HR Data Analytics Course
HR Data Analytics certification course
If you’re looking for the best HR Data Analytics training in Vadodara, this comprehensive course is the perfect starting point. Designed for HR professionals, analysts, and aspiring data specialists, this program equips you with the essential tools and techniques to transform workforce data into valuable strategic insights.
The HR Data Analytics certification course in Vadodara focuses on helping participants enhance human resource decision-making through data. You’ll learn to work with tools like Excel, Python, and R to organize, analyze, and visualize HR data. The course includes practical case studies and hands-on projects to ensure you can apply your knowledge to real-world HR challenges, such as optimizing recruitment, boosting employee engagement, and improving retention.
This course offers multiple benefits. By mastering people analytics, you’ll be able to interpret data trends, build predictive models, and generate actionable insights that align HR strategy with business goals. These skills are in high demand across industries, giving you a competitive edge in the job market and paving the way for advanced HR roles.
Through this expertly crafted program—among the most sought-after HR Data Analytics coaching classes in Vadodara—you’ll not only strengthen your analytical capabilities but also gain critical thinking and decision-making skills essential for solving complex workforce problems.
Whether you’re aiming to upgrade your career or help your organization embrace data-driven HR practices, this course provides everything you need to succeed in today’s analytics-driven landscape
What will I learn?
- Participants will gain a solid understanding of the fundamentals of HR analytics, including its role in strategic HR management, key concepts, and terminology specific to HR analytics.
- Students will learn how to collect, clean, and preprocess HR data from various sources, ensuring that it is accurate, reliable, and suitable for analysis.
- Participants will gain proficiency in predictive analytics techniques for HR, enabling them to forecast future HR trends, predict workforce demand, talent shortages, and identify at-risk employees using predictive modeling.
- Participants will develop skills in workforce planning, succession planning, and talent management analytics, enabling them to align HR strategies with organizational goals and ensure a sustainable talent pipeline.
Requirements
- Basic understanding of HR concepts and terminology.
- No prior experience with programming or statistical software is necessary.
HR Data Analytics Course Content
- SQL Fundamentals
- Various types of databases
- Introduction to Structured Query Language
- Distinction between client server and file server databases
- Understanding SQL Server Management Studio
- SQL Table basics
- Data types and functions
- Transaction-SQL
- Authentication for Windows
- Data control language
- The identification of the keywords in T-SQL, such as Drop Table
- Database Normalization
- Entity Relationship Model
- SQL Operators
- Working with SQL
- Join
- Tables
- Variables
- Advanced concepts of SQL tables
- SQL functions
- Operators & queries
- Table creation
- Data retrieval from tables
- Combining rows from tables using inner, outer, cross, and self joins
- Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
- Temporary table creation
- Set operator rules
- Table variables•
- Deep Dive into SQL Functions
- Working with Subqueries
- SQL Views, Functions, and Stored Procedures
- Deep Dive into User-defined Functions
- SQL Optimization and Performance
- SQL Server Management Studio
- Using pivot in MS Excel and MS SQL Server
- Differentiating between Char, Varchar, and NVarchar
- XL path, indexes and their creation
- Records grouping, advantages, searching, sorting, modifying data
- Clustered indexes creation
- Use of indexes to cover queries
- Common table expressions
- Index guidelines
- Managing Data with Transact-SQL
- Querying Data with Advanced Transact-SQL Components
- Programming Databases Using Transact-SQL
- Creating database programmability objects by using T-SQL
- Implementing error handling and transactions
- Implementing transaction control in conjunction with error handling in stored procedures
- Implementing data types and NULL
- Designing and Implementing Database Objects
- Implementing Programmability Objects
- Managing Database Concurrency
- Optimizing Database Objects
- Advanced SQL
- Correlated Subquery, Grouping Sets, Rollup, Cube
- Implementing Correlated Subqueries
- Using EXISTS with a Correlated subquery
- Using Union Query
- Using Grouping Set Query
- Using Rollup
- Using CUBE to generate four grouping sets
- Perform a partial CUBE
Basic Math
- Linear Algebra
- Probability
- Calculus
- Develop a comprehensive understanding of coordinate geometry and linear algebra.
