HR Data Analytics Course

The HR Data Analytics course is a specialized program designed to equip human resources professionals and data enthusiasts with the skills to analyze and interpret HR data effectively. The course covers key areas such as employee performance analysis, workforce planning, recruitment metrics, and retention strategies, using advanced analytical tools and techniques.

|| HR Data Analytics Course

The HR Data Analytics courses on this list provide all the necessary techniques and resources to provide data-driven insights into talent management, workforce planning, and operational enhancement. The goal of these programs is to improve strategic human resource decision-making. Students will learn to utilize software like Excel, R, and Python to manage and analyze HR data, create predictive models, and generate actionable insights. Through real-world case studies and practical projects, participants will gain hands-on experience in applying analytics to solve HR challenges and enhance organizational performance.


The benefits of this course are multifaceted. By mastering HR Data analytics, participants can transform raw HR data into strategic insights that drive evidence-based decision-making, improving hiring processes, employee engagement, and retention rates. This expertise is highly valued in the HR field, providing a competitive edge and opening up advanced career opportunities. Additionally, the course fosters critical thinking and problem-solving skills, enabling students to address complex HR issues and contribute to their organizations' strategic goals. Overall, the HR Analytics course empowers individuals to leverage data analytics to optimize human resource functions, leading to more efficient and effective HR practices. The main objectives of this course are to help with organizational strategy and tactics by finding efficient data sources, creating relevant metrics, creating long-term measurements, and implementing results.


HR data collection, analysis, and reporting are all part of HR data analytics, also known as people analytics or workforce analytics. It helps your company to make data-driven decisions, assess the effect of various HR KPIs on overall business success, and gain a deeper understanding of your staff.


Please contact the nearest BIT training institute or send an email on inquiry@bitbaroda.com HR Data Analytics training course. We offer a free demo by calling us at +91-9328994901. We offer top-notch HR Data Analytics Training in Vadodara-Sayajigunj, Vadodara - Waghodia Road, Vadodara - Manjalpur, Ahmedabad, Anand, and Nadiad.

|| Why Choose HR Data Analytics Certification from BIT ? 

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|| 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.

|| 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.

|| Requirements

  • Basic understanding of HR concepts and terminology.
  • No prior experience with programming or statistical software is necessary.

    An effective HR Data Analytics course should cover the essentials: an introduction to HR Data analytics, its role in business, and key concepts. It should include data fundamentals, statistical analysis, and predictive analytics. Participants should learn to define and select HR metrics and KPIs, develop HR dashboards, and use advanced techniques like machine learning and text analytics. Practical skills with tools like HRIS, Tableau, and Python should be developed through hands-on workshops.


    The course should also address implementing HR Data analytics in organizations, building analytics teams, driving adoption, and handling ethics and legal issues. A capstone project should allow participants to apply their learning to a real-world HR problem. Additional resources such as readings, reports, and tutorials should support ongoing learning. This comprehensive framework ensures participants gain the skills needed to implement HR Data analytics effectively.


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    • Microsoft Excel fundamentals.
    • Entering and editing texts and formulae.
    • Working with basic Excel functions.
    • Modifying an Excel worksheet.
    • Formatting data in an excel worksheet.
    • Inserting images and shapes into an Excel worksheet.
    • Creating Basic charts in Excel.
    • Printing an Excel worksheet.
    • Working with an Excel template.
    • Working with an excel list.
    • Excel list function.
    • Excel data validation.
    • Importing and exporting data.
    • Excel pivot tables.
    • Working with excels
    • Pivot tools.
    • Working with large sets of Excel data.
    • Conditional function.


    • Lookup functions.
    • Text based functions
    • Auditing and Excel worksheet.
    • Protecting Excel worksheets and workbooks.
    • Mastering Excel "What if?" Tools?
    • Automating Repetitive Tasks in Excel with Macros.
    • Macro Recorder Tool.
    • Excel VBA Concepts.
    • Ranges and Worksheet in VBA 
    • IF condition 
    • Loops in VBA 
    • Debugging in VBA 
    • Messaging in VBA
    • Preparing and Cleaning Up Data with VBA.
    • VBA to Automate Excel Formulas.
    • Preparing Weekly Report.
    • Working with Excel VBA User Forms.
    • Importing Data from Text Files.

    • 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.

    • 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 Basic Building
    • Python Keywords and identifiers
    • Comments, indentation, statements
    • Variables and data types in Python
    • Standard Input and Output
    • Operators
    • Control flow: if else elif
    • Control flow: while loop
    • Control flow: for loop
    • Control flow: break & continue


    • Python Data Structures
    • Strings
    • Lists, Lists comprehension
    • Tuples
    • Sets
    • Dictionary, Dictionary Comprehension


    • Python Functions
    • Python Builtin Functions.
    • Python Userdefined Functions.
    • Python Recursion Functions.
    • Python Lambda Functions.
    • Python Exception Handling, 
    • Logging And Debugging


    • Exception Handling 
    • Custom Exception Handling
    • Logging With Python
    • Debugging With Python


