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

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.

* 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

  1. Full resource code for the course (all data sheets, r codes and python codes.)

  2. 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.

Installation of Python Preview 05:39

Getting Started with Python Packages Preview 04:57

Getting Started with Jupyter Notebook Preview 06:19

Population and Sample Preview 03:25

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Types of Data Preview 04:32

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Types of Data - Python Preview 04:47

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Types of Data - R Preview 02:47

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Data Dictionary Preview 02:15

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Validate for Correctness- Python Preview 03:23

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Validate for Correctness- R Preview 01:46

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Univariate Analysis Preview 01:10

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Mean/Median/Mode - Excel Preview 04:35

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Decoding Median Preview 02:39

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Mean/Median/Mode - Python and R Preview 03:45

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Measures of Variability Preview 02:42

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Range - Python and R Preview 02:08

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Inter-Quartile Range Preview 05:33

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Inter-Quartile Range - Python and R Preview 01:41

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Variance Preview 06:14

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Variance- Excel, Python and R Preview 01:58

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Standard Deviation Preview 02:40

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Standard Deviation- Excel, Python and R Preview 01:48

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Histogram- Python & R Preview 04:29

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Skewness Preview 02:52

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Skewness- Python and R Preview 05:17

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Standardization Preview 06:03

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Standardize in Python and R Preview 02:29

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Count, Frequency and Percentage Preview 04:03

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Count and Percentages- Python and R Preview 03:17

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Bar chart and Pie chart - Excel Preview 03:23

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Bar chart and Pie chart- Python and R Preview 03:24

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Introduction Preview 01:19

Missing Values Preview 05:46

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Missing Value - Excel Preview 08:42

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Missing Value - Python Preview 08:36

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Missing Value - R Preview 10:06

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Understanding Outlier Preview 03:25

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Understanding Boxplot Preview 03:39

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Detection by BoxPlot - Python and R Preview 04:26

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Detection by Z-Score - Excel, Python and R Preview 07:33

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Treating Outlier by Mean and Median - Python and R Preview 06:08

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Treating by Flooring and Capping - Python and R Preview 10:53

Scatter Plot Preview 03:45

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Scatter Plot - Python and R Preview 02:37

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Scatter Matrix - Python and R Preview 05:53

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Co-Variance Preview 01:58

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Co-Variance in Excel Preview 05:05

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Co-Variance - Python and R Preview 01:54

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Co-Variance Matrix - Python and R Preview 02:18

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Correlation Preview 08:22

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Correlation - Python and R Preview 02:57

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Correlation Matrix - Python and R Preview 03:58

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Cross-tab Preview 02:47

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Cross-Tab - Python and R Preview 01:50

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Proportion Table Preview 04:44

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Proportion Table - Python and R Preview 02:58

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Stacked/Bar/Column Chart in Excel Preview 05:36

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Stacked/Bar/Column Chart in Python and R Preview 05:40

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Mosaic Plot - Python and R Preview 06:16

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Introduction to Hypothesis Testing Preview 03:04

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Steps in Hypothesis Testing Preview 01:39

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Formulate Hypothesis Preview 03:39

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When to do which Hypothesis Test Preview 02:05

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Significance level Preview 02:13

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Apply test find P value and compare with Significance Level Preview 04:22

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One sample t-test R and Python Preview 07:02

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Paired t-test Python and R Preview 04:07

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Chi-square in Python and R Preview 05:22

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ANOVA in Python and R Preview 06:39

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Decoding Chi-Square Preview 08:37

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Decoding ANOVA Preview 11:19

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Introduction Preview 03:19

Derived Variables Introduction Preview 01:40

Derived Variable 'Tenure' - Excel, Python and R Preview 03:54

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Derived Variable 'Compa Ratio' Introduction Preview 01:59

Derived Variable 'Compa Ratio' - Excel, Python and R Preview 02:37

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Dummy Variables Introduction Preview 01:44

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Dummy Explained Preview 04:07

Dummy Variable - Python Preview 10:19

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Dummy Variable in R Preview 06:53

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Variable Transformation Introduction Preview 01:44

