Foundations of Data Science

Effective tools and techniques Data science involves multiple tools and programming languages to visualize the huge amount of data. they are: Python R programming SQL Hadoop Tableau Weka

Here are some examples of data science, how data science is applied in different fields: Amazon uses the recommendation system that recommends users various products based on their historical purchases. Netflix uses data science in providing suggestions based on your interest or on your previous search. Healthcare industries, data, science provides accurate diagnostic measures to cure diseases. Data science assists the film industry to understand its target audience and the recent trends followed in the market.

Applications of data science Data science is known for its versatility, having application in different fields that include: ECommerce Banking Consulting services Manufacturing Transportation Education Finance Movies Working as a life savior healthcare industry

There are 8 major job profiles available for anyone who is interested to work in Data Science. They are the following:

 Data Science has the following components: 1. STATISTICS Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. Statistics began in the ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. 2. VISUALIZATION Visualization is when we display the results of Data Science analysis in a simpler way using diagrams, charts, and graphs. It improves decision making, sense of work, customer relationship and financial performance. 3. MACHINE LEARNING Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. Closely related to computational statistics. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics. 4. DEEP LEARNING Deep lea

What is Data Science?                                        Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. There are 3 important sciences which are formed Data Science. These are: Computer Science Mathematical Statistics Applications Typical Job Duties for Data Scientists Collecting large amounts of unruly data and transforming it into a more usable format. Solving business-related problems using data-driven techniques. Working with a variety of programming languages, including SAS, R, and Python. Having a solid grasp of statistics, including statistical tests and distributions. Staying on top of analytical techniques such as machine learning, deep learning and text analytics. Communicating and collaborating with both IT and business. Looking for order and patterns in data, as well as spotting trends that can help a business's bottom line                [ Reference: https://www.l