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Description

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.
Who this course is for:
Data Analysis professionals looking to apply their skills to people management decisions.
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.
Students learning HR.
Students or any individual who want to advance into HR.
Business owners and Entrepreneurs.
And the most important is applying machine learning on HR Data and predicting futuristic insights.

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

  • 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: Welcome to HR Analytics Master Course
1
1.1-Introduction
2-Resources & Anatomy of Statistical Modelling
2
2.1-Resources (IMPORTANT)
2.2-Anatomy of Statistical Modeling
3-Installation and Introduction to Python
3
3.1-Installation of Python
3.2-Getting Started with Python Packages
3.3-Getting Started with Jupyter Notebook
4-Installation and Introduction of R and Rstudio
1
4.1-Installation and Introduction of R and Rstudio
5-Importing Excel File in Python and R
1
5.1-Importing Excel File in Python and R
6-Understand, Import and Validate
14
6.1-Population and Sample
6.2-Quiz
6.3-Types of Data
6.4-Quiz
6.5-Types of Data - Python
6.6-Quiz
6.7-Types of Data - R
6.8-Quiz
6.9-Data Dictionary
6.10-Quiz
6.11-Validate for Correctness- Python
6.12-Quiz
6.13-Validate for Correctness- R
6.14-Quiz
7-Statistics Part-1 : Univariate (Numerical Variable)
34
7.1-Univariate Analysis
7.2-Quiz
7.3-Mean/Median/Mode - Excel
7.4-Quiz
7.5-Decoding Median
7.6-Quiz
7.7-Mean/Median/Mode - Python and R
7.8-Quiz
7.9-Measures of Variability
7.10-Quiz
7.11-Range - Python and R
7.12-Quiz
7.13-Inter-Quartile Range
7.14-Quiz
7.15-Inter-Quartile Range - Python and R
7.16-Quiz
7.17-Variance
7.18-Quiz
7.19-Variance- Excel, Python and R
7.20-Quiz
7.21-Standard Deviation
7.22-Quiz
7.23-Standard Deviation- Excel, Python and R
7.24-Quiz
7.25-Histogram- Python & R
7.26-Quiz
7.27-Skewness
7.28-Quiz
7.29-Skewness- Python and R
7.30-Quiz
7.31-Standardization
7.32-Quiz
7.33-Standardize in Python and R
7.34-Quiz
8-Statistics Part-2 : Univariate (Categorical Variable)
8
8.1-Count, Frequency and Percentage
8.2-Quiz
8.3-Count and Percentages- Python and R
8.4-Quiz
8.5-Bar chart and Pie chart - Excel
8.6-Quiz
8.7-Bar chart and Pie chart- Python and R
8.8-Quiz
9-Data Cleaning Part-1
9
9.1-Introduction
9.2-Missing Values
9.3-Quiz
9.4-Missing Value - Excel
9.5-Quiz
9.6-Missing Value - Python
9.7-Quiz
9.8-Missing Value - R
9.9-Quiz
10-Data Cleaning Part-2
11
10.1-Understanding Outlier
10.2-Quiz
10.3-Understanding Boxplot
10.4-Quiz
10.5-Detection by BoxPlot - Python and R
10.6-Quiz
10.7-Detection by Z-Score - Excel, Python and R
10.8-Quiz
10.9-Treating Outlier by Mean and Median - Python and R
10.10-Quiz
10.11-Treating by Flooring and Capping - Python and R
11-Statistics Part-3 : Bi-variate Introduction
2
11.1-Introduction to Bi-Variate
11.2-Quiz
12-Statistics Part-3 : Bi-variate Part-1 (Numerical Numerical)
20
12.1-Scatter Plot
12.2-Quiz
12.3-Scatter Plot - Python and R
12.4-Quiz
12.5-Scatter Matrix - Python and R
12.6-Quiz
12.7-Co-Variance
12.8-Quiz
12.9-Co-Variance in Excel
12.10-Quiz
12.11-Co-Variance - Python and R
12.12-Quiz
12.13-Co-Variance Matrix - Python and R
12.14-Quiz
12.15-Correlation
12.16-Quiz
12.17-Correlation - Python and R
12.18-Quiz
12.19-Correlation Matrix - Python and R
12.20-Quiz
13-Statistics Part-3 : Bi-variate Part-2 (Categorical Categorical)
14
13.1-Cross-tab
13.2-Quiz
13.3-Cross-Tab - Python and R
13.4-Quiz
13.5-Proportion Table
13.6-Quiz
13.7-Proportion Table - Python and R
13.8-Quiz
13.9-Stacked/Bar/Column Chart in Excel
13.10-Quiz
13.11-Stacked/Bar/Column Chart in Python and R
13.12-Quiz
13.13-Mosaic Plot - Python and R
13.14-Quiz
14-Statistics Part-3 : Bi-variate Part-3 (Numerical Categorical)
4
14.1-Category wise Descritive Statistics
14.2-Quiz
14.3-Box Plot
14.4-Quiz
15-Hypothesis Testing
25
15.1-Introduction to Hypothesis Testing
15.2-Quiz
15.3-Steps in Hypothesis Testing
15.4-Quiz
15.5-Formulate Hypothesis
15.6-Quiz
15.7-When to do which Hypothesis Test
