Data Science

What is Data Science?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The data used for analysis can be from multiple sources and present in various formats.

Now that you know what is data science, let’s see why data science is essential in the current scenario.

Why Data Science?

Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:

• Find the leading cause of a problem by asking the right questions
• Perform exploratory study on the data
• Model the data using various algorithms 
• Communicate and visualize the results via graphs, dashboards, etc.

In practice, data science is already helping the airline industry predict disruptions in travel to alleviate the pain for both airlines and passengers. With the help of data science, airlines can optimize operations in many ways, including:

• Plan routes and decide whether to schedule direct or connecting flights
• Build predictive analytics models to forecast flight delays
• Offer personalized promotional offers based on customers booking patterns 
• Decide which class of planes to purchase for better overall performance

In another example, let’s say you want to buy new furniture for your office. When looking online for the best option and deal, you should answer some critical questions before making your decision.

Prerequisites for Data Science

Here are some of the technical concepts you should know about before starting to learn what is data science.

1. Machine Learning

Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics. 

2. Modeling

Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics

Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming

Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Databases

A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.