Human Resources (HR)
Human Resources (HR) is a vital department within any organization, responsible for managing the employee life cycle, which includes recruitment, onboarding, training, performance management, and employee relations. HR ensures that the company attracts and retains skilled professionals while maintaining a productive and positive work environment. The department also plays a key role in enforcing company policies, ensuring legal compliance, and supporting organizational development. By focusing on people management, HR helps align employee goals with the company’s objectives, fostering both individual growth and overall business success.
Recruitment & Talent Acquisition
Understanding job descriptions and candidate requirements.
Assisting in resume screening and shortlisting.
Participating in scheduling and conducting interviews.
Maintaining candidate databases and follow-up communication.
Role of HR in Employee Engagement
- Planning employee engagement events and activities.
- Conducting engagement surveys and feedback analysis.
- Promoting internal communication and collaboration.
- Analyzing impact of engagement strategies.
Employee Onboarding Experience
- Coordinating document collection and verification.
- Introducing company policies and team structures.
- Ensuring smooth induction and orientation.
- Collecting feedback on onboarding experience.
Performance Appraisal Methods
- Understanding appraisal techniques.
- Supporting performance data collection.
- Assisting managers with review documentation.
- Preparing performance summary reports.
Training Need Analysis
Identifying skill gaps through employee surveys.
Coordinating with department heads to assess needs.
Designing training schedules and resource plans.
Preparing pre- and post-training evaluations.
Employee Satisfaction Survey
Designing simple yet effective survey questions.
Collecting responses confidentially.
Analyzing results using charts or tools like Excel.
Suggesting improvements based on findings.
Role of HR in Employer Branding
Showcasing company culture via social media and careers page.
Gathering testimonials from employees and interns.
Assisting with employer branding campaigns.
Monitoring reputation on platforms like Glassdoor.
Digital HR
Working with HR software (HRMS).
Learning to update and extract reports from HRIS.
Understanding digital leave and attendance systems.
Supporting automation of HR tasks.
DAY : 1
- Introduction to Data Science
- Python Fundamentals
- Variables, Data Types & Operators
DAY : 2
- Control Statements & Loops
- Functions, Lists & Dictionaries
- NumPy Basics
DAY : 3
- Pandas DataFrames
- Data Cleaning & Preprocessing
- Data Visualization with Matplotlib
DAY : 4
- Introduction to Machine Learning
- Linear Regression
- Model Training & Prediction
DAY : 5
- Real-Time Data Science Project
- Model Evaluation & Testing
- Project Presentation & Career Guidance
DAY : 1
- Introduction to Data Science
- Data Science Life Cycle
- Python Environment Setup
DAY : 2
- Python Fundamentals
- Variables, Data Types & Operators
- Control Statements & Loops
DAY : 3
- Functions & Modules
- Lists, Tuples & Dictionaries
- File Handling in Python
DAY : 4
- NumPy Basics
- Array Operations
- Mathematical & Statistical Functions
DAY : 5
- Pandas Introduction
- DataFrames & Data Cleaning
- Handling Missing Values
DAY : 6
- Data Visualization
- Matplotlib Charts
- Exploratory Data Analysis (EDA)
DAY : 7
- Data Preprocessing
- Feature Engineering
- Train-Test Split
DAY : 8
- Introduction to Machine Learning
- Linear Regression
- Model Training & Prediction
DAY : 9
- Classification Algorithms
- Model Evaluation Metrics
- Real-Time Dataset Analysis
DAY : 10
- Complete Data Science Project
- Project Presentation
- Career Guidance & Interview Preparation
DAY : 1
- Introduction to Data Science
- Data Science Life Cycle
- Python Environment Setup
DAY : 2
- Python Fundamentals
- Variables, Data Types & Operators
- Input, Output & Type Casting
DAY : 3
- Conditional Statements
- Loops & Pattern Programs
- Functions & Modules
DAY : 4
- Strings
- Lists, Tuples & Dictionaries
- File Handling in Python
DAY : 5
- NumPy Introduction
- Array Creation & Operations
- Mathematical Functions
DAY : 6
- Pandas Introduction
- Series & DataFrames
- Importing CSV Files
DAY : 7
- Data Cleaning
- Handling Missing Values
- Data Transformation
DAY : 8
- Data Visualization
- Matplotlib Charts
- Exploratory Data Analysis (EDA)
DAY : 9
- Feature Engineering
- Feature Scaling
- Train-Test Split
DAY : 10
- Introduction to Machine Learning
- Supervised vs Unsupervised Learning
- Machine Learning Workflow
DAY : 11
- Linear Regression
- Model Training
- Prediction & Performance
DAY : 12
- Classification Algorithms
- Decision Tree & KNN
- Model Evaluation Metrics
DAY : 13
- K-Means Clustering
- Model Comparison
- Hyperparameter Tuning
DAY : 14
- Real-Time Data Science Project
- Dataset Analysis
- Model Building & Testing
DAY : 15
- Project Presentation
- Resume & Interview Preparation
- Course Review & Career Guidance
Week : 1
- Introduction to Data Science
- Python Environment Setup
- Python Fundamentals
- Variables, Data Types & Operators
