This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data. Statistics and mathematics are fundamental to the field of data science and data analytics. A strong foundation in these subjects is essential for understanding and working with data. Statistics is the science of collecting, analyzing, and interpreting data. It involves using statistical methods and techniques to understand patterns and trends in data, and to make predictions and decisions based on that data. Some key areas of statistics that are important for data science and data analytics include descriptive statistics, probability theory, hypothesis testing, and regression analysis.

Absolutely no previous experience required. Absolutely no previous experience required. Are you aiming for a career in Data Science or Data Analytics? Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory.

In the book'Bad Data Handbook' Q Ethan McCallum has rightly said, "We all say we like data, but it's not the data but the insights that we derive from it are what we care about." Yet, a data analyst gets to dedicate only 20% of her time to the art and science of generating insights out of data. The rest of her time is spent in structuring and cleaning the data. In order to minimize the time investment in data cleaning, there is a need of standardized frameworks and tools that work for the diverse data and business use cases across industries, functions, and domains. This blog aims to equip you with the knowledge you need to build and execute such standardized data quality frameworks that work for your data and use cases.

This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions, decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly.

The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science. If you too aspire to work for such top companies in future, it is essential for you to develop a mathematical understanding of data science. Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic. I've met many data scientists who struggle to explain predictive models statistically. More than just deriving accuracy, understanding & interpreting every metric, calculation behind that accuracy is important.

The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. Not just Google, other top companies (Amazon, Airbnb, Uber etc) in the world also prefer candidates with strong fundamentals rather than mere know-how in data science. If you too aspire to work for such top companies in future, it is essential for you to develop a mathematical understanding of data science. Data science is simply the evolved version of statistics and mathematics, combined with programming and business logic. I've met many data scientists who struggle to explain predictive models statistically. More than just deriving accuracy, understanding & interpreting every metric, calculation behind that accuracy is important.