccups. So it is best to  “learn” from other people’s experience, be prepared for it or even take  precautions.

Again the main idea here is to learn and have a broader view of how other people tackle their challenges and because you never know when you might  come across something similar and you can adapt the solution to your  own projects.

Conclusion

The  broad idea is to learn from as many people, blogs, periodicals, books and websites on how to tackle different challenges. Working on a data science project involves many areas, components and teams thus challenges can come from anywhere, it is most important to be  prepared for it so that we can solve them as they come and be able to  continuously provide value through the organization’s data.

I wish all readers all the best in the data science journey! Keep learning!