    • Python OOPS
    • Python Objects And Classes
    • Python Constructors
    • Python Inheritance
    • Abstraction In Python
    • Polymorphism in Python
    • Encapsulation in Python


    • File Handling
    • Create 
    • Read
    • Write
    • Append

    • Introduction to NumPy
    • NumPy Array
    • Creating NumPy Array
    • Array Attributes, 
    • Array Methods
    • Array Indexing, 
    • Slicing Arrays
    • Array Operation
    • Iteration through Arrays


    • Introduction to Pandas
    • Pandas Series
    • Creating Pandas Series
    • Accessing Series Elements
    • Filtering a Series
    • Arithmetic Operations
    • Series Ranking and Sorting
    • Checking Null Values
    • Concatenate a Series


    • Data Frame Manipulation
    • Pandas Dataframe 
    • Introduction Dataframe Creation
    • Reading Data from Various Files
    • Understanding Data
    • Accessing Data Frame Elements using Indexing
    • Dataframe Sorting
    • Ranking in Dataframe
    • Dataframe Concatenation
    • Dataframe Joins, 
    • Dataframe Merge
    • Reshaping Dataframe
    • Pivot Tables, 
    • Cross Tables
    • Dataframe Operations


    • Checking Duplicates
    • Dropping Rows and Columns
    • Replacing Values
    • Grouping Dataframe
    • Missing Value Analysis & Treatment
    • Visualization using Matplotlib
    • Plot Styles & Settings
    • Line Plot, 
    • Multiline Plot
    • Matplotlib Subplots
    • Histogram, Boxplot
    • Pie Chart ,Scatter Plot
    • Visualization using Seaborn
    • Strip Plot ,Distribution Plot
    • Joint Plot, 
    • Violin Plot, 
    • Swarm Plot
    • Pair Plot,
    • Count Plot
    • Heatmap
    • Visualization using Plotly
    • Boxplot
    • Bubble Chart
    • Violin Plot
    • 3D Visualization


    • EDA and Feature Engineering
    • Introduction of EDA
    • Dataframe Analysis using Groupby
    • Advanced Data Explorations

    Power BI and Tableau are both leading business intelligence (BI) tools used extensively in data analytics, each offering distinct features and capabilities tailored to different user needs.

     

    Power BI, developed by Microsoft, is known for its integration with the Microsoft ecosystem, particularly Excel and Azure services. It excels in data connectivity and integration, allowing users to easily connect to various data sources, clean and transform data using Power Query, and create interactive visualizations and reports. Power BI's strength lies in its user-friendly interface and seamless integration with other Microsoft products, making it a preferred choice for organizations already invested in Microsoft technologies.

     

    Tableau, on the other hand, is celebrated for its powerful data visualization capabilities and ease of use. Tableau enables users to create visually appealing and interactive dashboards with simple drag-and-drop functionality. It supports a wide range of data sources and provides robust analytics features, including advanced statistical analysis, predictive modeling, and geographic mapping. Tableau's intuitive interface and strong emphasis on visual storytelling make it popular among analysts and data professionals who prioritize data visualization and storytelling.

    • MS Power BI Desktop
    • Introduction to Power BI Desktop
    • Overview of Power BI
    • Key Features and Benefits
    • Comparison with other BI tools


    • Getting Started with Power BI Desktop
    • Installation and Setup
    • Tour of the Interface
    • Navigating Power BI Ribbon and Panes


    • Connecting to Data Sources
    • Importing Data from Excel
    • Connecting to Databases (SQL Server, MySQL, etc.)
    • Using Web and Text Data Sources


    • Transforming and Cleaning Data
    • Understanding Power Query Editor
    • Data Cleaning and Shaping
    • Merging and Appending Queries


    • Data Modeling in Power BI
    • Introduction to Data Modeling
    • Creating Relationships between Tables
    • Defining Calculated Columns and Measures


    • Creating Visualizations
    • Types of Visualizations (Bar charts, Line charts, Pie charts, etc.)
    • Formatting and Customizing Visuals
    • Using Interactive Filters and Slicers


    • Advanced Visualizations and Techniques
    • Hierarchies and Drill-downs
    • Using Custom Visuals
    • Applying Themes and Templates


    • Working with Maps and Geographic Data
    • Mapping Data Points
    • Using Shapefiles and Custom Maps
    • Geocoding and Location Analytics


    • Creating Dashboards
    • Designing Effective Dashboards
    • Using Tiles and Q&A Features
    • Sharing Dashboards


    • Data Analysis Expressions (DAX)
    • Introduction to DAX
    • Writing DAX Formulas
    • Calculating Totals, Ratios, and Percentages


    • Advanced Data Modeling with DAX
    • Understanding CALCULATE and FILTER Functions
    • Time Intelligence Functions (DATESYTD, SAMEPERIODLASTYEAR, etc.)
    • Implementing Row-level Security


    • Power BI Service Integration
    • Publishing Reports to Power BI Service
    • Setting up Scheduled Data Refresh
    • Sharing and Collaborating on Reports


    • Data Insights and AI Features
    • Introduction to AI Insights in Power BI
    • Using Quick Insights and AI Visuals
    • Integrating Azure AI Services


    • MS Power BI Desktop Exercises
    • Importing and Transforming Data
    • Task: Import sales data from Excel, clean and transform data using Power Query.
    • Outcome: Create a clean dataset ready for analysis.


