Duration 5 days – 35 hrs

 

Overview

 

This Data Science and Analytics Training Course provides a structured and hands-on foundation in data analytics, statistics, visualization, and applied machine learning. Participants will learn how to transform raw data into insights, build dashboards and meaningful reports, and create basic predictive models to support business decision-making. The course uses real-world datasets and exercises to ensure practical learning and workplace readiness.

 

Objectives

 

• Understand the full data science lifecycle and analytics workflow
• Define business problems and translate them into measurable data questions
• Perform data cleaning, preparation, and transformation
• Apply essential statistical concepts for analysis and interpretation
• Use effective data visualization techniques for storytelling
• Build and interpret analytics metrics and KPIs
• Perform exploratory data analysis (EDA) to identify patterns and trends
• Create basic predictive models and evaluate model performance
• Communicate results clearly to stakeholders using insight-driven reporting
• Deliver an end-to-end mini project using real datasets

 

Target Audience

 

• Business Analysts / Data Analysts
• Reporting Analysts / MIS Analysts
• Operations, Finance, HR, Sales, Marketing Analysts
• Product Owners / Project Managers needing analytics skills
• IT Professionals transitioning into Data / Analytics roles
• Managers and Decision Makers who work with dashboards and reports
• Fresh graduates or career shifters into Data Analytics / Data Science

Prerequisites 

 

• Basic knowledge of Excel (sorting, filtering, formulas)
• Familiarity with basic math (percentages, averages)
• Comfort working with numbers and tables

 

Course Outline 

 

Module 1: Introduction to Data Science & Analytics

 

• What is Data Science vs Data Analytics?
• Roles: Data Analyst, Data Scientist, Data Engineer
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• Understanding the Data Science lifecycle (CRISP-DM overview)
• Common use cases in business and IT

 

Module 2: Data Thinking & Problem Framing

 

• Turning business goals into analytics questions
• Identifying KPIs and success metrics
• Data sources: internal, external, structured, unstructured
• Measuring value: impact, ROI, decision support
• Activity: framing a problem statement and KPI mapping

 

Module 3: Data Collection & Understanding

 

• Data types: numerical, categorical, time series
• Data quality fundamentals: completeness, accuracy, consistency
• Exploratory review of datasets (columns, meaning, data dictionary)
• Handling missing data and inconsistent formats
• Activity: dataset familiarization + initial profiling

 

Module 4: Data Cleaning & Preparation

 

• Data cleaning techniques:
  • removing duplicates
  • standardizing values
  • data formatting rules
  • missing value handling
• Feature engineering basics:
  • new columns from dates/text/numbers
  • transformations and scaling (conceptual)
• Activity: clean and prepare a dataset for analysis

 

Module 5: Exploratory Data Analysis (EDA)

 

• EDA workflow: what to look for
• Distribution, patterns, and trends
• Correlation and relationships
• Outliers and anomaly detection (basic)
• Activity: EDA checklist + insights report

 

Module 6: Statistics Essentials for Analytics

 

• Mean, median, mode, variance, standard deviation
• Sampling and distributions (practical interpretation)
• Confidence intervals (basic concept)
• Hypothesis testing (intro): p-value (high-level)
• A/B testing overview (business use case)
• Activity: simple stats interpretation exercise

 

Module 7: Data Visualization & Storytelling

 

• Choosing the right chart for the right message
• Best practices: labels, clarity, avoiding misleading charts
• Dashboard design principles:
  • layout, hierarchy, filters, drilldowns
• Storytelling approach: problem → insight → action
• Activity: build a mini dashboard / insight slides

Module 8: Introduction to Predictive Analytics (Machine Learning Basics)

 

• What is Machine Learning? (supervised vs unsupervised)
• Regression vs Classification examples
• Common beginner models:
  • Linear Regression
  • Logistic Regression
  • Decision Trees
• Train/test split concept
• Avoiding overfitting (intro)

 

Module 9: Model Evaluation & Interpretation

 

• Metrics for Regression:
  • MAE, MSE, RMSE, R²
• Metrics for Classification:
  • accuracy, precision, recall, F1-score
  • confusion matrix
• Model interpretation for business stakeholders
• Activity: evaluate a simple model and explain results

 

Module 10: Capstone Mini-Project (End-to-End)

 

• Participants will complete a guided mini-project such as:

• Sales performance analysis
• Customer churn prediction (basic)
• Loan/default risk dataset analysis (simplified)
• Operational efficiency analytics dashboard

• Project Steps:

• Define problem and KPIs
• Clean + prepare data
• Perform EDA
• Visualize insights
• (Optional) Build a simple predictive model
• Present findings + recommendations