Social media produces a lot of data in an immeasurable amount. Consider the company Amazon, which is a multi-billion dollar company. What makes Amazon rich? Is it the availability of their products or their service? Well, the answer is data. Data makes them very rich. But do you think data is enough to make a business rich? Yes, it is enough. One should know how to use data for useful insights to solve problems. Data science is about using data to create as much impact as possible for a company or an organization. Its impact can be on multiple things in the form of insights, data products, or product recommendations for a company. A job of the data scientist is to solve real company problems with data using any kind of tool. Companies emphasize data to improve their products.
Let’s take the example of Uber. You want to book a cab but there is an unusual increase in the cost of the ride at that time. Why there is an increase in the price? Well, it is because of the higher demand for the cabs at that moment which leads to an increase in the fare of the cab. All this is implemented with the help of data science which is the heart of the surge price algorithm of the company. The surge price algorithm ensures that the customer gets a ride when they need even at the cost of the inflated price. Uber finds out which place will be the busiest using data science, so it can activate surge pricing to get more drivers on the road. In this manner, Uber maximizes the number of rides and hence get benefits from this.
Data science is implemented in 7 steps
- Business requirements
- Data collection
- Data cleaning
- Data Exploration & Analysis
- Data modeling
- Data validation
- Deployment and optimization
In 2001, William S Cleveland combined computer science and data mining to produce data science. He made the statistics more technical which could expand the possibilities of data mining and produce a powerful force for innovation. So now we can take advantage of computer power for statistics.
A data scientist does a descriptive analysis and predictive analysis. Descriptive analysis is all about analyzing historical data to answer the question “what has happened till now?” and predictive analysis is what will happen in the future. It also involves the analysis of historical data. For example, we have sales data of any XY company in tabular form. Once we know how the company has been performing in previous years we can predict the sales of the company in the coming years with the help of predictive analysis.
A data scientist does the descriptive and predictive analytics similar to data analysts. Data science involves maths, statistics, data handling, excellent knowledge of SQL, knowledge about big data tools and technologies to work with large data sets, and structured and unstructured data. Those who have excellent communication and storytelling skills, good in using techniques in artificial intelligence and data mining are strongly recommended to join the best data science courses.
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