    • Creating Basic Visualizations
    • Task: Build a bar chart and a line chart to visualize sales trends.
    • Outcome: Understand basic visualization types and formatting options.


    • Creating Advanced Visualizations
    • Task: Create a slicer-based dashboard page with interactive visuals.
    • Outcome: Learn how to use slicers, filters, and drill-down capabilities.


    • Implementing DAX Calculations
    • Task: Write DAX formulas to calculate year-to-date sales and growth percentages.
    • Outcome: Gain proficiency in using DAX for calculations and analysis.


    • Publishing and Sharing Reports
    • Task: Publish a completed sales dashboard to Power BI Service, set up scheduled refresh.
    • Outcome: Understand the workflow of publishing and sharing reports.

    • MS Power BI Server 
    • Introduction to Power BI Server
    • Overview of Power BI Ecosystem
    • Key Features and Capabilities
    • Understanding Power BI Server vs. Power BI Online


    • Installation and Configuration
    • System Requirements and Installation Steps
    • Configuring Power BI Server
    • Integration with Active Directory


    • Power BI Server Architecture
    • Components Overview (Gateway, Data Sources, Reports)
    • Understanding Data Gateways
    • Security and Permissions


    • Data Sources and Connectivity
    • Connecting to Various Data Sources
    • Live vs. DirectQuery vs. Import
    • Refreshing Data


    • Creating Reports and Dashboards
    • Using Power BI Desktop for Report Authoring
    • Building Interactive Visualizations
    • Designing Effective Dashboards


    • Publishing and Managing Reports
    • Publishing Reports from Power BI Desktop to Power BI Server
    • Organizing Content in Workspaces
    • Version Control and Sharing Reports


    • Data Security and Governance
    • Implementing Row-level Security
    • Applying Security Policies
    • Data Encryption and Compliance


    • Advanced Analytics and AI Integration
    • Introduction to AI Features in Power BI
    • Using Custom Visuals and R/Python Scripts
    • Integrating Azure AI Services


    • Performance Optimization
    • Optimizing Query Performance
    • Improving Report Rendering Speed
    • Monitoring and Troubleshooting


    • Customizing and Extending Power BI
    • Creating and Using Custom Themes
    • Developing Custom Visuals
    • Using Power BI APIs for Automation


    • Practical Exercises
    • Exercise 1: Setting up Power BI Server
    • Exercise 2: Creating and Publishing Reports
    • Exercise 3: Implementing Security Measures
    • Exercise 4: Performance Optimization Tasks
    • Exercise 5: Customizing Reports and Dashboards


    •  Case Studies and Real-world Applications
    • Industry-specific Use Cases
    • Success Stories and Best Practices


    • MS Power BI Server Exercise
    • Setting up Power BI Server
    • Install Power BI Server on a local machine or VM.
    • Configure basic settings and connect to a sample database.


    • Creating and Publishing Reports
    • Design a sales dashboard using Power BI Desktop.
    • Publish the dashboard to Power BI Server and configure data refresh.


    •  Implementing Security Measures
    • Set up row-level security based on user roles.
    • Configure encryption settings and access policies.


    • Performance Optimization Tasks
    • Identify slow-performing reports and optimize queries.
    • Monitor resource usage and apply performance tuning techniques.


    • Customizing Reports and Dashboards
    • Customize the appearance of reports using custom themes.
    • Create a custom visual using Power BI SDK and integrate it into a dashboard.

    • Tableau Desktop 
    • Introduction to Tableau Desktop:
    • Overview of Tableau Desktop and its features.
    • Understanding the Tableau interface and terminology.


    • Connecting to Data:
    • Importing data into Tableau from various sources (Excel, CSV, databases, etc.).
    • Understanding data source connection options and considerations.


    • Basic Visualization:
    • Creating basic visualizations such as bar charts, line charts, scatter plots, and maps.
    • Applying formatting and customization to visualizations.


    • Working with Data:
    • Data organization and structuring.
    • Filtering and sorting data.
    • Grouping and aggregating data.


    • Advanced Visualization Techniques:
    • Creating more complex visualizations such as dual-axis charts, treemaps, and heatmaps.
    • Implementing reference lines, bands, and distributions.


    • Calculations and Expressions:
    • Introduction to Tableau Calculated Fields.
    • Writing basic calculations (e.g., arithmetic calculations, string calculations, date calculations).Use clustering algorithms to segment customers based on their purchasing behavior.


    • Dashboard Creation:
    • Building dashboards to combine multiple visualizations into a single view.
    • Implementing interactivity with dashboard actions and filters.


    • Data Blending and Joins:
    • Working with multiple data sources and blending data.
    • Understanding different types of joins and their implications.


