DATA SCIENCES

Introduction to Data Science

What is Data Science?

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

The major topics in Data Science syllabus are Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Algorithms, amongst others.

Most important decisions are made with only partial information and uncertain outcomes. However, the degree of uncertainty for many decisions can be reduced sharply by public access to large data sets and the computational tools required to analyze them effectively. Data-driven decision-making has already transformed a tremendous breadth of industries, including finance, advertising, manufacturing, and real estate. At the same time, a wide range of academic disciplines are evolving rapidly to incorporate large-scale data analysis into their theory and practice.

Studying data science enables individuals to bring these techniques to bear on their work, their scientific endeavors, and their personal decisions. Critical thinking has long been a hallmark of a rigorous education, but critiques are often most effective when supported by data. A critical analysis of any aspect of the world, may it be business or social science, involves inductive reasoning — conclusions can rarely been proven outright, only supported by the available evidence. Data science provides the means to make precise, reliable, and quantitative arguments about any set of observations. With unprecedented access to information and computing, critical thinking about any aspect of the world that can be measured would be incomplete without the inferential techniques that are core to data science.

Most important decisions are made with only partial information and uncertain outcomes. However, the degree of uncertainty for many decisions can be reduced sharply by public access to large data sets and the computational tools required to analyze them effectively. Data-driven decision-making has already transformed a tremendous breadth of industries, including finance, advertising, manufacturing, and real estate. At the same time, a wide range of academic disciplines are evolving rapidly to incorporate large-scale data analysis into their theory and practice.

Studying data science enables individuals to bring these techniques to bear on their work, their scientific endeavors, and their personal decisions. Critical thinking has long been a hallmark of a rigorous education, but critiques are often most effective when supported by data. A critical analysis of any aspect of the world, may it be business or social science, involves inductive reasoning — conclusions can rarely been proven outright, only supported by the available evidence. Data science provides the means to make precise, reliable, and quantitative arguments about any set of observations. With unprecedented access to information and computing, critical thinking about any aspect of the world that can be measured would be incomplete without the inferential techniques that are core to data science.



Why Data Science?

It’s been said that Data Scientist is the “Sexiest Job of the 21st century”. Why? Because over the past few years, companies have been storing their data. And this being done by each and every company, it has suddenly led to data explosion. Data has become the most abundant thing today.

But, what will you do with this data? Let’s understand this using an example:

Say, you have a company which makes mobile phones. You released your first product, and it became a massive hit. Every technology has a life, right? So, now its time to come up with something new. But you don’t know what should be innovated, so as to meet the expectations of the users, who are eagerly waiting for your next release?

Somebody, in your company, comes up with an idea of using the user-generated feedback and pick things that we feel users are expecting in the next release.

Comes in Data Science, you apply various data mining techniques like sentiment analysis etc, and get the desired results. 

It’s not only this, you can make better decisions, you can reduce your production costs by coming out with efficient ways, and give your customers what they actually want!

With this, there are countless benefits that Data Science can result in, and hence it has become absolutely necessary for your company to have a Data Science Team. Requirements like these led to “Data Science” as a subject today, and hence we are writing this blog on Data Science Tutorial for you. 



Careers in Data Science

The most common careers in data science include the following roles.

• Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis
• Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
• Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
• Business intelligence specialists: Identify trends in data sets
• Data architects: Design, create and manage an organization’s data architecture.