- Build a strong foundation in calculus, including limits, derivatives, and integrals.
- What is Keras?
- How to Install Keras?
- Why to Use Keras?
- Different Models of Keras
- Preprocessing Methods
- What are the Layers in Keras?
- Descriptive Statistics
- Sampling Techniques
- Measure of Central Tendency
- Measure of Dispersion
- Skewness and Kurtosis
- Random Variables
- Bassells Correction Method
- Percentiles and Quartiles
- Five Number Summary
- Gaussian Distribution
- Lognormal Distribution
- Binomial Distribution
- Bernoulli Distribution
- Inferential Statistics
- Standard Normal Distribution
- ZTest
- TTest
- ChiSquare Test
- ANOVA / FTest
- Introduction to Hypothesis Testing
- Null Hypothesis
- Alternet Hypothesis
- Probability Theory
- What is Probability?
- Events and Types of Events
- Sets in Probability
- Probability Basics using Python
- Conditional Probability
- Expectation and Variance
- Python Programming
- Python for Analytics
- R Programming
- R for Anlalytics
- Introduction
- Roles
- Snowflake Pricing
- Resource Monitor – Track Compute Consumption
- Micro-Partitioning in Snowflake
- Clustering in Snowflake
- Query History & Caching
- Load Data from AWS – CSV / JASON / PARQUET & Stages
- Snow pipe – Continuous Data Ingestion Service
- Different Type of Tables
- Time Travel – Work with History of Objects & Fail Safe
- Task in Snowflake – Scheduling Service
- Snowflake Stream – Change Data Capture (CDC)
- Zero-Copy Cloning
- Snowflake SQL – DDL
- Snowflake SQL – DML & DQL
- Snowflake SQL – Sub Queries & Case Statement
- Snowflake SQL – SET Operators
- Snowflake SQL – Working with ROW NUMBER
- Snowflake SQL – Functions & Transactions
- Procedures
- User defined function
- Types of Views
- Intro to Qlik View
- Installation of Qlik view
- Data Modelling in Qlik View
- Circular reference
- Link Tables to your model
- Joins in Qlik view
- ETL in Qlik View
- Handling Null Values
- Visualizations in Qlik View
- Pivot Table in Qlik View
- KPI Development in Qlik View
- Introduction to Alteryx
- Download and Install Alteryx
- User Interface of Alteryx
- Get Data from Excel
- Get Data from CSV
- Append All CSV files
- Browse Tool
- Output Tool – Update Existing Data
- Directory Tool
- Directory Tool – Specific Files
- Text Input Tool
- Date and Time Tool
- Auto Field Tool
- Data Cleansing Tool
- Filter Tool (Text Example)
- Filter Tool (Number Example)
- Filter Tool ( Date Example)
- Formula Tool ( Basic Example )
- Formula Tool – (Multiple Examples)
- Generate Rows Tool
- Imputation Tool
- Multi-Field Binning Tool
- Multi-Field Formula
- Multi Row Formula
- Random % Sample Tool
- Sample Tool
- Record Id Tool
- Select Tool
- Sort
- Create Sample Tool
- Tile Tool
- Unique Tool
- Append Fields Tool
- Find And Replace Tool
- Fuzzy Match Tool
- Join Tool
- Join Multiple Tool
- Union Tool
- Regex Tool
- Text To Columns
- Cross Tab Tool
- Transpose Tool
- Running Total Tool
- Summarize Tool
- Table Tool
- Interactive Chart Tool
- Join Table And Chart
- Add Annotation
- Report Text Tool
- Report Header Tool
- Report Footer Tool
- Report Layout Tool
- Comment Tool
- Explorer Tool
- Container Tool
- Cloud AWS
- Cloud Azure
- Introduction to GIT
- Version Control System
- Introduction and Installation of Git
- History of Git
- Git Features
- Introduction to GitHub
- Git Repository
- Git Features
- Bare Repositories in Git
- Git Ignore
- Readme.md File
- GitHub Readme File
- GitHub Labels
- Difference between CVS and GitHub
- Git – SubGit
- Git Environment Setup
- Using Git on CLI
- How to Setup a Repository
- Working with Git Repositories
- Using GitHub with SSH
- Working on Git with GUI
- Difference Between Git and GitHub
- Working on Git Bash
- States of a File in Git Working Directory
- Use of Submodules in GitHub
- How to Write Good Commit Messages on GitHub?