    • Advanced Data Analysis:
    • Implementing advanced calculations using Tableau Calculated Fields and Parameters.
    • Utilizing Level of Detail (LOD) expressions for complex analysis.


    • Geospatial Analysis:
    • Mapping geographic data in Tableau.
    • Creating custom geocoding and using spatial files for analysis.


    • Performance Optimization:
    • Optimizing workbook performance for large datasets.
    • Understanding Tableau data extracts and incremental refreshes.


    • Advanced Dashboard Techniques:
    • Designing interactive and responsive dashboards.
    • Incorporating storytelling and guided analytics into dashboards.



    • Tableau Desktop Exercises
    • Data Connection and Basic Visualizations:
    • Import a dataset (e.g., CSV, Excel) into Tableau Desktop.
    • Create a bar chart to visualize sales by product category.
    • Create a line chart to show trends in monthly sales.
    • Add filters to interactively explore the data.


    • Geographic Visualization:
    • Use a geographic dataset (e.g., countries, states) to create a map visualization.
    • Color code the map based on a measure such as sales or population.
    • Drill down from country-level to state-level data using hierarchical filters.


    • Advanced Visualizations:
    • Create a dual-axis chart to compare two measures on the same axis.
    • Build a treemap to visualize hierarchical data such as sales by product category and subcategory.
    • Design a dashboard to display multiple visualizations together.


    • Calculations and Expressions:
    • Create a calculated field to calculate profit margin (profit divided by sales).
    • Use a LOD (Level of Detail) expression to calculate the total sales regardless of filters applied.
    • Implement a parameter to dynamically change the view (e.g., switch between different metrics).


    • Advanced Analytics:
    • Implement forecasting to predict future sales trends.

    • Use clustering algorithms to segment customers based on their purchasing behavior.

      Apply trend lines and statistical models to analyze data patterns.

    • Apply trend lines and statistical models to analyze data patterns.


    • Dashboard Design and Interactivity:
    • Design a dynamic dashboard with interactivity (e.g., use of parameters, dashboard actions).
    • Incorporate user input controls like dropdowns and sliders to filter data dynamically.
    • Implement URL actions to link Tableau visualizations to external web pages or documents.


    • Sales Performance Analysis:
    • Analyze sales performance by region, product, and time period.
    • Identify top-performing products and regions.
    • Visualize sales trends and seasonality.


    • Customer Segmentation:
    • Segment customers based on demographics, purchasing behavior, or lifetime value.
    • Identify key characteristics of each segment and tailor marketing strategies accordingly.


    • Profitability Analysis:
    • Analyze profitability by product line, customer segment, or sales channel.
    • Identify low-margin products or unprofitable customer segments and recommend actions to improve profitability.

    • Tableau Server
    • Introduction to Tableau Server:
    • Overview of Tableau Server
    • Introduction to Tableau Server architecture and components.
    • Understanding the role of Tableau Server in the Tableau ecosystem.


    • Installation and Configuration:
    • Installation prerequisites and best practices.
    • Step-by-step installation and configuration of Tableau Server.
    • User Management:
    • User authentication options (local authentication, Active Directory, SAML).
    • Managing users, groups, and permissions.


    • Content Management:
    • Publishing workbooks and data sources to Tableau Server.
    • Managing projects and content permissions.
    • Versioning and revision history.
    • Tableau Server Administration:


    • Server Administration Tasks:
    • Monitoring server status and performance.
    • Configuring server settings and resource management.
    • Backup and restore procedures.


    • Data Source Management:
    • Connecting to data sources and configuring data connections.
    • Managing data source permissions and connections.


    • Security and Governance:
    • Implementing security best practices.
    • Enforcing data governance policies.
    • Auditing and logging user activities.


    • High Availability and Scalability:
    • Configuring high availability and load balancing.
    • Scaling Tableau Server for increased capacity.


    • Advanced Topics:
    • Customization and Integration:
    • Customizing Tableau Server interface and branding.
    • Integrating Tableau Server with other applications and services.


    • Automation and Scripting:
    • Automating server tasks using Tableau Server REST API.
    • Scripting common administrative tasks for efficiency.


    • Disaster Recovery and Failover:
    • Planning and implementing disaster recovery strategies.
    • Configuring failover and redundancy options.


    • Tableau Server Exercises
    • Setting Up Tableau Server:
    • Installation and Configuration:
    • Install Tableau Server on a virtual machine or server environment.
    • Configure server settings, including authentication method (local, Active Directory, SAML).


    • Adding Users and Groups:
    • Add users to Tableau Server and assign them to appropriate groups.
    • Configure permissions to control access to projects, workbooks, and data sources.
    • Publishing Content to Tableau Server


    • Publishing Workbooks:
    • Publish a workbook from Tableau Desktop to Tableau Server.
    • Set permissions for the published workbook to control who can view and interact with it.


    • Publishing Data Sources:
    • Publish a data source to Tableau Server.
    • Configure data source permissions and refresh schedules.


    • Managing Content on Tableau Server:
    • Managing Projects:
    • Create new projects on Tableau Server to organize content.
    • Move workbooks and data sources between projects.


