What is information overload?

One of the challenges that people face, as they get into data science, is the fact that there is a lot that needs to be learned and mastered. This is often overwhelming, especially for an aspiring data scientist. This phenomenon of information overload in data science needs to be carefully handled for one to succeed in their data science career.

According to Wikipedia, “Information overload is the difficulty in understanding an issue and effectively making decisions when one has too much information about that issue and is generally associated with the excessive quantity of daily information”. This phenomenon is very typical in data science and it mostly affects aspiring data scientists. I have personally encountered this challenge in my data science journey and I would like to share some tips that I consider useful especially for aspiring data scientists in wading the murky waters of breaking into the career.

Possible Causes of information overload in data science

Before we delve into how to deal with information overload in data science, let us examine some of the causes of this problem. What makes data scientists deal with the pressure of getting more and new skills in many areas within the field?

Firstly, I will suggest that one of them is the unrealistic expectation of some recruiters. If you have looked at the many adverts from most recruiters looking to fill data science positions, most likely you have seen a plethora of technical skills required to fill these positions. The amount and type of technical skills required, even for junior data science positions, is quite ridiculous. Sometimes one wonders whether it is really possible for a junior data scientist to have all those skills. What this does is that it puts an aspiring data scientist under immense pressure to try and study to gain knowledge on those skills. In so doing, they get overwhelmed by the amount of content they need to master and the skills they need to develop.

Secondly, this phenomenon has been caused by the ever dynamic and evolving nature of the field. Being a relatively new field, there are many things that are still being researched, explored, and invented regularly. Which is good – very good. These changes, therefore, necessitate learning new skills and the use of new technologies. For one to succeed in this developing field, you must master the skills.

Consequently, this makes it tempting to learn many new things and hence find yourself in a storm of dealing with information overload. What happens is that when you have just mastered a tool or technology on how to perform a task, there comes one claimed to be ‘state-of-the-art’ tool or algorithm that you need to learn. And this never stops! You keep learning day and night.

Thirdly, one of the reasons for information overload is the fact that data science is a very wide field. Conceptually, from big data to business intelligence, machine learning, data engineering, data analytics, Natural Language processing, etc., the field has a breadth and depth that needs to be covered. This probably is one of the most confusing areas to aspiring data scientists. The desire to be a jack of all trades and a master of none is very common. I will be sharing on how to specifically deal with this problem later in this article. But the fact is, you cannot master everything data science. It will just ruin your learning experience by subjecting you to unending ride on the surface of this field with no benefit to show.

How to deal with information overload.

Now back to the crux of the article. After understanding some of the causes of information overload, let me highlight a few things we need to consider as we deal with this problem.

1. Choose an area of interest and spend your energies in it.

First and foremost, choose an area of interest. Picking an area of interest will make learning fun and you will be able to dive deeper into the area and understand it better. You will also be able to master the skills in a way that you can be able to comfortably work on projects related to that field. One of the ways of choosing an area of interest is looking at your abilities. What can you easily master and do? Let the title “data scientist” not deceive you. There are so many roles you can do in data science that have no title “data scientist”.

2. Choose the tools you want to work with and focus to build your skills on them.

Another way of dealing with information overload in data science is by concentrating on select tools. There are different tools that can be used to perform the same task. This is a very good way of dealing with information overload in data science. Do not be worried if you are not able to master all of them. For instance, python and R can be used comfortably in data analysis so are Power BI and Tableau in business intelligence. So instead of struggling to understand all of them and end up with a superficial mastery of all, dig deep into either of the tools, understand it better, use it to work on practical projects, and enjoy its use. Do not be in trivial discussions of what is better than what. So long as you can be able to perform a task comfortably using that tool, feel comfortable.

3. Select a learning path and identify relevant resources

If you are in self-paced learning, one of the challenges that you probably may be facing is to get relevant and curated content for learning. There are so many resources available for data science. Choose the right resources wisely and focus on them. Do not subscribe to many MOOCs that teach the same thing. This will end up giving you a lot of pressure and you may not fully benefit from them. Too many duplicate resources will end up straining your learning curve. Dig deep into the learning content. Do not go too wide yet too shallow in your quest for knowledge. This will make you remain a novice for long. Learn to delve deep and maintain intellectual curiosity. 

4. Identify a mentor.

Finally, get a mentor. Mentors are very useful as you get into data science. I have personally gained a lot from people who have guided my learning and affirmed my abilities. A mentor will help you wisely and strategically navigate the tough tides that are the data science landscape. He/she will help you make the right choices and allow you to explore new and interesting things that you may not know in this field.

Conclusion

In summary, information overload is a common thing in data science. Always watch that it doesn’t work against you. Develop and maintain intellectual curiosity as you keep a sober mind in dealing with the overwhelming information available. Be consistent and develop grit in data science. It can be tough at times but it is doable. Learn, do and enjoy!