Beginning With Data Science
By: Anjali Savlani
Tags: Machine Learning, Data Science, Business Intelligence, Technology, Analytics

Data Science. Isn’t this topic of the decade? Everyone talks about it, everyone wants in on it, no one knows where to begin. With questions like what’s the post, how’s the salary, is the work interesting or not, it has become a new age black hole and there is no Rick Sanchez to help us. Do you want to be a data analyst? Perhaps, a business analyst. Or maybe, the proverbial Data Scientist! But if you’re slightly confused (more than slightly works too), then this post is for you. This might not be the only post you read today, but I hope I can at least give you a glimpse of what this fuss is all about.

The Buzz Words — Data Analytics, Business Analytics and Data Science

Did you know that there’s a difference between analysis and analytics? Analysis mainly refers to the past. Analytics, on the other hand, is an attempt to see what the future holds. Analytics can’t exist without Analysis. Data Analytics then means figuring out what data points would look like in future, for example, what traffic might look like in an hour. Business Analytics, as the term suggests, is an assessment and prediction of business, for example, sales prediction of the latest iPhone in the next year. What makes data analytics different from business analytics is the need for business acumen. Data Science is a wide term that pertains to several areas. It means understanding and applying statistics and complex mathematical and computational techniques to predict the future more accurately. While this seems a lot like data and business analytics, the difference comes from the usage of the data mining techniques, big data along with working with large structured and unstructured data.

Techniques — Traditional vs New Age Techniques

Prediction or forecasting has been in the picture for a long time now. Earlier, statisticians used methods like hypothesis analysis combined with clustering, regression and time series to predict the future. Today, with large unstructured and structured data, there is a need to use a much more advanced method to accurately make predictions. Artificial Intelligence — Machine Learning is one way to achieve this.

I would not go into too much detail. But below are a few examples where you see data science in everyday life.

Recommendation Systems — Ever used ‘Similar Products’ feature on Amazon, maybe “You Might Also Like” on another e-commerce platform? Yeah. Me neither. ;p Google Maps — the Best route to your destination, traffic prediction are some ways that save our lives every day Fraud Detection — How do banks let you transact that easily? Denying your request, calculating your credit score? With the number of transactions happening each second, data science is a necessity. Voice Recognition — Do you make Alexa do your work for you? Perhaps let Siri dial your mother’s number? Thought so.

Career Path

You might find this chart useful based on your experience. Or might not. Help me in improving this if you know more, please?

Links of some good courses to get started:

This is all I had for today. Let’s hope that in this journey, both you and I can learn to do some good and interesting.