    • Content Permissions:
    • Modify permissions for existing content on Tableau Server.
    • Assign permissions to specific users or groups for projects, workbooks, and data sources.


    • Collaboration and Interactivity:
    • Creating and Managing Comments:
    • Add comments to workbooks and views on Tableau Server.
    • Reply to comments and manage comment threads.
    • Subscriptions and Alerts:
    • Set up email subscriptions to receive scheduled updates of workbook views.
    • Configure alerts to be notified when certain data thresholds are met.
    • Monitoring and Administration:
    • Server Status and Performance Monitoring:
    • Monitor server status, including CPU usage, memory usage, and disk space.
    • Identify performance bottlenecks and optimize server resources.
    • Backup and Restore:
    • Perform a backup of Tableau Server data and configuration.
    • Practice restoring Tableau Server from a backup in a test environment.
    • Security and Governance:
    • Security Best Practices:
    • Review and implement security best practices for Tableau Server.
    • Ensure compliance with data governance policies and regulations.
    • Auditing and Logging:
    • Review audit logs to track user activity on Tableau Server.
    • Analyze logs to identify security incidents or compliance issues.
    • Scaling and High Availability:
    • Scaling Tableau Server:
    • Add additional nodes to scale Tableau Server for increased capacity.
    • Configure load balancing to distribute traffic across multiple nodes.
    • High Availability Configuration:
    • Configure Tableau Server for high availability to ensure uptime and reliability.
    • Test failover and disaster recovery procedures to ensure continuity of service.


    • 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


    • Set Analysis in Qlik View
    • Date functions
    • What If analysis
    • Calculated Dimensions
    • Conditional Objects
    • Securing your document and document tuning
    • Cross tables
    • Bookmarks
    • Chart-level and script-level functions
    • Security measures and access points in QlikView
    • Integrating visualizations with dashboards

    • 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

    AWS (Amazon Web Services) and Azure (Microsoft Azure) are two of the leading cloud computing platforms offering robust data analytics services, each with its own strengths and capabilities tailored to diverse business needs.

     

     

    AWS provides a comprehensive suite of data analytics services under its Amazon Web Services umbrella. Key services include Amazon Redshift for data warehousing, Amazon EMR (Elastic MapReduce) for big data processing using Apache Hadoop and Spark, and Amazon Athena for querying data stored in Amazon S3 using standard SQL. AWS also offers analytics services like Amazon QuickSight for business intelligence and visualization, AWS Glue for ETL (Extract, Transform, Load) tasks, and AWS Data Pipeline for orchestrating data workflows. AWS's ecosystem is extensive, with a broad range of integrations and support for various programming languages and frameworks, making it a preferred choice for organizations seeking flexibility and scalability in their data analytics solutions.

     

     

    Azure, Microsoft's cloud platform, provides a robust set of data analytics services integrated with its suite of tools and services. Azure Synapse Analytics (formerly SQL Data Warehouse) offers enterprise-level data warehousing capabilities, supporting both relational and big data analytics. Azure HDInsight provides managed Apache Hadoop, Spark, HBase, and Storm clusters for big data processing. Azure Data Lake Store and Azure Databricks further enhance data storage and analytics capabilities, while services like Azure Machine Learning enable advanced predictive analytics and machine learning model development. Azure also includes Power BI for business intelligence and visualization, tightly integrating with other Microsoft products like Excel and SharePoint. Azure's strength lies in its seamless integration with Microsoft's enterprise ecosystem, making it an attractive option for organizations already using Microsoft technologies.

    • S3 Basics
    • Storage Classes 
    • Data Management
    • security & Access Control 
    • Cost Optimization
    • Monitoring & Logging 
    • Use Cases 
    • Data Replications and Disaster recovery
    • Course Overview 
    • Introducing our Hands-On Case Study
    • Collection Section 
    • Introduction Kinesis Data Streams Overview 
    • Hot shard 
    • Kinesis Producers
    • Kinesis Consumers 
    • Kinesis Enhanced Fan Out 
    • Kinesis Scaling
    • Kinesis - Handling Duplicate Records part 1 
    • Kinesis - Handling Duplicate Records part 2 
    • Kinesis Security 
    • Kinesis Data Firehose
    • CloudWatch Subscription Filters with Kinesis 
    • Kinesis Data Streams vs SQS 
    • IoT Overview 
    • IoT Components Deep Dive
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    HR analytics case studies exemplify how organizations leverage data-driven insights to enhance human resources management and strategic decision-making. For instance, one compelling case study may focus on employee retention, aiming to predict turnover by analyzing factors such as demographics, job roles, performance metrics, and engagement levels. Data collected from HR systems undergoes rigorous cleaning and preprocessing to ensure accuracy and consistency. Exploratory data analysis (EDA) uncovers patterns and correlations, revealing insights into why employees may leave and which factors are most influential.


