Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Data Analysts
• Business Analysts
• Data Scientists
• IT Managers
• Business Intelligence Professionals
• Digital Transformation Managers

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

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

• Understand the fundamentals of data analytics and machine learning.
• Collect, clean, and prepare data for analysis and modeling.
• Apply statistical techniques to identify trends and patterns.
• Build and evaluate predictive machine learning models.
• Create impactful dashboards and visualizations for decision-makers.
• Implement analytics-driven solutions to solve business challenges.

DAY 1

Foundations of Data Analytics and Machine Learning

• Introduction to data-driven organizations
• Data analytics lifecycle
• Types of data and data sources
• Data quality and governance fundamentals
• Introduction to machine learning concepts
• Analytics and AI use cases across industries

DAY 2

Data Preparation and Exploratory Analysis

• Data collection methods
• Data cleansing and preprocessing
• Handling missing and inconsistent data
• Exploratory Data Analysis (EDA)
• Statistical summaries and data profiling
• Identifying trends, anomalies, and correlations

DAY 3

Machine Learning Essentials

• Supervised vs. unsupervised learning
• Classification algorithms
• Regression techniques
• Clustering and segmentation methods
• Feature engineering concepts
• Model training and validation

DAY 4

Predictive Analytics and Model Optimization

• Performance measurement metrics
• Model evaluation techniques
• Improving model accuracy
• Predictive analytics applications
• Forecasting and trend prediction
• Managing bias and overfitting

DAY 5

Data Visualization and Business Applications

• Data storytelling techniques
• Dashboard design principles
• Visualization best practices
• Communicating analytical findings
• Deploying analytics solutions in organizations
• Capstone case study and action planning