Hey Guys, welcome to yet another interesting session by Intellipaat. We will start off by understanding what exactly is Data Science and then we will go through some data science use cases, following which we will look at different languages used for data science and finally we will implement data science concepts with R language. So, what In the world is Data Science?
Have you ever come across huge amounts of data, only to discard it because you thought it wasn’t useful to you? Well, my friend, you couldn’t be more wrong. Data is always useful to you. You just need to dig through it to find meaningful insights. So, Data Science is using some tools and techniques which help you to manipulate or wrangle the data so that you can find something new and meaningful. Now, what if I gave you a really long spreadsheet
containing all the sales figures for the past three years? It would be difficult for you
to comprehend the data, wouldn’t it? So, instead of the spread-sheet what if I gave
you some charts and graphs related to annual sales? You would obviously prefer the graphs over the spread-sheet, right? This again is data science my friend. Visualizing the data helps you get a better perspective and understand it easily.
Now, every industry has it’s own problems and the top management needs to take responsible and right decisions to stay ahead in the race. So, how does one get good at decision making?
Well, that’s simple.By going through the historical data and understanding what worked and what did not and this is where data science comes in. Data Science brings along with it a bag of techniques which makes decision making simple.
Well, we are always curious about our future, aren’t we? Now, what if I told you we can
predict what happens in future by analysing the current data. Sounds like magic, doesnt it? Well I’m no magician, but I can definitely use data science concepts to perform predictive
analysis. So, in simple terms, data science is a set
of tools and techniques with which we can make the data talk to us.
Now, let’s go ahead and look at some data science use cases:
In the telecom industry, customer attrition is a major concern. Everyday a new player
comes into the market with new and attractive prices. In such a scenario, how does a company retain it’s users? This is where data science plays a pivotal
role. So, the data scientists or the data analysts, go through the data to understand customer behavior. They do a thorough analysis of data usage patterns, social media activity and voice call/sms patterns. The data scientists also analyse customer demographics so that proper segregation can be done in terms of age, gender or geographic location. All of this analysis helps in providing the
right offers to the right customers and this in turn helps to retain the customers. Now, let’s say, you are at your home, happily eating a pizza and you get a message on your phone stating that $10,000 was spent on your credit card to buy a diamond necklace and asking you to verify if the transaction was
actually done by you. You are shocked, and you immediately call up your bank and tell them that this transaction wasn’t done by you and hold off the purchase.
Now, how did the bank know that it was a fraudulent transaction? Well, Data science again folks!
The banks keep a check on your purchase pattern and whenever there’s a deviance in that
pattern, it flags it off as an anomaly and immediately notifies you. All of this, courtesy data science. Let’s look at some languages to implement
data science concepts: First in the list is R. R is the most widely
used language for data science tasks. R provides more than 10,000 packages for different purposes such as data visualization, data manipulation, machine learning , statistical analysis and
so on. Next in line is python. Well, python and R
are actually in close competition. Python also provides packages for deep learning such as “keras” and “tensorflow” which help in creating deep neural networks quite easily. We can also use our good old java to perform data science tasks. The biggest reason why people use Java is Speed and scalability with Big Data.