What you'll learn:
- End-to-end Statistical project on Renege using logistic regression algorithm in R.
- Understand how Renege affect business in terms of money?
- Master practical skills to solve an HR business problem using Step-by-step approach called “Anatomy of a Statistical Model”.
- Convert Renege business problem into a Statistical problem.
- Understand how to discover and collect data.
- Understand how to prepare and explore the data for meaningful insights.
- Applying feature engineering techniques to get in depth knowledge hidden inside the data.
- Understand what machine learning algorithm you need to apply to predict the probability whether the candidate will renege or not.
- Understand model validation method to check whether the model which you used is giving the accurate result or not.
- Extract major findings and insights from the statistical solution.
- Understand how the insights will help leaders make strategies and policies to improve employee satisfaction.
This is the third course in our series “People Analytics : Learn ~ Practice ~ Implement”.
Our mission is simple – “Help anyone learn Data Science, create projects they were passionate about, and use those projects to improve their careers and lives”.
This hands-on project-based course is the second course on Udemy which offers an end-to end statistical project, guiding you to develop and master practical skills to solve any HR business problem using Step-by-step approach called “Anatomy of a Statistical Model for HR Analytics”.
This is the course you’ve been looking for, to start building a portfolio of great HR Analytics projects. This without any doubt, is one of the best ways you can advance your HR analytics and data science career.
In fact, when we spoke to data science recruiters and hiring managers all over the world, we heard the same thing over and over again: data science portfolios and Git-hub repositories are among the first things they look at. Employers want to see if you can really do the job you’re being hired for, so having real-world projects to prove your skills you’re claiming on your resume is a must, whether or not you have a fancy degree.
Major takeaways:
1. In this course you will learn to use a logistic regression machine learning technique to reduce an important HR issue “RENEGE”
2. This course starts with a fundamental understanding of what is Recruitment process, Various Recruitment metrics you must know, and how renege can actually impact the Business ROI.
3. After finishing this course, you will be able to convert Renege business problem into a Statistical problem, know how to discover and collect data, how to prepare and explore the data for meaningful insights using various methods such as Uni-variate and Bi-variate Analysis, hypothesis testing etc,Apply appropriate machine learning technique to predict offer dropout, extract major findings and insights from the statistical solution and finally how the insights will help leaders make strategies and policies to reduce offer dropout..
4. You will learn all the above with specific tool that is one of the most in demand in the industry right now – R Studio. It’s geared specifically for people who want to learn employable skills in 2019.
5. This course is developed by a team of analytics professional with a in-depth knowledge and understanding of HR domain. We wanted to build something that would not only teach students HR Analytics in a fun, hands-on way, but that would also help motivate them to keep learning.
In this course, you will be taken through online videos of exercises where you will be able to do the following things by the end:
1. End-to-end Statistical project on Renege using logistic regression algorithm in R
2. Master practical skills to solve an HR business problem using Step-by-step approach called “Anatomy of a Statistical Model”.
3. Understand how Renege affect business in terms of money?
4. Convert Renege business problem into a Statistical problem.
5. Understand how to discover and collect data.
6. Understand how to prepare and explore the data for meaningful insights.
7. Applying feature engineering techniques to get in-depth knowledge hidden inside the data.
8. Understand what machine learning algorithm you need to apply to predict the probability whether the candidate will renege or not.
9. Understand model validation method to check whether the model which you used is giving the accurate result or not.
10. Extract major findings and insights from the statistical solution.
11. Understand how the insights will help leaders make strategies and policies to reduce offer dropout.
This course and all other courses in this series is the accumulation of all of our years of working in Data Science, Human Resource, learning, and teaching and all of the frustrations and incomplete information we have encountered along the way. There is so much information out there, so many opinions, and so many ways of doing things. So, this course is the answer to that exact problem. Throughout the years I have taken notes on what has worked, and what hasn't and I've created this course to narrow down the best way to learn and the most relevant information.
We firmly believe that you won’t find a course like this out there that is as well organized, and as useful, to build a strong foundation for you.
