Course Title: Data Analytics

Level: Beginner to Intermediate

Duration: 12–16 Weeks

Mode: Theory + Hands-on Practice

Course Description

This course introduces learners to the fundamentals of data analytics, focusing on collecting, cleaning, analyzing, and visualizing data to support data-driven decision-making. Students will work with real-world datasets and industry-standard tools to extract insights and communicate findings effectively.

Course Objectives

By the end of this course, learners will be able to:

• Understand data types, structures, and sources
• Clean and prepare data for analysis
• Perform exploratory and statistical analysis
• Use analytical tools to visualize and interpret data
• Present insights through dashboards and reports

Module 1: Introduction to Data Analytics

• What is Data Analytics?
• Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
• Analytics lifecycle
• Applications of Data Analytics
• Career paths in Data Analytics

Module 2: Data Types & Data Collection

• Structured vs unstructured data
• Qualitative vs quantitative data
• Data sources (databases, APIs, surveys, web data)
• Data quality and integrity
• Ethics and privacy in data

Module 3: Excel for Data Analytics

• Excel interface and functions
• Data cleaning techniques
• Pivot tables & pivot charts
• Lookup functions (VLOOKUP, XLOOKUP)
• Data analysis using Excel

Module 4: Statistics for Data Analytics

• Descriptive statistics
• Probability concepts
• Data distributions
• Correlation and regression basics
• Hypothesis testing

Module 5: SQL for Data Analysis

• Introduction to databases
• SQL queries (SELECT, WHERE, JOIN)
• Aggregations and filtering
• Subqueries
• Data extraction for analysis

Module 6: Data Cleaning & Preparation

• Handling missing values
• Outlier detection
• Data normalization
• Data transformation
• Data validation techniques

Module 7: Python for Data Analytics (Optional / Intermediate)

• Python basics for analytics
• Libraries: Pandas, NumPy, Matplotlib, Seaborn
• Data manipulation with Pandas
• Data visualization with Python
• Intro to Jupyter Notebook

Module 8: Data Visualization

• Principles of data visualization
• Charts and graphs selection
• Storytelling with data
• Dashboards and reports
• Tools: Power BI / Tableau (overview or hands-on)

Module 9: Exploratory Data Analysis (EDA)

• EDA techniques
• Pattern and trend analysis
• Feature understanding
• Visualization-driven analysis
• Business insights from EDA

Module 10: Business & Marketing Analytics

• KPIs and metrics
• Customer analytics
• Sales and revenue analysis
• Marketing campaign analytics
• Decision-making using data

Module 11: Advanced Analytics (Overview)

• Predictive analytics basics
• Forecasting techniques
• Introduction to machine learning
• Big data concepts
• Automation in analytics

Module 12: Reporting & Presentation

• Creating analytics reports
• Data storytelling techniques
• Dashboard presentation
• Communicating insights to stakeholders
• Best practices for analytics reporting

Practical Components

• Excel-based data analysis project
• SQL query assignments
• Data cleaning and EDA project
• Visualization dashboard project
• Final capstone project (real-world dataset analysis)

Assessment Methods

• Assignments & quizzes
• Practical lab work
• Mid-term evaluation
• Final project & presentation

Tools Covered

• Microsoft Excel
• SQL (MySQL / PostgreSQL)
• Python (Pandas, NumPy)
• Power BI / Tableau
• Google Sheets

If you want, I can:

• Customize this for degree programs or short-term courses
• Create a weekly lesson plan
• Simplify it for non-technical students