Why Data Science?

“Hello this is Blossom Academy where we recruit and develop world-class  data science talents, with the goal of connecting them to global opportunities.”

One question that consistently comes up when talking to our clients is “Why Data Science?” Why didn’t we choose to train talent in anything else. Now here’s our answer; instead of training the African youth for just today, why don’t we secure them for the future? Okay, we may have jumped the gun and answered a question with a question. Please read on to our next few paragraphs which gives you an idea of Data Science’s history and its relevance in the world today.

Data Science may seem like a new phenomenon – a bandwagon people are just jumping on – a new trend with the tagline, “Future!” Truth is, Data Science has been in existence for many years with it’s beginnings rooted in traditional statistics and computer science.

The history of Data Science dates back to 1962 when John Turkey wrote about combining statistics and computers to derive measurable outputs. Notable points in the evolution of Data Science includes the publishing of Peter Naur’s book, Concise Survey of Computer Methods, where he mentioned a number of methods for processing data.

In 1994, seventeen  years after the International Association of Statistical Computing was formed, organizations had made Database Marketing a crucial part of their Business activities which the Business Week reported on their cover page and it read: “Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product.” The issue at that time and now is this: companies are overwhelmed with the amount of Data coming to them and even though they want to use it, the majority are clueless.

By 2011, the idea of Data Science was still expanding but well shaped up. David Smith wrote in “Data Science: What’s in a name?” that “I think Data Science describes what we do: a combination of computer hacking, data analysis and problem solving.” According to the IDC, by 2025 global data will grow to 175 zettabytes. Every millisecond, we create data. Every individual pretty much punches in keywords on search engines everyday and companies use this data footprint we periodically  leave to learn and understand us better.

Based on the aforementioned points, I believe you chose to read this article due to a bubbling interest in the field of data science. As a result, I encourage you to comment at the bottom and join our next cohort.

Where were we? Ah, for companies to understand the information they collect everyday, they need a Data Scientist. It’s as simple as that. Check this out.

People create Data⇾ Companies Collect data ⇾ Companies need to understand Data ⇾ Companies hire Data Scientist.

Baseline is, Data Science will help companies make their decisions and business processes more effective. If you understand what your customer really wants, you don’t waste your time on terrible advertising decisions that ultimately do not speak to your customer. What this means is, companies need a Data Scientist to help run a better positioned strategic business. Which then translates to, “Hey Data Scientist, you are in demand!”

Despite the increased demand, the world lacks a ready talent pool to fill up the number of Data Science vacancies available. The problem is, lots of people seem to think Data Science is all too difficult. However it’s a skill anyone can get into! With the right training of course.

Here are a few relevant skills you’ll need as a Data Scientist.

Statistics: Statistics is a key concept in Data Science. The core fundamentals of Machine learning is statistics and you’ll need a good grasp of it to become a good Data Scientist. Statistics help select, evaluate and interpret data collected by using metrics like mean, mode, media, standard deviation etc. Even linear regression is a statistical concept.

Programming Knowledge: You want to communicate with machines? Learn a programming language. Python Programming is quite popular compared to other languages like R and Julia as it’s fairly easier to learn. It also has a wider community and multiple data science libraries with a wide range of modules to suit you precise needs.

Domain Knowledge: It’s important to have an understanding of the business and a core understanding of the problem they are trying to solve to know the right questions to ask.You may have the technical know-how but building your prediction algorithm without the necessary knowledge about the business is never a good idea.

Data Science is a growing field; a crucial part of most sectors today. As the importance for Data Science grows in the business sector so does the demand for Data Scientists. With new discoveries and expansions in the field it’s always important to upskill yourself and make yourself a bit special everyday.

Kickstart your data analytics or data science career by joining the next Blossom Academy bootcamp. Sign up at bit.ly/blossomofferings.

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