So many areas: Statistics, Machine learning, AI, Business Intelligence, Decision Sciences, Data Science, Marketing Analytics, Customer Analytics, Deep Learning and what not. All these roles/job profiles: Business analyst, Marketing analyst, Machine learning specialist, Data scientist, Decision Scientist, Product Analyst, Marketing analyst and many more.
I know it’s confusing, and I’ve been there too. Using this blog, I'm trying to bring clarity, to as many of you, as I can. By no means am I claiming to give you a comprehensive understanding of this vast, complicated landscape. Rather, my intention is to help clear out the confusion, enable you to evaluate for yourself what path suits you best. In the process, I’ll make a lot of simplifications and generalizations. But they’re essential at this stage, to help you see the big picture.
Information is everywhere around us. Every day, you are bombarded with information, and we’re rather comfortable with statements like “60% chance of rain”. Some of these constructs/notions are rather complex (think about what “60% chance of rain” really means). But you didn’t really need a formal degree in statistics to make sense of it. Also, note that data is not just numerical/quantitative. While traditionally we have been studying data as numbers recorded and stored in tables/files; the past decades have led to an explosion in text, image, audio and video data. It’s pretty easy for anyone to go on to a social media platform and post some text, image and accompany it with a video in a matter of minutes.
Data generation is at an all-time high – the internet and smartphones have made data ubiquitous and abundant. Every day, there are more than 600 million tweets made on Twitter, about 4 billion searches made on Google. These numbers explode when considering data generated by sensors and IOT, and these numbers are growing exponentially.
Data is powerful and is changing the face of our world. From helping cure diseases, to recommending you the best product to buy. From boosting a bank’s revenue by identifying potential customers, ,to optimizing the advertisements to show. We could just go on and on. The value of data is tremendous. Data gives companies that edge to make better, more informed decisions and improve their products/services. There are several case studies where leveraging data turned the fortunes for the organization; Netflix’s success is a prime example.
Source: Tim Elliott’s awesome cartoons Companies are becoming increasingly data driven – meaning that no decision is made purely out of ‘hunch’ or a gut instinct. Even minor changes to, say, an element on a website for a business, are therefore approved only after validating through data that it will work. Data >>> Opinions We’ve come to a point where organizations that do not pay heed to data will perish sooner or later. No matter what your career, you will make professional decisions that involve data.
From identifying potential customers in banking, to intelligent filters on Instagram, to suggested connections on Linkedin – the applications of data science are across domains and are affecting all aspects of life. More and more institutions/ companies/ organizations are embracing this wave and will continue to do so. In this age of data overload, if you can understand data and extract meaningful and actionable insights, you will be immensely valued. That data science is the most attractive job family of this century is something you keep hearing time and again (almost a cliché now). But it is true indeed, and if you’re aspiring to start a career in data science, this is an amazing time to be here. Take it from a practitioner in this field!
I’ll be your guide in these first steps of yours. Over this series of posts, we’ll discuss some larger ideas, clear some of the confusions, and touch upon many of the important technical topics in this field, to help you grasp concepts and develop an intuition that will make you an awesome data science professional. Stay tuned!