    Feature engineering then transforms raw data into meaningful variables for predictive modeling. Models like logistic regression or random forests are trained and evaluated using metrics such as accuracy, precision, and recall to predict attrition risk accurately. The results provide actionable insights, enabling HR departments to proactively implement retention strategies like targeted training, improved career development, or enhanced work-life balance initiatives.


    Another case study might focus on workforce diversity, using data to assess representation across different demographics, departments, and job roles. EDA here could highlight disparities in hiring practices or promotional opportunities, leading to strategies that promote diversity and inclusion. These include targeted recruitment efforts, diversity training, and mentorship programs tailored to foster an inclusive workplace culture.

    In summary, HR analytics case studies illustrate how data-driven approaches not only diagnose current challenges but also inform proactive solutions that optimize workforce management, foster employee satisfaction, and drive organizational success.

    Some common topics include:


    • HR Analytics Case studies
    • Employee Attrition and Retention:
    • Predicting employee turnover and identifying factors that contribute to attrition.
    • Developing retention strategies based on data insights.


    • Employee Performance Analysis:
    • Evaluating factors influencing employee performance.
    • Predicting high-performing employees and identifying key drivers of performance.


    • Workforce Planning and Optimization:
    • Forecasting future workforce needs based on historical data and business projections.
    • Optimizing staffing levels and resource allocation.


    • Diversity and Inclusion:
    • Analyzing diversity metrics across different demographics (e.g., gender, ethnicity) and job roles.
    • Identifying barriers to inclusion and developing strategies to promote diversity.


    • Employee Engagement and Satisfaction:
    • Assessing employee engagement levels through surveys and feedback.
    • Understanding factors affecting employee satisfaction and morale.


    • Compensation and Benefits Analysis:
    • Analyzing the effectiveness of compensation packages in attracting and retaining talent.
    • Identifying disparities in pay equity and benefits utilization.


    • Recruitment and Talent Acquisition:
    • Optimizing recruitment strategies based on data-driven insights.
    • Evaluating the effectiveness of recruitment channels and processes.


    • Learning and Development:
    • Assessing the impact of training programs on employee performance and career development.
    • Identifying skill gaps and training needs within the organization.


    • Succession Planning:
    • Identifying potential successors for key positions within the organization.
    • Developing succession plans to ensure continuity and leadership development.


    • HR Metrics and KPIs:
    • Establishing and tracking key HR metrics such as turnover rate, time-to-hire, and employee productivity.
    • Using metrics to evaluate HR initiatives and interventions.


    • Predictive HR Analytics:
    • Using predictive modeling to forecast future HR trends and outcomes.
    • Anticipating HR challenges and opportunities based on data analysis.


    • Ethical and Legal Considerations:
    • Addressing ethical issues related to data privacy, confidentiality, and fairness in HR analytics.
    • Ensuring compliance with legal regulations and standards.

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|| Skill to master

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|| Tool to Master 

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|| Scope of HR Data Analytics Course in India 

The scope of an HR Data Analytics course in India is quite promising, given the growing importance of data-driven decision-making in human resources. Here are several factors contributing to its relevance and opportunities:


  • Increased Adoption of Technology: Indian companies are increasingly adopting advanced technologies and tools for data analysis. HR Data Analytics helps organizations leverage data to improve hiring, employee engagement, performance management, and retention strategies.
  • Talent Management: With a large and diverse workforce, companies are looking for ways to better manage talent. HR Data Analytics provides insights into workforce trends, skill gaps, and employee behavior, enabling more effective talent management strategies.
  • Decision Making: Businesses are increasingly relying on data to make informed decisions. HR Data Analytics helps HR professionals understand patterns and trends, thereby enhancing decision-making processes related to recruitment, employee performance, and workforce planning.
  • Competitive Advantage: Companies that use HR Data Analytics can gain a competitive edge by optimizing their HR functions. This can lead to improved employee satisfaction, higher productivity, and better organizational performance.
  • Growth in HR Technology: The market for HR technology is growing rapidly in India, with many startups and established firms offering tools for analytics, AI, and machine learning in HR. This growth is creating more opportunities for professionals skilled in HR Data Analytics.
  • Educational and Professional Demand: There is a growing demand for skilled HR professionals who can analyze and interpret data. Many universities and institutes in India are now offering specialized courses and certifications in HR Data Analytics, which can enhance career prospects for HR professionals.
  • Industries Embracing Analytics: Various industries such as IT, manufacturing, BFSI, and healthcare are increasingly adopting HR Data Analytics to improve their HR functions. This broadens the scope for professionals skilled in HR Data Analytics across different sectors.
  • Improved Employee Experience: HR Data Analytics can help in understanding employee needs and improving their overall experience. This is becoming increasingly important for companies looking to enhance employee satisfaction and reduce turnover rates.


