Hr Analytics
Tags: HR Analytics
The Most Comprehensive Course on Data Analytics and HR Analytics.
Last updated 2022-01-10 | 4.2
- Learn applied statistics right from scratch and move to machine learning.- Simultaneously learn analytics on R and Python.
- Understand the steps involved in data preparation.
What you'll learn
* Requirements
* Anyone can take this course. You do not need any prior knowledge or additional equipment.Description
- Learn applied statistics right from scratch and move to machine learning.
- Simultaneously learn analytics on R and Python.
- Understand the steps involved in data preparation.
- Various methods to measure Central Tendency, Variability and Shape of data.
- Understand the steps involved in hypothesis testing, Univariate and Bi-variate Analysis.
- Learn the concepts of Feature Engineering.
- Identify the dependent and independent variable in your dataset.
- Understand the concepts of Statistical model building.
- Identify a business problem and its importance.
- Understand the concept of Machine Learning – Supervised and Unsupervised Learning Techniques.
- 4 Hands-on case studies.
- More than 20 types of charts/plots.
- All of it with practical approach.
Course content
25 sections • 264 lectures
Resources (IMPORTANT) Preview 01:19
In this module, there are 2 attachments
Full resource code for the course (all data sheets, r codes and python codes.)
Anatomy of Statistical Modelling - it is a step by step approach towards building an analytical project as it reflects what is to be done next in the journey. This tool will always help in your journey and bring you out from any confusion ever. It is designed to how a project is done in the analytics and statistical areas, as to what comes after what and which technique needs to be used.
Anatomy of Statistical Modeling Preview 00:19
It is a step by step approach towards building analytical projects as it reflects what is to be done next in the journey.
This tool will always help you in the process and bring you out of any confusion ever.
It is designed to know how a project is done in the analytics and statistical areas, as to what comes after what and which technique needs to be used. How to clean data, prepare data, and model data.