What is data science, and how it is helping brands stand out from the competition?

There is a lot of buzz about data science in the market, and I’m sure you’re aware that data science is the most flourishing field right now. But there are a lot of misunderstandings about data science, and most people don’t even understand what data science is. Keep reading, we will cover everything about data science in the easiest way possible with examples. 

What is Data Science?

Data science is a technology that analyzes complex data that provides key business insights that help businesses make better decisions. This, in turn, improves the profits of a company.

Most people think that data science is just a programming language. Data Science is a technology that requires three key ingredients: the first ingredient being the domain knowledge, the second ingredient being statistics, and probability, and the third ingredient being programming skills. So Data Science is not merely a programming language but something more than that.

Let us try to understand the concept of data science using an example.

In 2019 Liverpool football club of England won the English Premier League. This all started in 2015 when new manager Jurgen Klopp was hired, and he decided to use data science with the in-house expertise of Ian Graham who is the head of research data science at Liverpool Football Club. 

But you must be wondering why the Liverpool Football Club took four years to win the championship despite using data science in the year 2015.  

Data Science takes time to give results, and it’s a long process consisting of various steps.

The four-step process of data science is given below: 

1. Objective of analysis.

2. data collection.

3. data cleansing.

4. Data Visualization.

Let’s understand them one by one- 

1. Objective of analysis: First of all, you have to find out the purpose of doing the analysis. Here in Liverpool’s case, the purpose of doing the analysis was how to plug gaps in the team and win the championship in the coming years.

2. Data Collection: This is the most crucial step as here we collect the required data that is going to be the pillar for everything and the outcome will be dependent on this step.

So, in Liverpool’s case, they collected the required data from all the newspapers, magazines, and fan clubs and made a consolidated list of which players and what skills they wanted.

3. Data Cleansing: After the data that has been collected from various sources. It has to be cleansed. Data Cleansing means segregating the data accordingly and removing unnecessary data. 

Data cleansing is tedious yet the most important aspect in the process of data science. It is important to have crisp and clear data if one wants to have the desired results.   

4. Data Visualization: Data Visualization is the final step in this process. It means presenting the data in the easiest way possible. Commonly Graphs and pie charts are used for visualization. Because not everyone can understand the complex stats involved.

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