Knowledge Series – Demystifying the science behind Double “A” – AI & Analytics for a Smooth Career Transition to Data Science/ Analytics – Part 1

Analytics India Magazine recently caught up with me to get an insight into my analytics journey, some of the important contributions that I have made to the field and my advice for Data Science enthusiasts.  The article Don’t Fall For Data Science Myths, witnessed a strong readership complemented with a string of questions from a number of readers ranging from University students to experienced professionals on how to start career in Data & Analytics. It was amazing to learn that the questions were not limited from students but also from professionals holding more than a decade of experience in various domains. Clearly, there seems to be a lot of apprehension surrounding what does it really take to start career in this space and needless to say, a lot of myths impede one’s determination to learn and start on the analytics journey.

Well, if you have an analytical mindset and love interpreting data to tell a story, you may want to consider a career as a Data Analyst or Data Scientist. After all, these are two of the hottest jobs currently. Harvard Business Review even bestowed “Data Scientist” the title of “sexiest job of the 21st century.”

To this end, I strongly believe it is important that I take an opportunity to share my views on this subject. I initially thought to cover everything in a single article. However, given the depth of the subject, I feel it makes more sense to break this article into six themes. This would mean that; a) as a reader, you will not feel bogged down by seeing a lengthy article and immediately get dis-engaged and b) I would be able to explain each theme in detail while ensuring I am to some extent, able to provide some level of much needed guidance which may help you to embed course correction in your advent to start a career in Data Analytics world.

The six themes I will be covering are:

1. Data Science vs. Data Analytics – Big Daddy Data is the ONLY common factor!
2. The AI Ecosystem – its really simple, I swear!
3. Role profiles – Only the heroes, no villains!
4. Skills required – The essentials!
5. Getting Ready – Right first time applies to job interviews too!
6. How to become a Champion – You can challenge your Boss too!

If you believe there is any other topic which I should cover over the next couple of weeks, please do let me know. I will be happy to embed the same in upcoming features.

Data never sleeps and in today’s world. As of June 2019, there were more than 4.5 billion internet users across the globe. Moreover, we create 2.5 quintillion bytes of data every single day – a number that is expected to grow exponentially with each passing year. If this data can be somehow analysed, we can build revolutionary systems where businesses can provide state-of-the-art solutions to everyday problems. However, such large data also requires understanding and availability of proper tools to parse through them with an aim to uncover the right information. To better comprehend Big Data, the fields of Data Science and Analytics have gone from largely being consigned to academia, to instead becoming integral elements of an organisation.

However, it can be confusing to differentiate between Data Analytics and Data Science. Despite the two being interconnected, they provide different results and follow different approaches. That said, without sparing any confusion and offer you a definitive insight into these two innovative fields, let’s explore Data Science vs Data Analytics.

Data Science

Data Science is a multidisciplinary profile aimed at tackling large sets of structured and un-structured data and includes data cleansing, preparation, and analysis with an aim to derive actionable insights. The role primarily grips on exhuming answers to things we don’t know. The main goal here is to ask questions, with less concern for specific answers and more emphasis placed on finding the right question to ask. Instead of checking a hypothesis, Data Science tries to build co-relation and a plan for the future. Data Science often moves an organisation from inquiry to insight.

Data Analytics

Data Analytics focuses on creating methods to capture, process, and organise data to uncover actionable insights for current problems, and establishing the best way to present this data. More simply, the field of Data Analytics is directed towards solving problems for questions we know we don’t know the answers to. Data Analytics also encompasses a few different branches of broader statistics.

So, is there an overlap between Data Science and Data Analytics?

Data Science is a parasol term that encompasses Data Analytics and other related disciplines. While a Data Scientist is expected to forecast the future based on past patterns, Data Analysts extract meaningful insights. A Data Scientist creates questions, while a Data Analyst finds answers to the existing set of questions. Although the differences exist, both Data Science and Data Analytics are important parts of the future of work and the way we understand the data. Both fields are growing and lucrative, and you can’t go wrong with either.

Disclaimer - The views expressed in this article are my personal views derived basis my own experience and research and in no way indicate towards strategy of any organisation.