“Data is a precious thing and will last longer than the systems themselves.”
- Tim Berners Lee, Inventor of The World Wide Web
Data is everywhere today. Let us have a look at some interesting stats about data.
- In 2020, every person created around 1.7 MB of data every second.
- Nearly 500 million tweets are made every day and 306.4 billion emails are sent every day.
- On Instagram, 95 million photos and videos are shared every single day.
- By 2025, the amount of data created each day will reach up to 463 exabytes.
- According to Statista, there are nearly 4.66 billion active Internet users across the globe.
- Around 2.5 quintillion bytes of data is generated by our activities over the Internet in a single day.
When such huge amounts of data are generated, there is an equal need to analyze it to make it meaningful and extract hidden insights from it. Given the critical need for analyzing and making data useful, the demand for Data Scientists is skyrocketing in almost every corner of the world.
This article will let you know the importance of data, and eventually, a Data Science career. The job of a Data Scientist is regarded as “the hottest job of the 21st century”.
You would surely like to get into this hottest career. Data Science training can help you enter the world of data, where you will use numerous techniques to extract meaningful insights from the data and make it into a user-readable format.
Did You Know?
Nearly 2.7 million jobs are estimated for positions in Data and Analytics across the globe by 2022.
Data Scientists earn an average annual salary of around USD 62,833 to USD 137,870.
Table of Contents
What is Data?
As far as Data Science is concerned, data is the foundation of Data Science. All the analysis is done over the data. Data serves as fuel to drive the business in the right direction and provides meaningful insights such that strategic decisions can be taken in the scenarios like arranging campaigns, launching new products/services, or trying different appraisals.
In the digital era in which we live today, we generate huge amounts of data that is equired to be analyzed to make it useful.
Do you know how much data is produced by Flipkart in a single day?
It’s 2 TB.
It is essential to store the data properly so that it can be processed seamlessly. To deal with datasets, it is crucial to consider the category of data so that you can determine which processing strategy can be applied to the dataset to get the desired result.
Types of Data
The image above shows the overview of types of data you come across as a Data Scientist.
● Quantitative Data
This data type considers numeric values that are countable. You come across this data very often, for instance, the price of a TV set, the discount offered, the number of channels it supports, etc.
There are numerous values that an attribute can take. Every brand has its price and its own set of features. So, this data type is further divided into two subcategories.
This data type refers to the whole numbers that cannot be subdivided. The number of speakers in a TV, the number of buttons, the number of USB ports, and the number of channels in it are examples of Discrete data type.
The numbers that can be further divided or broken down come under continuous data types. The android version of your smart TV, the frequency it supports are continuous data types.
Continuous data types again are of two types.
Numbers with known differences between variables come under Interval, and the numbers with measurable intervals so that the difference can be identified come under Ratio data type.
● Qualitative Data Type
Also referred to as Categorical Data, qualitative data refers to non-numerical data. There is a finite set of discrete classes, and the data cannot be counted. Instead, it is divided into categories. For example, responses of type yes/no.
Usually, these types of data are extracted from surveys, images, videos, or text. Coming to the example of TV, the attributes like its color, category, or screen type can be qualitative data types.
The two subcategories of qualitative data are:
There is no natural sequence in this data type. An example of nominal data type is color of TV, budget segment, or is its midrange.
When the values have a natural sequence, you can maintain the class of values. The best example of Ordinal data types is the grading system in schools where A+, A, B+, B, etc., are given according to the results obtained.
The categories are such that they help you decide the encoding strategies to be applied to the data. It is required to apply data encoding to the qualitative data because machine learning algorithms can’t handle these values directly and are required to be converted to numerical data types. This is because the machine learning models are numerical.