Description
Statistics Essentials for Analytics
A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real world data set to analyze and conclude insights. Statistics and its methods are the backend of Data Science to “understand, analyze and predict actual phenomena”. Machine learning employs different techniques and theories drawn from statistical & probabilistic fields.
The self-paced Statistics Essentials for Analytics Course has been designed in such a manner that it is easy for a future Data Scientist to get a solid foundation on the concepts. The complete mechanism of Data Science is explained in detail in terms of Statistics and Probability. Data and its types are discussed along with different kind of sampling procedures.
Other essential concepts of Statistics (statistical inference, testing, clustering) are emphasized here as well since that’s a very important part of being a Data Scientist. In addition, you will be introduced to primary machine learning algorithms in this Course.
Objectives:
At the end of this Module, you should be able to:
- Understand various data types
- Learn Various variable types
- List the uses of variable types
- Explain Population and Sample
- Discuss sampling techniques
- Understand Data representation
Topics:
- Introduction to Data Types
- Probability and its uses
- Statistical Inference
- Data Clustering
- Testing the Data
- Regression Modelling
