What is Data Science?

 



Data Science

Data Science is a field that involves using various algorithms, techniques and processes to draw valuable insights from data which can be structured or unstructured. To get the best out of the data we combine the  statistics, computer science, mathematics and the domain knowledge which helps us to analyze and interpret the data better.

What is Data?

Data is a collection of raw facts, figures and information about anything which can be used to analyze, draw insights and make decisions.



Types of Data

Data can be structured, unstructured or semi-structured.

Structured data

The data which are organized and has a standardized form like a table with rows and columns are called the structured data. For example, data from databases and spreadsheets. Structured data are easy to use, analyze and sort.

Unstructured data 

The data which are not organized and doesn't have a proper structure are called the unstructured data. For example, media like images and videos, text documents and social media content. 

Semi-structured data

The semi-structured data are those which don't have a proper format but the data has spaces, markers or commas to keep the elements in the data separated. For example, data in HTML, JSON and XML formats. These data are easier to process than the unstructured data but still, the structured data is the best.

Importance of Data

Data is the main source for us to analyze a organization's progress and to analyze stuffs to make it better. Here we can see few aspects which prioritize the importance of data. 

1. Decision making - Data helps us to make decisions based on the sources we have which helps us to take decisions based on proper evidence. For an instance, to make a decision on a employee's future we need the data about his previous works from the projects which he has worked on. So the data can help us to make a decision which can be either positive or negative evidently.

2. Innovation - Data can help us to improve everything. Asking questions like where we went wrong, how can we improve the results and for analyzing ways to implement the new ideas, we need data. An example for that can be improving safety in a formula one race car, like a Halo. Which was made mandatory in 2018 to improve safety in open wheeled racing cars which helps in protecting head collision of the drivers avoiding fatal accidents. The data from various races from the past helped the engineers to come up with this innovation which is saving lives.


3. Analyze Patterns - To find a recent trend which has really bothering the company or identifying in which period of the year people buy a product more, these kind of things are called patterns. Identifying patterns is one of the core things you have to do as a Data Scientist. For identifying these trends and patterns, data is very essential. 

4. Problem Solving - Apparently, to solve problems we need data. For example, to avoid wastage in a Sandwich store it is better to make sure which days the sales are high like weekends. This would help the store manager to bring the edibles as per the requirement and to keep them fresh. To make this happen we need the data from at least 2-3 past years to see if the trend is constant.


Location: Chicago, IL, USA

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