- Deleting a Local GitHub Repository
- Git Workflow Etiquettes
- Git Packfiles
- Git Garbage Collection
- Git Flow vs GitHub Flow
- Git – Difference Between HEAD, Working Tree and Index
- Git Ignore
- Introduction of Scum and Agile
- How to differentiate between Waterfall and Agile
- Agile Framework
- Agile Manifesto
- Agile Principles
- Top Agile Methodologies
- Scrum terminology and roles
- Managing tasks and events within a Sprint
- Scrum Framework
- Introduction to Scrum Framework
- Three pillars of Scrum Framework
- Values of Scrum
- When to use Scrum
- Cross-Functional, Self-Organizing Teams
- Scrum Team philosophy
- Developers
- Product Owner
- Scrum Master
- Scrum Events and Planning
- Scrum Events
- Understanding Sprint
- Sprint Planning
- Daily Scrum Meeting
- Sprint Review Meeting
- Sprint Retrospective
- Scrum Planning with backlog
- Product Backlog
- Refining Backlog
- Backlog items Estimation
- Planning Poker
- T-Shirt Sizing
- Defining Product Goals
- User Stories and INVEST
- Sprint Backlog
- Definition of Done
- Product Increment
- Definition of Done
- Objective and Scope
- Objective: To improve the effectiveness of marketing campaigns by analyzing customer behavior data.
- Scope: Focus on a retail company’s email marketing campaigns over the past year.
- Background Information
- Business Context: A retail company wants to increase sales through targeted email marketing campaigns.
- Data Sources: Customer transaction data, email campaign engagement metrics (open rates, click-through rates), demographic information.
- Data Collection and Preparation
- Data Collection: Gather transactional data from the company’s CRM system and email campaign data from marketing tools.
- Data Cleaning: Handle missing values, remove duplicates, and ensure data consistency.
- Data Transformation: Merge datasets, perform segmentation based on customer demographics and purchase history.
- Data Analysis and Exploration
- Descriptive Analytics: Analyze email open rates, click-through rates, and conversion rates over time.
- Exploratory Data Analysis (EDA): Identify trends in customer behavior and preferences.
- Customer Segmentation: Use clustering algorithms to group customers based on their purchasing patterns and demographics.
- Modeling and Analysis
- Predictive Analytics: Build a predictive model to forecast customer response to different types of email campaigns.
- Campaign Optimization: Use A/B testing to compare the effectiveness of different campaign strategies.
- ROI Calculation: Estimate the return on investment (ROI) for each campaign based on sales uplift.
- Interpretation of Results
- Business Insights: Discover that customers in certain demographics respond better to personalized product recommendations.
- Impact Analysis: Show that targeted campaigns led to a 15% increase in sales compared to generic promotions.
- Case Study Structure
- Introduction: Overview of the retail company’s marketing challenges and objectives.
- Methodology: Detailed explanation of data collection, analysis techniques, and modeling approach.
- Results: Presentation of findings including visualizations (charts, graphs) and statistical analysis.
- Discussion: Interpretation of results, implications for marketing strategy, and actionable recommendations.
- Conclusion: Summary of key findings and the impact of data-driven insights on business outcomes.
- Review and Validation
- Peer Review: Obtain feedback from marketing experts and data analysts within the company.
- Validation: Validate findings by comparing with historical performance and conducting sensitivity analyses.
