People Analytics 101 : HR Analytics Fundamentals
4.0 (1,057 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,495 students enrolled

People Analytics 101 : HR Analytics Fundamentals

Conceptualize the Basics of Statistical Model Building.
4.0 (1,057 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
3,495 students enrolled
Created by Unlock HR
Last updated 4/2020
English [Auto]
Current price: $128.99 Original price: $184.99 Discount: 30% off
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This course includes
  • 9 hours on-demand video
  • 6 articles
  • 11 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
  • Anyone can take this course. You do not need any prior knowledge or additional equipment.

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

  1. Hiring,

  2. Recruitment,

  3. Talent Development,

  4. Employee Retention,

  5. Employee Satisfaction,

  6. 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

  1. Understanding of what is analytics and why it is required.

  2. Areas where you need to apply your business analytics understanding.

  3. Understanding and acting on talent data across the entire employee life cycle.

  4. Understanding a business problem

  5. Finding a better analytical solution to that business problem

  6. How to collect data to solve that problem

  7. How to Find solutions using basic analytics technique or you can say Exploratory Data analytics.

  8. Applying feature engineering techniques to get the most out of data.

  9. You will also learn, how to do hypothesis testing and what are various techniques.

  10. And the most important is applying machine learning on HR Data and predicting futuristic insights.

  11. 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.
Course content
Expand all 59 lectures 08:48:16
+ Journey of Analytics
4 lectures 17:32

In this chapter, you will learn

  1. What exactly is Analytics?

  2. Why analytics is required in the first place?

  3. What is analytics maturity model?

  4. What tools do we need for data analytics?

  5. What are the areas where we can apply analytics?

  6. And the most important we will help you with step by step approach to solve any business problem faced by you on a day to day basis. We call it ANATOMY OF STATISTICAL MODEL here at

Journey of Analytics
What is Analytics and Why it is important?
Analytics Maturity Model
+ (Theory) - Understanding Business Problem
1 lecture 09:35

Your basic understanding of how HR works must be clear by now. Let's move to the next step.

Here in this chapter, you will learn :

  1. What is classified as a business problem?

  2. What are the objectives of this project?

  3. How you will define the problem is also a problem. Don't worry we got it covered for you.

  4. Next thing you need to learn is to understand the problems you came up with and what are the requirements to solve that.

  5. And at the end of this chapter, you will learn the most important part, i.e. how you will convert your business problem into the statistical problem.

Understanding Business Problem
Knowledge Check (Business Problem Understanding)
6 questions
+ Installation of R & R Studio
1 lecture 18:17
Installation of R & R Studio
+ (Theory) - Data Discovery & Collection
7 lectures 22:02
Full code

From this chapter, we are going to prepare you for the analytics part.

  1. First of all, we will start with data architecture.

  2. Then we learn to identify all the data sources and create preparation of data list.

  3. Next thing you will learn is how to collect the data for further analysis.

  4. Once the data is collected you need to understand the data first.

  5. Next step in this learning process would be the data import.

  6. Don't you wanna know whether the data you collected is correct or not or how you will do that in R. Don't worry guys we have covered that as well.

Introduction to Data Discovery & Collection
HR Data Architecture
Data list preparation and identification of Data Sources
Collect initial Data
Define Variables and create Data Dictionary
Data Verification
Knowledge Check (Data Collection)
9 questions
+ (Practical) - Data Discovery & Collection
3 lectures 25:37
Defining Variables and Data Dictionary
Data Verification
+ (Theory) - Data Preparation
9 lectures 01:49:50

Well, the most awaited moment is here, now we will start working with data.

  1. The first thing we will learn is how we can get some insight with the help of Uni-variate analysis.

  2. You must have found some problems with the data. Like null values and outliers, that is why the next thing is data cleaning.

  3. After data cleaning, we will move to feature engineering where you will learn what is a feature, why do we need feature engineering in the first place and how do we do feature engineering.

  4. You will learn what is feature creation, variable transformation, and variable reduction.

  5. Great we have prepared the data halfway, next thing we will perform analysis on more that one variable and test some hypothesis. Ohh.. I mean how you will test the hypothesis.

  6. Don't worry we will teach every bit of it in a very simple way.

  7. Next thing we will learn how to get rid of unwanted variables in the data and why do we need to do that.

  8. Finally, you are ready to apply machine learning algorithms, but before that, we will learn to divide our data set into two parts.

  9. To know why let's start this chapter.

Introduction to Data Preparation
Uni-Variate Analysis
Missing value treatment
Outlier Detection & Treatment
Feature Engineering - Variable Creation
Feature Engineering - Variable Transformation
Feature Engineering - Dimension Reduction
Hypothesis Testing and Bi-Variate Analysis
Data Split
Knowledge Check (Data Preparation)
10 questions
+ (Practical) - Data Preparation
8 lectures 01:52:24
Univariate Analysis
Feature Engineering Part - 1
Bi-Variate Analysis Part - 1 (Categorical- Categorical)
Bi-Variate Analysis Part - 2 (C-C Hypothesis Testing)
Bi-Variate Analysis Part - 3 (Numerical - Categorical)
Bi-Variate Analysis Part - 4 (Numerical - Categorical Hypothesis Testing)
Feature Engineering Part - 2 (Dummy Variable Creation)
Data Split