Overall, the scope of HR Data Analytics in India is expanding, offering numerous career opportunities for HR professionals who are adept at using data and analytics to drive business outcomes. Whether through roles in HR consulting, data analysis, or strategic HR planning, there is a strong demand for expertise in this area.

placement report placement report

|| HR Data Analytics Course Career Option & Job Opportunities in India

Completing an HR Data Analytics course can open up a variety of career options and job opportunities in India. Here’s a look at some of the key roles and potential career paths you might pursue:


  • HR Analyst: HR Data Analysts are responsible for collecting and analyzing HR data to provide insights into workforce trends, employee performance, and other key metrics. They often use statistical tools and software to interpret data and support decision-making processes.
  • Talent Acquisition Specialist: In this role, you would use HR Data  Analytics to optimize recruitment strategies, identify the best sources for talent, and improve the hiring process. You might also analyze data to enhance employer branding and candidate experience.
  • HR Business Partner: HR Business Partners work closely with management to align HR strategies with business goals. HR Data Analytics skills are essential for identifying workforce trends, assessing the impact of HR initiatives, and advising on strategic workforce planning.
  • People Analytics Specialist: This role focuses on using data analytics to understand and improve employee engagement, satisfaction, and retention. People Analytics Specialists often work with large datasets to uncover insights that can drive HR policies and practices.
  • HR Manager: With expertise in HR Data Analytics, you can take on senior HR management roles, where you would oversee HR functions and leverage data to enhance organizational performance, develop HR strategies, and support business objectives.
  • Learning and Development (L&D) Analyst: L&D Analysts use HR Data Analytics to assess training needs, evaluate the effectiveness of training programs, and identify skills gaps. They help design and implement learning strategies that align with business goals.
  • Compensation and Benefits Analyst: In this role, you would analyze data related to employee compensation and benefits to ensure competitiveness and fairness. HR Data Analytics skills help in designing effective compensation structures and benefits packages.
  • HR Consultant: HR Consultants with analytics expertise can offer valuable insights and recommendations to organizations looking to improve their HR practices. They may work independently or with consulting firms to provide strategic HR solutions based on data analysis.
  • Data Scientist in HR: For those with a strong background in data science, specializing in HR Data Analytics can lead to roles that involve developing predictive models, machine learning algorithms, and other advanced analytical techniques to solve HR-related challenges.
  • HR Technology Specialist: As companies increasingly adopt HR technology solutions, there is a growing need for professionals who can implement and manage these systems. HR Data Analytics expertise is valuable for configuring tools, analyzing data, and ensuring these systems meet organizational needs.


  • Job Opportunities:
  • IT Companies: Many IT firms are heavily investing in HR Data Analytics to streamline their recruitment and talent management processes.
  • Consulting Firms: HR consulting firms often seek professionals with analytics skills to provide data-driven insights to their clients.
  • Manufacturing and BFSI Sectors: These industries are also increasingly adopting HR Analytics to improve workforce management and operational efficiency.
  • Startups and New-age Companies: Startups, especially in the tech and service sectors, are keen on leveraging HR Data Analytics to scale efficiently and maintain a competitive edge.


With the right skills and certifications, HR Data Analytics professionals can find ample opportunities across various sectors, driving better HR practices and contributing to organizational success.


|| HR Data Analytics Course holds in a Prominent Position in India Job Market 

HR Data Analytics in India offers abundant placement opportunities across sectors such as IT (Infosys, TCS), finance (ICICI Bank, HDFC Bank), consulting (Deloitte, PwC), e-commerce (Flipkart, Amazon), manufacturing (Tata Motors, Larsen & Toubro), healthcare (Apollo Hospitals, Fortis Healthcare), telecommunications (Airtel, Reliance Jio), and startups (Ola, BYJU's). Roles include HR Analyst, People Analytics Specialist, and Compensation and Benefits Analyst. Key placement channels are campus placements, internships, job portals like Naukri.com and LinkedIn, and professional networking. Essential skills include proficiency in Excel, Python, HR metrics, and strong analytical and communication abilities, making HR Data Analytics a promising career path in India.

|| Empowering Your Career Transition From Learning To Leading

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Rajvi Suthar

Rajvi Suthar, excelling as a Data Analyst at Tata Consultancy Services (TCS), leverages unique tools such as Python for scripting, R for statistical analysis, and Alteryx for data blending. Her adept use of these cutting-edge tools contributes to efficient and advanced data analysis solutions.

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Sarthak Gupta

Sarthak Gupta, demonstrating mastery as a Business Data Analyst at Accenture, leverages unique tools such as Power BI for visual analytics, Python for data scripting, and Alteryx for data blending. His adept use of these cutting-edge tools contributes to efficient and advanced business data analysis solutions.

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Megha Bhatt

Megha Bhatt, demonstrating prowess as a ML Engineer at Cognizant, leverages unique tools such as Alteryx for advanced data blending, Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

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Rishabhjit Saini

Rishabhjit Saini, demonstrating mastery as a Senior Data Processing professional at NielsenIQ, leverages unique tools such as Talend for data integration, Apache Spark for big data processing, and Trifacta for advanced data wrangling. His adept use of these cutting-edge tools contributes to efficient and innovative data handling.