Normalization/ Standardization Preview 02:33

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Normalization/ Standardization - Python and R Preview 05:13

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Log & Square Root Transformation Preview 02:50

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Log & Square Root Transformation - Python Preview 09:32

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Log & Square Root Transformation - R Preview 04:59

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Binning Preview 01:09

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Binning - Python Preview 07:20

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Binning - R Preview 04:09

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Introduction Preview 03:08

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Data Split - Python & R Preview 06:40

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Introduction to Linear Regression Preview 03:10

Visually Understanding Linear Relationships Preview 02:36

Business Problem Preview 02:12

Types of Linear Regression Preview 01:12

Building First Linear Regression Model Preview 01:51

Simple Linear Regression Equation Preview 01:37

Visualizing Linear Regression Preview 03:22

Interpretation of Linear Regression equation Preview 07:52

Ordinary Least Square Preview 02:37

Errors Explained Preview 02:38

R- Squared Preview 03:09

Multiple Linear Regression Preview 03:00

Adjusted R-Square Preview 02:39

F-Test Preview 02:09

Simple Linear Regression in R and Python Preview 06:00

Multiple Linear Regression in R and Python Preview 05:49

Choosing Significant Factors in R Preview 09:08

Stepwise Regression Preview 05:29

Multicollinearity Preview 05:16

Multicollinearity in R Preview 04:41

Introdution to Attrition Case Preview 02:34

Data Validation Preview 03:22

Univariate Analysis Preview 04:28

Feature Engineering Preview 06:35

Bivariate Analysis (Categorical-Categorical) Preview 05:07

Chi-square Test Preview 05:37

Bivariate Test (Numerical-Categorical) Preview 07:33

T-Test Preview 04:21

Why Remove Insignificant Variables? Preview 02:50

Removal of Insignificant Variables Preview 01:27

Dummy Variable Creation Preview 04:18

Data Split Preview 03:32

Understanding Logistic Regression Preview 07:55

Building Logistic Regression Model Preview 06:01

Model Evaluation Techniques Preview 19:42

Model Evaluation - Confusion Matrix Preview 12:10

Model Evaluation - ROC, AUC Preview 02:34

Business Problem Preview 02:45

Text Classification Approach Preview 02:19

Data Cleaning 1 Preview 08:02

Data Cleaning 2 Preview 05:08

Data Cleaning 3 Preview 03:37

Bag of Words Preview 08:45

Train Test Split Preview 07:32

Building Logistic Regression Model Preview 04:48

Model Evaluation Preview 03:55

Model Deployment Preview 00:50

Model Deployment in Python Preview 02:43

Business Problem Preview 02:38

Overview of Clustering Preview 02:51

How to Make Clusters Preview 06:20

Distance Measures Preview 02:05

Select a Clustering Procedure Preview 04:48

Hierarchical Clustering Theory Preview 03:57

Finding Optimal Number of Clusters Preview 01:29

Loading Data and Univariate on Demographics Preview 07:43

Univariate on Test Scores Preview 06:13

Finding Optimal Clusters Practical Preview 04:56

Hierarchical Clustering Practical Preview 04:27

Profiling Clusters Part 1 Preview 06:04

Profiling Clusters Part 2 Preview 03:24

Profiling Clusters Part 3 Preview 07:58

Saving Clusters Preview 01:36

Charts Preview 01:31

Types of Charts PDF Preview 00:02

Data for Charts Preview 00:01

Distribution Charts Preview 00:00

Correlation Charts Preview 00:00

Ranking Charts Preview 00:00

Part of a Whole Charts Preview 00:01

Evolution Charts Preview 00:00

What is Human Resources and its Importance Preview 02:48

Key objectives of HR Analytics Preview 03:38

People Analytics applied to Employee Life Cycle Preview 04:24

Critical Areas of People Analytics Preview 09:12

Introduction to HR Metrics Preview 03:37

Ecosystem of HR Metrics Preview 02:41

Metrics in critical areas of HR Preview 05:11

Align HR Metrics with overall organizational strategies Preview 03:10

The Journey from Metrics to Analytics Preview 06:54