15.8-Quiz
15.9-Significance level
15.10-Quiz
15.11-Apply test find P value and compare with Significance Level
15.12-Quiz
15.13-Quiz
15.14-One sample t-test R and Python
15.15-Quiz
15.16-Paired t-test Python and R
15.17-Quiz
15.18-Chi-square in Python and R
15.19-Quiz
15.20-ANOVA in Python and R
15.21-Quiz
15.22-Decoding Chi-Square
15.23-Quiz
15.24-Decoding ANOVA
15.25-Quiz
16-Feature Engineering Part-1 Variable Creation
14
16.1-Introduction
16.2-Derived Variables Introduction
16.3-Derived Variable 'Tenure' - Excel, Python and R
16.4-Quiz
16.5-Derived Variable 'Compa Ratio' Introduction
16.6-Derived Variable 'Compa Ratio' - Excel, Python and R
16.7-Quiz
16.8-Dummy Variables Introduction
16.9-Quiz
16.10-Dummy Explained
16.11-Dummy Variable - Python
16.12-Quiz
16.13-Dummy Variable in R
16.14-Quiz
17-Feature Engineering Part-2 Variable Transformation
17
17.1-Variable Transformation Introduction
17.2-Normalization/ Standardization
17.3-Quiz
17.4-Normalization/ Standardization - Python and R
17.5-Quiz
17.6-Log & Square Root Transformation
17.7-Quiz
17.8-Log & Square Root Transformation - Python
17.9-Quiz
17.10-Log & Square Root Transformation - R
17.11-Quiz
17.12-Binning
17.13-Quiz
17.14-Binning - Python
17.15-Quiz
17.16-Binning - R
17.17-Quiz
18-Data Partitioning
4
18.1-Introduction
18.2-Quiz
18.3-Data Split - Python & R
18.4-Quiz
19-Linear Regression
20
19.1-Introduction to Linear Regression
19.2-Visually Understanding Linear Relationships
19.3-Business Problem
19.4-Types of Linear Regression
19.5-Building First Linear Regression Model
19.6-Simple Linear Regression Equation
19.7-Visualizing Linear Regression
19.8-Interpretation of Linear Regression equation
19.9-Ordinary Least Square
19.10-Errors Explained
19.11-R- Squared
19.12-Multiple Linear Regression
19.13-Adjusted R-Square
19.14-F-Test
19.15-Simple Linear Regression in R and Python
19.16-Multiple Linear Regression in R and Python
19.17-Choosing Significant Factors in R
19.18-Stepwise Regression
19.19-Multicollinearity
19.20-Multicollinearity in R
20-Logistic Regression Case Study
17
20.1-Introdution to Attrition Case
20.2-Data Validation
20.3-Univariate Analysis
20.4-Feature Engineering
20.5-Bivariate Analysis (Categorical-Categorical)
20.6-Chi-square Test
20.7-Bivariate Test (Numerical-Categorical)
20.8-T-Test
20.9-Why Remove Insignificant Variables?
20.10-Removal of Insignificant Variables
20.11-Dummy Variable Creation
20.12-Data Split
20.13-Understanding Logistic Regression
20.14-Building Logistic Regression Model
20.15-Model Evaluation Techniques
20.16-Model Evaluation - Confusion Matrix
20.17-Model Evaluation - ROC, AUC
21-Text Analytics
11
21.1-Business Problem
21.2-Text Classification Approach
21.3-Data Cleaning 1
21.4-Data Cleaning 2
21.5-Data Cleaning 3
21.6-Bag of Words
21.7-Train Test Split
21.8-Building Logistic Regression Model
21.9-Model Evaluation
21.10-Model Deployment
21.11-Model Deployment in Python
22-Clustering Case Study
15
22.1-Business Problem
22.2-Overview of Clustering
22.3-How to Make Clusters
22.4-Distance Measures
22.5-Select a Clustering Procedure
22.6-Hierarchical Clustering Theory
22.7-Finding Optimal Number of Clusters
22.8-Loading Data and Univariate on Demographics
22.9-Univariate on Test Scores
22.10-Finding Optimal Clusters Practical
22.11-Hierarchical Clustering Practical
22.12-Profiling Clusters Part 1
22.13-Profiling Clusters Part 2
22.14-Profiling Clusters Part 3
22.15-Saving Clusters
23-Different Types of charts
8
23.1-Charts
23.2-Types of Charts PDF
23.3-Data for Charts
23.4-Distribution Charts
23.5-Correlation Charts
23.6-Ranking Charts
23.7-Part of a Whole Charts
23.8-Evolution Charts
24-(ADDITIONAL VIDEOS) About HR Analytics
4
24.1-What is Human Resources and its Importance
24.2-Key objectives of HR Analytics
24.3-People Analytics applied to Employee Life Cycle
24.4-Critical Areas of People Analytics
25-(ADDITIONAL VIDEOS) HR Metrics
5
25.1-Introduction to HR Metrics
25.2-Ecosystem of HR Metrics
25.3-Metrics in critical areas of HR
25.4-Align HR Metrics with overall organizational strategies
25.5-The Journey from Metrics to Analytics