What you'll learn:
- 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.
HR analytics is also known as people analytics or you can say talent analytics. It is kind of analytics which helps HR managers, executives to make data-driven decisions about their employee or the workforce. It gives you expertise in using statistics, technology on unused but very important people’s data which can help you in making better business decisions and management for your company.
In this course, we take you on a journey where you start from a simple topic of calculating mean and move on to many complex topics such as text analytics. Hence you kickstart from statistics and land on machine learning techniques.
Once you have completed the course, you can help your company to better drive the ROI. Classic approaches are not sufficient in getting the required result in the long run.
To overcome this gap we came up with a solution where you can learn the techniques of solving these problems on your own in a very simple and intuitive self-paced learning method.
We have tried to create a very simple structure for this course so even if you have no knowledge or very basic knowledge of analytics then even you won't face any problem throughout the course. In this course you will:
Learn applied statistics right from scratch.
Simultaneously learn analytics on R and Python.
Identify the dependent and independent variables in your dataset.
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.
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.
And the most important is applying machine learning on HR Data and predicting futuristic insights.
