Data Science Tutorial for Beginners

Data Science Tutorial

We are hearing a lot about the data nowadays since the internet has become an ever-increasing knowledge plate form and nowadays a particular individual is generating TeraBytes of data in a single week due to his/her social network commitments and other internet usages. This is the best time to be a data scientist since through that particular data one can draw multiple insights from credit card sales to, mobile data sales to health predictions and weather forecasting and so on. Every application in the mobile or the internet we are using is all driven by the data. All the major companies are hugely investing in the field of data science to make themselves future-ready.

Why do we need to learn data science?

As everything around us is completely driven by the data which we are only generating, basically we are leaving a footprint of us in the form of data while we are browsing the internet or surfing around the mobile apps. So to capture and use the huge potential of the data one should learn about this field, as this is the future. Data science is not only a field which is bifurcated from the field of computer science its an amalgamation of various Fields such as below figure

Source Link: https://intellipaat.com/blog/wp-content/uploads/2016/11/What-is-Data-Science.docx.jpg

Basically Data science is the intersection of 3 Fields that are:

1. statistics: This plays a vital role since mathematics is the crux of data science.

2. Data analysis: This is also very imported as the data needs to analyzed and plotted to identify the intricacies of it.

3. Machine learning: This comprises of the various algorithms involving statistics.

Also, the domain knowledge is very much important(for example one is working on credit card fraud detection then banking domain knowledge is must in this scenario)

Applications

There is the various application of data science such as:

•  Credit card fraud detection
• Recommendation engines
• Internet search
• Targeted advertisement
• Speech recognition
• Airline Route Planning
• Weather Forecasting
• Sales Forecasting
• Expenditure Forecasting
• Augmented reality

Example

A simple example of the data science application can be sales forecasting:

• Consider there a beverage company known as (ABBeverage)which wants to launch a special offer in the new year for its users.
• That beverage company is 12 years old and it has its sales data for 12 years
• So that beverage company will hire a data scientist and ask them to analyze there 12 years of sales data and predict which brand they can provide a discount and which brand they cannot.
• So the data scientist analyzed there sales data for each brand and then told them to give a discount on the x brand and not to give a discount on y brand. Since x brand beverage sold the most during the new year and y brand didn’t. But y brand was there a most famous brand of beverage
• Here the data scientist analyzed not only the sales of each brand of beverage but also kept in mind the time of the sale that was(new year)

This is the basic use case of a data science project.

Prerequisites

Before starting with this tutorial one should have a basic knowledge of coding preferably python and also should know how the python code is executed in a particular IDE or have a basic knowledge of a code editor

Target Audience

This tutorial is targeted towards the software professionals and software engineering graduates of any other individual who have a basic knowledge of programming and wants to learn and make a carrier for himself/herself in the field of data science