What Is Data Science
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us find out the hidden insights or patterns from raw data, which can be of significant use in the formation of big business decisions.

In data science, one deals with both structured & unstructured data. The algorithms also involve predictive analytic in them. Thus, data science is all about the present & future. Finding out the trends based on historical data can be useful for present decisions and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.

Why Learn Data Science?

With the amount of data generated and the evolution in analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT, or Bank All are looking for Data Scientists. This has led to a great demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a high income job for many. This field is such that anyone from some background can make a career as a Data Scientist.

Components Of Data Science

Data Science consists of 3 parts, namely: Machine Learning, Big Data, Business Intelligence.

Machine Learning: Machine Learning involves algorithms & mathematical Techniques, chiefly employed to make machines learn & prepare them to alter to everyday advancements. For example, time-series forecasting is very much in use in trading and financial systems these days. Based on historical data This is an application of machine learning. Patterns, the machine can predict the outcomes for the coming months or years.

Big Data: every day, humans produce so much data in clicks, orders, videos, images, comments, articles, RSS Feeds, etc. These data are generally unstructured and are often called Big Data. Big Data tools & Method mainly help in reshape This unstructured data into a structured form. For example, suppose someone wants to track the prices of various products on e-commerce sites. He / she can access the same products from other websites using Web APIs, and RSS Feeds. Then convert them into structured form.

Business Intelligence:-Each business has & produces too much data every day. When analyzed carefully and then presented in visual reports involving graphs, this data can bring good decision-making to life. This can help the management take the best decision after carefully delving
Skills required to become a data scientist include: into patterns and details the reports bring to life.

Tools

In depth skills in R:-R is used for data analysis, as a programming language, as an environment for statistical analysis, and data visualization

Python coding:-Python is majorly preferred to implement mathematical models & concepts because python has rich libraries / packages to build & deploy models.
MS Excel: Microsoft Excel is considered an essential requirement for all data entry jobs. It is of great use in data analysis, applying formulas, equations, diagrams out of a messy lot of data.

Hadoop Platform:-It is an open source administer processing framework. It is used for managing the processing & storage of big data Supplication.

SQL database / coding: It is mainly used for the preparation and extraction of data sets. It can also be used for Graph and Network Analysis, Search behavior, fraud detection, etc.

Technology: Since there is so much unstructured data out there, one also should know how to access that details. This can be done in a variety of ways, via APIs or web servers.

Techniques

Mathematical Expertise: Data scientists also work on machine learning algorithms such as regression, clustering, time series, etc., requiring a very high amount of mathematical knowledge since they are based on mathematical algorithms.

Working with unstructured data:-Since most of the data manufacture every day, in the form of images, comments, posts, tweets, search history, etc. is formless, it is a beneficial skill in today's market to know how to convert this unstructured into a structured form and then working with them.

Business Understanding:
Business Acumen: Analytic Professionals come in the mid management to high management in the ranking. So, having business knowledge comes as a significant requirement for them.

Model building process in Data Science

Job Roles

Business Analytics Professional
Business Intelligence Professional
Data Scientist
Big Data Analysts
HR Analytic Professionals
Marketing Analytic Professionals

https://www.innomatics.in/advanced-data-science-training-in-hyderabad/