Intermediate Data Science for HR Professionals

Programme Outline

Learning Objectives

By the end of this course, participants should be able to / can expect to:

• Understand and apply the fundamental HR data analytics building blocks and linking them to the core business;
• Learn about the robust methods for data collection
• Interpret a mix of descriptive, predictive, and prescriptive analytics tools, as well as causal inference techniques;
• Learn about basic coding skills and machine learning for manipulating swaths of data;
• Understand data-driven story telling approaches for developing a narrative that makes sense for your business leaders;
• Prepare, analyse and identify business insights for elevating the impact of HR to your organisation’s success.

Participants will also get to work on practical HR problems using machine learning techniques. These problems include

• Employee Turnover Analysis using Logistic Regression
• Developing Engagement Surveys,
• Workforce Planning Analytics,
• Leave Likelihood Analysis,
• Compensation and Benefit data;
• Employee performance;

Day 1

• Introduction to the course
• Understanding the importance of data science and its application in HR business cases
• Types of analytics and machine learning methods used in HR Projects
• Business case sharing
• Regression vs classification supervised learning methods.
• Understanding the concept of regression models covering simple linear regression, multiple linear regression, and polynomial regression.
• Understanding gradient descent optimization.
• Understanding the importance of feature scaling.
• Understanding the concept of classification models covering logistic regression and decision trees.

Day 2

• Introducing Python programming
  • Basics of Object-Oriented Programming (OOP)
  • Data Types, Variables, Classes
  • Functions
  • String, Special Characters, Date, Regex handling
  • Data Structures
• Data Acquisition with Python
  • Connecting to a database
  • Querying a database and loading data into a DataFrame
• Combining multiple DataFrames

Day 3

• Data Preparation on HR dataset with Python
  • Introducing various data profiling and cleaning techniques.
  • Understanding data enrichment and the importance of feature engineering.
  • Understanding the importance of feature scaling.
  • Performing train-test split and cross validation.
• Data Science Modelling on HR dataset with Python
  • Building machine learning models.
  • Evaluating the performance of machine learning models.
  • Optimizing the performance of your machine learning models.

Day 4

Intermediate Data Storytelling
Under the intermediate data storytelling, the emphasis is on deriving insights from visuals and putting the visuals together in a dashboard.

• Data vs Insights – Data Science Lifecycle and DIKW Pyramid
• Insights from Visuals – Exploratory Data Analysis (Techniques)
• Enhanced SPSN
• Dashboard – Design and Considerations
• Individual Quiz

This is a quick, practical introduction to PowerBI. At the end of this session, participants should:

• Understand additional PowerBI elements that enhance user interactivity
• Have exposure to basic Data Analysis Expressions (DAX)
• Be able to create visuals with dynamic features
• Know how to put together a dashboard to tell a story

This course does not cover programming or advanced DAX formulas and leverages on knowledge and pre-work done during PowerBI fundamentals.

Contents

• Applying filters and drill-throughs
• Binning and grouping
•  Data Analysis Expressions (DAX) basics
• Calculated Columns
• Measures + Level of Details
• Dynamic Features
  • Parameters
  • Dimensions
  • Measures
  • Chart Titles
• Dashboard Creation
• Dashboard Interactivity
• Individual Quiz

Assessment

• Project
• Presentation

Subject Credits

Upon completion and satisfying the requirements of passing this course, learners will be awarded 12 subject credits.