Data Analytics & Business Intelligence
Master Google Sheets/Excel, SQL, Python, and Power BI for Real-World Data Analysis
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4 Hours
Course Incharge
Muzammil Bilwani
📋 Prerequisites
✓ Basic computer literacy and familiarity with spreadsheets. No prior programming experience required.
📖 Course Description
A practical, hands-on 4.5-month journey into data analytics and business intelligence. Learners build a strong foundation in Google Sheets/Excel, progress into SQL for querying databases, learn Python for data analysis and automation, and finish with Power BI for professional dashboards and reporting. Every topic is taught through real datasets and business scenarios, culminating in a capstone project that integrates all four tool sets.
What You Will Learn
Explore, clean, and visualize data in Google Sheets and Excel
Query and manage relational databases using SQL
Apply core statistics or EDA to real datasets
Write Python code for data analysis using Pandas, NumPy, and visualization libraries
Build interactive dashboards and reports in Power BI
Automate repetitive tasks using VBA and Power Query
Build forecasts and time series models
Present a full analytics report combining SQL, Sheets/Excel, Python, and Power BI
Course Outline
Introduction to Data Analytics
- →Understanding the role of data analytics in decision-making
- →Introduction to data-driven insights
- →Overview of tools: Sheets/Excel, SQL, Python, Power BI
- →Exploring industry case studies
- →Hands-on: Analyze a sample business problem
Data Exploration and Visualization in Google Sheets/Excel
- →Sorting, filtering, and summarizing data
- →Using pivot tables to uncover patterns
- →Creating charts and applying conditional formatting
- →Hands-on: Analyze a dataset to uncover trends
Advanced Sheets/Excel: Dashboards and Formulas
- →Lookup and reference formulas (VLOOKUP, XLOOKUP, INDEX/MATCH)
- →Building interactive dashboards with slicers and drop-downs
- →Data validation and formatting for clean reports
- →Hands-on: Build a one-page sales/performance dashboard
Introduction to Data and Databases (SQL)
- →Understanding relational databases and SQL basics
- →Basic SQL commands: SELECT, WHERE, ORDER BY
- →Hands-on practice with SQLite or MySQL
- →Hands-on: Query a sample student or sales database
SQL: Joins, Grouping, and Aggregation
- →Combining tables with JOIN (INNER, LEFT, RIGHT)
- →GROUP BY, HAVING, and aggregate functions
- →Subqueries and nested logic
- →Hands-on: Build multi-table reports from a business database
Data Cleaning and Preprocessing
- →Handling missing and inconsistent data
- →Removing duplicates and standardizing formats
- →Using formulas and SQL to transform data
- →Hands-on: Clean a messy real-world dataset for analysis
Exploratory Data Analysis (EDA)
- →Calculating descriptive statistics
- →Analyzing relationships and distributions
- →Visualizing insights using Sheets/Excel
- →Hands-on: EDA on a customer or academic dataset
Statistical Fundamentals for Data Analysis
- →Intro to probability and hypothesis testing
- →Correlation and regression basics
- →Applying statistics to real-world business decisions
- →Hands-on: Apply formulas to analyze real scenarios
Automation Basics: VBA and Power Query
- →Declaring variables and writing simple VBA functions
- →Loops, conditionals, and message boxes
- →Introduction to Power Query: importing and cleaning data
- →Hands-on: Automate a repetitive Excel task
Introduction to Python for Data Analysis
- →Setting up Python and Jupyter/Colab
- →Variables, data types, and basic syntax
- →Control flow: if statements, loops
- →Hands-on: Write your first data-handling Python script
Python Data Structures and Functions
- →Lists, dictionaries, tuples, and sets
- →Writing reusable functions
- →Reading and writing CSV/Excel files in Python
- →Hands-on: Process a dataset using core Python
Data Analysis with Pandas and NumPy
- →Introduction to Pandas DataFrames and NumPy arrays
- →Filtering, sorting, and aggregating data with Pandas
- →Merging and joining datasets in Python
- →Hands-on: Analyze a sales or survey dataset using Pandas
Data Visualization with Python
- →Introduction to Matplotlib and Seaborn
- →Building bar charts, line charts, histograms, and heatmaps
- →Storytelling with visualizations
- →Hands-on: Build a visual report from a cleaned dataset
Predictive Analytics and Forecasting
- →Linear regression for forecasting in Sheets/Excel and Python
- →Evaluating model performance (R-squared, RMSE)
- →Applying predictive models to business questions
- →Hands-on: Forecast sales or grades using regression
Time Series Analysis
- →Moving averages and smoothing techniques
- →Trend and seasonality detection
- →Forecasting with time series data in Sheets/Excel and Python
- →Hands-on: Analyze website traffic or financial data over time
Power BI Fundamentals
- →Power BI interface and data import
- →Data modeling and relationships between tables
- →Building basic visuals and reports
- →Hands-on: Load and model a business dataset in Power BI
Power BI: DAX and Interactive Dashboards
- →Introduction to DAX formulas and measures
- →Building interactive dashboards with filters and slicers
- →Publishing and sharing Power BI reports
- →Hands-on: Build a complete KPI dashboard in Power BI
Case Studies and Capstone Project
- →Integrating SQL, Sheets/Excel, Python, and Power BI in one workflow
- →Analyzing an industry-specific case study
- →Capstone: Build a full analytics report from raw data to dashboard
- →Present findings and receive feedback
📊 Grading Criteria
| Component | Percentage |
|---|---|
| Quizzes | 20% |
| Class Participation / Attendance | 15% |
| Projects | 25% |
| Final Projects | 40% |
| Total | 100% |
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