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Description

People analytics is also known as HR 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 decision and management for your company. If we talk about numbers, research by McKinsey shows the people analytics can help in an 80% increase in recruiting efficiency, 25% rise in business productivity and 50% decreases in attrition rate.
Once you have completed the course, you can help your company to better drive the return on their investment on their employee. 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.
You will understand how and when to use the people’s data to make decisions on
Hiring,
Recruitment,
Talent Development,
Employee Retention,
Employee Satisfaction,
Employee Engagement, etc.
Don't worry we will not going to perform complex talent management data analysis, but guide you to reach that step.
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. Let's take a look at the structure. We will start with the
Understanding of what is analytics and why it is required.
Areas where you need to apply your business analytics understanding.
Understanding and acting on talent data across the entire employee life cycle.
Understanding a business problem
Finding a better analytical solution to that business problem
How to collect data to solve that problem
How to Find solutions using basic analytics technique or you can say Exploratory Data analytics.
Applying feature engineering techniques to get the most out of data.
You will also learn, how to do hypothesis testing and what are various techniques.
And the most important is applying machine learning on HR Data and predicting futuristic insights.
Oh, and wait, you are also learning data analysis techniques, which you can apply anywhere.
We intend to introduce you to "Businesses prosper when the people who work in them prosper". People analytics 101 is curated to help make both happen and at the same time, you flourish in your career growth, too.
Who this course is for:
HR Professionals who want to incorporate data analysis into their practice.
Managers who want to make data-driven decisions about employee, teams and their management practices.
Data Analysis professionals looking to apply their skills to people management decisions.
Students learning HR.
Students or any individual who want to advance into HR.
Business owners and Entrepreneurs.

What you'll learn

A Step by Step Approach to solve Business Problems in the area of People Analytics.

Understand the concepts of Statistical model building.

Journey of Analytics.

HR analytics and its importance.

Employee life cycle the areas where you can use analytics.

Get an understanding of HR metrics and the Journey from Metrics to Analytics.

Identify a business problem and its importance. You’ll also learn how to convert a business problem into statistical problem.

Understand the science behind gathering the data from various sources and how to do it right.

Understand how to create an efficient data dictionary for better understanding and future reference.

Identify the dependent and independent variable in your dataset.

Understand and learn about various file formats in which the data is stored 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.

R and Rstudio: Installation, importing files and installation of packages.

Understand the concept of Machine Learning – Supervised and Unsupervised Learning Techniques.

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction to People Analytics 101
1
1.1-Introduction
2-Journey of Analytics
4
2.1-Journey of Analytics
2.2-What is Analytics and Why it is important?
2.3-Analytics Maturity Model
2.4-Extras
3-Anatomy of Statistical Modeling
1
3.1-Anatomy of Statistical Modeling
4-(Theory) - Understanding Business Problem
2
4.1-Understanding Business Problem
4.2-Knowledge Check (Business Problem Understanding)
5-(Practical) - Hands-on Project Introduction and Business Problem
3
5.1-Introduction
5.2-1. Employee Turnover
5.3-The Business Problem
6-Installation of R & R Studio
1
6.1-Installation of R & R Studio
7-(Theory) - Data Discovery & Collection
8
7.1-Full code
7.2-Introduction to Data Discovery & Collection
7.3-HR Data Architecture
7.4-Data list preparation and identification of Data Sources
7.5-Collect initial Data
7.6-Define Variables and create Data Dictionary
7.7-Data Verification
7.8-Knowledge Check (Data Collection)
8-(Practical) - Data Discovery & Collection
3
8.1-Resources
8.2-Defining Variables and Data Dictionary
8.3-Data Verification
9-(Theory) - Data Preparation
10
9.1-Introduction to Data Preparation
9.2-Uni-Variate Analysis
9.3-Missing value treatment
9.4-Outlier Detection & Treatment
9.5-Feature Engineering - Variable Creation
9.6-Feature Engineering - Variable Transformation
9.7-Feature Engineering - Dimension Reduction
9.8-Hypothesis Testing and Bi-Variate Analysis
9.9-Data Split
9.10-Knowledge Check (Data Preparation)
10-(Practical) - Data Preparation
8
10.1-Univariate Analysis
10.2-Feature Engineering Part - 1
10.3-Bi-Variate Analysis Part - 1 (Categorical- Categorical)
10.4-Bi-Variate Analysis Part - 2 (C-C Hypothesis Testing)
10.5-Bi-Variate Analysis Part - 3 (Numerical - Categorical)
10.6-Bi-Variate Analysis Part - 4 (Numerical - Categorical Hypothesis Testing)
10.7-Feature Engineering Part - 2 (Dummy Variable Creation)
10.8-Data Split
11-(Theory) - Model Selection & Building
2
11.1-Model Selection & Building
11.2-Knowledge Check (Model Selection)
12-(Practical) - Model Selection & Building
2
12.1-Random Forest Theory and Code
12.2-Understanding GINI Impurity
13-(Theory) - Model Evaluation
4
13.1-Introduction to Model Evaluation
13.2-Regression Model Evaluation
13.3-Classification Model Evaluation
13.4-Knowledge Check (Model Evaluation)
14-(Practical) - Model Evaluation
1
14.1-Random Forest Model Evaluation
15-Conclusion
1
15.1-Conclusion
16-(BONUS) Journey of Analytics
4
16.1-Information Regrading Bonus Lecture
16.2-1. Tools for Analytics
16.3-2. Areas of Business Analytics
16.4-Knowledge Check (Journey of Analytics)
17-(BONUS) About HR Analytics
4
17.1-3. What is Human Resources and its Importance
17.2-4. Key objectives of HR Analytics
17.3-5. People Analytics applied to Employee Life Cycle
17.4-6. Critical Areas of People Analytics
18-(Bonus) HR Metrics
6
18.1-7. Introduction to HR Metrics
18.2-8. Ecosystem of HR Metrics
18.3-9. Metrics in critical areas of HR
18.4-10. Align HR Metrics with overall organizational strategies
18.5-11. The Journey from Metrics to Analytics
18.6-Knowledge Check (HR Metrics)
19-Final Test
1
19.1-Final Test
20-Final Bonus
1
20.1-More Courses coming soon