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Darshna Dave

Darshna Dave, excelling as a Data Engineer at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

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Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-IT institute, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

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Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post-IT institute, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

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|| Job Roles and Salary

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|| Average Salary for HR Data Analytics Course in India 

The average salary for professionals with HR Data Analytics skills in India can vary significantly based on the level of experience and specific job role. Here's a general overview of salary expectations at different experience levels:


  • Entry-Level (0-2 years of experience): Average Salary: ₹3,00,000 - ₹5,00,000 per annum
  • Mid-Level (3-7 years of experience): Average Salary: ₹5,00,000 - ₹10,00,000 per annum
  • Senior-Level (8+ years of experience): Average Salary: ₹10,00,000 - ₹20,00,000 per annum and above


These figures can vary based on several factors including the size and type of the organization, the specific location within India, educational background, and the individual's specific skill set and performance. Additionally, professionals with advanced certifications or degrees, such as an MBA in HR or a specialized certification in HR Data Analytics, might command higher salaries.


Roles in HR Data Analytics typically include positions such as HR Analyst, People Analytics Specialist, Workforce Analyst, and HR Business Partner with an analytics focus. Higher-level roles can include HR Analytics Manager or Director of HR Analytics.

Keep in mind that these figures are averages and can fluctuate with changes in the job market and economic conditions.

 

|| Top Hiring Companies

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|| Some Prominent Companies in India that use HR Data Analytics Course 

In India, several leading companies across various industries have integrated HR Analytics into their operations to enhance their HR functions and decision-making processes. Here are some notable companies that have adopted HR Data Analytics:


  • Infosys: Infosys has been leveraging HR Data Analytics to optimize talent management, enhance employee engagement, and improve recruitment strategies. They use data analytics to drive decision-making in HR policies and practices.
  • Tata Consultancy Services (TCS): TCS utilizes HR Data Analytics to streamline their recruitment processes, assess employee performance, and enhance workforce planning. They focus on data-driven approaches to improve overall HR effectiveness.
  • Wipro: Wipro employs HR Analytics to analyze workforce trends, optimize talent acquisition, and enhance employee satisfaction. They use data insights to support their HR strategies and improve operational efficiency.
  • HCL Technologies: HCL Technologies uses HR Data Analytics to drive their talent management initiatives, improve hiring practices, and boost employee engagement. They focus on leveraging data to enhance employee experience and retention.
  • Reliance Industries: Reliance Industries integrates HR Data Analytics to support their large and diverse workforce. They use analytics to streamline HR operations, enhance employee engagement, and make informed decisions on workforce planning.
  • Bajaj Finserv: Bajaj Finserv employs HR Data Analytics to enhance their recruitment processes, improve employee performance evaluation, and optimize talent management. They focus on using data-driven insights to support strategic HR decisions.
  • Kotak Mahindra Bank: Kotak Mahindra Bank uses HR Data Analytics to enhance their recruitment strategies, optimize employee engagement, and improve performance management systems. They leverage data to make informed decisions in HR planning.
  • HDFC Bank: HDFC Bank utilizes HR Analytics to drive their talent acquisition and management strategies. They use data insights to enhance employee satisfaction, performance evaluation, and overall HR effectiveness.
  • Capgemini: Capgemini uses HR Data Analytics to streamline their HR processes, enhance talent management, and improve employee engagement. They focus on data-driven approaches to support their HR initiatives and business goals.
  • Mahindra Group: The Mahindra Group employs HR Data Analytics to optimize their talent management practices, improve recruitment strategies, and enhance employee engagement. They use data insights to support HR decision-making and workforce planning.


These companies, along with many others, recognize the value of HR Data Analytics in enhancing their HR functions and driving business success. By integrating data-driven approaches, they aim to improve employee experience, optimize talent management, and support strategic business objectives.

 

 

|| Get HR Data Analytics Certification 

Three easy steps will unlock your HR Data Analytics Certification:

 

  • Finish the online / offline course of HR Data Analytics Course and the Assignment
  • Take on and successfully complete a number of industry-based Projects
  • Pass the HR Data  Analytics Certification exam

 

The certificate for this HR Data Analytics Certification will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile.


Certificate

|| Frequently asked question

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

This course is suitable for HR professionals, managers, data analysts, and anyone interested in leveraging data to enhance HR practices and workforce management strategies. It caters to both beginners and experienced professionals looking to develop expertise in HR analytics.

Most reputable HR Data Analytics courses offer a certificate of completion, which can validate your skills and be added to your resume or LinkedIn profile. It's essential to verify the accreditation and recognition of the issuing institution or organization.

Yes, many HR Data Analytics courses are available online, offering flexibility in terms of timing and location. Online courses often provide video lectures, interactive exercises, and discussion forums to facilitate learning.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

Yes, the course includes several hands-on projects and case studies that simulate real-world HR scenarios. These projects help reinforce theoretical knowledge and provide practical experience.

Yes, participants will receive a certificate upon successfully completing the course and meeting all requirements, such as project submissions and assessments.

Yes, many courses include group projects to simulate collaborative work environments and provide experience in teamwork and communication.

Many courses are designed for beginners with no prior experience in data analytics or HR. Some courses may have advanced modules for those with prior experience.

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