- Presentation and Documentation
- Presentation: Prepare a compelling slide deck with key findings, visualizations, and actionable insights.
- Documentation: Document methodologies, assumptions, and data sources for transparency and reproducibility.
- Dissemination
- Publishing: Share the case study internally with stakeholders and externally through industry conferences or publications.
- Feedback: Gather feedback from stakeholders to refine strategies and improve future campaigns.
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Why Choose HR Data Analytics Certification Course from Bright Computer Education?
HR Data Analytics courses are designed to deliver a strategic, insightful, and hands-on learning experience for professionals looking to leverage data in human resource management. Whether you’re looking to Learn HR Data Analytics in Vadodara, just starting out with HR Data Analytics for beginners in Vadodara, or aiming to build advanced capabilities through Advanced HR Data Analytics training in Vadodara, these programs are crafted for learners at every level. The curriculum covers workforce analytics, employee performance metrics, attrition prediction, and the use of tools like Excel, Power BI, and Python to analyze HR trends. With real-world projects, expert-led sessions, and case-based learning, students gain the skills to make data-driven decisions that enhance HR strategies and organizational outcomes.
Designed Curriculum
Our curriculum covers everything from basic to advanced topics. Topics include variables, data types, control structures, functions, OOP, STL, and more.
Hands-on Learning
Dive into practical exercises and coding projects that reinforce learning and help you build real-world applications.
Experienced Instructors
Learn from industry experts with years of experience in C programming and software development.
Flexible Learning
Choose from flexible scheduling options, including self-paced learning or live virtual classes to fit your busy lifestyle.
Career Development
Gain valuable skills sought after by employers in various industries, from software development to embedded systems and beyond.
Interactive Learning
Engage with fellow learners and instructors through live Q&A sessions, discussion forums, and collaborative coding exercises.
Diverse Career Opportunities in HR Data Analtyics: Exploring Paths in India's Technology Sector
HR Data Analytics, also known as People Analytics, focuses on leveraging data to optimize human resource functions such as recruitment, performance management, employee retention, and workforce planning. A course in HR Data Analytics equips learners with skills in Excel, SQL, Python/R, and HR-specific analytics tools, enabling data-driven decision-making in HR departments.
In India, professionals trained in HR analytics can earn between ₹4–8 lakhs per annum at entry level, with higher packages offered in large corporations and HR tech firms. Internationally, especially in countries like the U.S., UK, and Australia, HR data analysts earn between $70,000 to $110,000 annually, depending on experience and domain expertise.
With 2–4 years of experience, individuals can grow into roles such as HR Analyst, People Analytics Consultant, Talent Strategy Analyst, or Workforce Planning Specialist. The integration of analytics into HR has become a key driver of organizational success, making this skill set increasingly in demand.
In summary, an HR Data Analytics course opens up dynamic career opportunities both in India and abroad—ideal for HR professionals looking to enhance their analytical capabilities and drive strategic workforce decisions.
Frequently Asked Questions
Typically, the HR Data Analytics course can be completed in around 2 to 4 months. The exact duration depends on how much time you dedicate each week to learning the concepts, completing assignments, and working on practical HR-related projects
You don’t need a deep technical background to get started. Having basic knowledge of HR processes and a general comfort with working on spreadsheets or simple analytics tools will be beneficial. The course is structured to guide you from the basics to more advanced topics at a steady pace.
You will learn how to collect, manage, and analyze HR data to improve decision-making in areas like hiring, employee engagement, performance management, and retention. Skills in using tools like Excel, HR dashboards, basic statistics, and data visualization techniques will also be covered.
Yes. Upon successfully finishing the coursework and assessments, you will earn a certification in HR Data Analytics. This certificate validates your ability to apply data-driven strategies in human resource management, enhancing your professional profile.
You will have access to recorded lectures, interactive assignments, real-world finance projects, and case studies. Many programs also offer live mentorship, one-on-one doubt-clearing sessions, and online discussion groups to help you stay on track and deepen your understanding.
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