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Trending September 2023 # Why Do We Need To Learn Data Science? # Suggested October 2023 # Top 10 Popular

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Data Science Tutorial and Resources

We are hearing a lot about data nowadays since the internet has become an ever-increasing knowledge plate form. Nowadays, a particular individual generates TeraBytes of data in a week due to their social network commitments and other internet usage. 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, health predictions and weather forecasting, and so on. Data drive every application on the mobile or the internet we are using. All the major companies are hugely investing in 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 we are only generating, we are leaving a footprint of us in the form of data while browsing the internet or surfing around 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 that is bifurcated from the field of computer science. Its an amalgamation of various Fields, such as in the below figure.

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 important as the data needs to be analyzed and plotted to identify its intricacies.

3. Machine learning: This comprises 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 a must in this scenario)


There is the various application of data science, such as:

 Credit card fraud detection

Recommendation engines

Internet search

Speech recognition

Airline Route Planning

Weather Forecasting

Sales Forecasting

Expenditure Forecasting

Augmented reality


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

For example, consider a beverage company (ABBeverage) that wants to launch a special offer in the new year for its users.

That beverage company is 12 years old and has its sales data for 12 years.

So the beverage company will hire a data scientist and ask them to analyze their 12 years of sales data and predict which brand they can provide a discount on and which brand they cannot.

So the data scientist analyzed their sales data for each brand and then told them to give a discount on the x brand, not a discount on the 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.


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

Target Audience

This tutorial targets software professionals and software engineering graduates or any other individual who has basic programming knowledge and wants to learn and make a career for himself in the field of data science.

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