- Control Statements & Loops
Week : 2
- Functions & Modules
- Strings, Lists & Tuples
- Dictionaries & Sets
- File Handling
- NumPy Basics
Week : 3
- Pandas Introduction
- Series & DataFrames
- Data Cleaning & Preprocessing
- Handling Missing Values
- Data Visualization with Matplotlib
Week : 4
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Feature Scaling
- Train-Test Split
- Machine Learning Workflow
Week : 5
- Linear Regression
- Classification Algorithms
- Decision Tree & Random Forest
- Model Evaluation Metrics
- Hyperparameter Tuning
Week : 6
- Real-Time Data Science Project
- Model Building & Testing
- Project Deployment Overview
- Resume & Interview Preparation
- Project Presentation
WEEK : 1
- Introduction to Data Science
- Data Science Life Cycle
- Python Environment Setup
- Python Fundamentals
- Variables & Data Types
WEEK : 2
- Operators & Input/Output
- Control Statements
- Loops & Functions
- Lists, Tuples & Dictionaries
- Python Practice Programs
WEEK : 3
- NumPy Introduction
- NumPy Arrays & Operations
- Mathematical Functions
- File Handling
- Python Mini Project
WEEK : 4
- Pandas Introduction
- Series & DataFrames
- Importing CSV & Excel Files
- Data Cleaning
- Handling Missing Values
WEEK : 5
- Data Visualization
- Matplotlib Basics
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Feature Scaling
WEEK : 6
- Introduction to Machine Learning
- Supervised & Unsupervised Learning
- Train-Test Split
- Linear Regression
- Model Training & Prediction
WEEK : 7
- Classification Algorithms
- Decision Tree & Random Forest
- K-Means Clustering
- Model Evaluation Metrics
- Hyperparameter Tuning
WEEK : 8
- Real-Time Data Science Project
- Model Building & Testing
- Project Documentation
- Resume & Interview Preparation
- Project Presentation
WEEK : 1
- Introduction to Data Science
- Data Science Life Cycle
- Python Installation & Environment Setup
- Python Fundamentals
- Variables & Data Types
WEEK : 2
- Operators & Input/Output
- Conditional Statements
- Loops
- Functions
- Python Practice Programs
WEEK : 3
- Strings
- Lists & Tuples
- Dictionaries & Sets
- File Handling
- Mini Python Project
WEEK : 4
- NumPy Introduction
- NumPy Arrays
- Array Operations
- Mathematical Functions
- Statistical Functions
WEEK : 5
- Pandas Introduction
- Series & DataFrames
- Importing CSV & Excel Files
- Data Cleaning
- Handling Missing Values
WEEK : 6
- Data Visualization
- Matplotlib
- Bar, Line & Pie Charts
- Histograms & Scatter Plots
- Exploratory Data Analysis (EDA)
WEEK : 7
- Feature Engineering
- Feature Scaling
- Encoding Categorical Data
- Train-Test Split
- Data Preprocessing Project
WEEK : 8
- Introduction to Machine Learning
- Machine Learning Workflow
- Supervised Learning
- Unsupervised Learning
- Model Training Basics
WEEK : 9
- Linear Regression
- Multiple Linear Regression
- Model Prediction
- Regression Evaluation
- Regression Mini Project
WEEK : 10
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Tree
- Random Forest
- Classification Project
WEEK : 11
- Support Vector Machine (SVM)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Clustering Project
WEEK : 12
- Model Evaluation Metrics
- Confusion Matrix
- Precision, Recall & F1-Score
- Cross Validation
- Hyperparameter Tuning
WEEK : 13
- Time Series Analysis Basics
- Working with Real-Time Datasets
- Data Analysis Case Study
- Model Comparison
- Performance Optimization
WEEK : 14
- End-to-End Data Science Project
- Project Documentation
- Git & GitHub for Data Science
- Project Testing & Validation
- Model Deployment Overview
WEEK : 15
- Complete Data Science Capstone Project
- Project Presentation
- Resume & Portfolio Building
- Interview Preparation
- Course Wrap-Up & Career Guidance
Handling Employee Grievances and Conflict Resolution
- Observing grievance redressal processes.
- Understanding root causes of common conflicts.
- Documenting issues and HR responses.
- Learning communication strategies for resolution.
Employee Retention Strategies
- Identifying reasons behind employee turnover.
- Supporting implementation of retention programs.
- Helping design employee recognition systems.
- Collecting exit interview data for insights.
Exit Interview Analysis and Attrition Trends
- Creating standardized exit interview templates.
- Collecting and organizing feedback.
- Analyzing common reasons for exits.
- Preparing a report on attrition trends and suggestions.
Payroll Management System and Statutory Compliance
- Assisting with salary calculations and attendance mapping.
- Learning basic statutory components (EPF, ESI, TDS).
- Helping generate payslips and salary statements.
- Ensuring confidentiality and data integrity.
Remote Work HR Practices
- Studying policies for remote and hybrid work.
- Tracking remote employee performance and engagement.
- Helping conduct virtual HR events and team meets.
- Identifying challenges like communication gaps.
The Impact of AI and Automations in HR
- Understanding AI tools in resume screening and hiring.
- Exploring chatbots for employee support.
- Learning how automation saves time in HR tasks.
- Discussing ethical issues and data privacy.
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