Professional Data Analyst

This module focuses on using Microsoft Excel as a powerful tool for data handling and analysis. Students begin with Excel fundamentals, including formatting, formulas, and shortcuts, then progress to logical and lookup functions such as IF, VLOOKUP, HLOOKUP, and XLOOKUP. The module also covers advanced tools like Pivot Tables, Slicers, Scenario Analysis, and Sensitivity Analysis to generate insights from data. Learners explore Excel charts, dashboard elements, and the Data Analysis ToolPak for descriptive statistics, concluding with techniques for basic data cleaning in Excel to prepare datasets for deeper analysis.

Module Lessons:

1. ➠ Excel basics: formatting, formulas, and shortcuts
2. ➠ IF, nested IF, VLOOKUP, HLOOKUP, and XLOOKUP
3. ➠ Pivot Tables and Slicers for dynamic data analysis
4. ➠ Scenario analysis and sensitivity analysis
5. ➠ Excel charts and dashboard components
6. ➠ Excel Data Analysis ToolPak for descriptive statistics
7. ➠ Basic data cleaning techniques in Excel

Tools and Technologies Covered:

This module teaches the fundamentals of SQL using PostgreSQL and MySQL, covering core commands such as SELECT, WHERE, GROUP BY, and JOINs. Learners explore aggregations, window functions, and subqueries while applying their skills to real business scenarios including customer churn analysis and revenue analysis.

Module Lessons:

1. ➠ Introduction to relational databases (PostgreSQL and MySQL)
2. ➠ SELECT, WHERE, GROUP BY, HAVING, and ORDER BY clauses
3. ➠ Filtering, sorting, and wildcard operations
4. ➠ JOIN operations: INNER, LEFT, RIGHT, and FULL OUTER
5. ➠ Aggregation functions: COUNT, SUM, AVG, MAX, and MIN
6. ➠ Window functions: RANK, DENSE_RANK, LEAD, and LAG
7. ➠ Creating views and working with subqueries
8. ➠ Real-world business cases including customer churn and revenue by category

Tools and Technologies Covered:

This module begins with setting up a Python development environment using Jupyter Notebook and Visual Studio Code, then introduces core programming concepts such as variables, loops, and conditionals. Learners work with essential data structures including lists, dictionaries, tuples, and sets, create functions and lambda expressions, and manage files and directories. The module also covers error handling, debugging techniques, and version control using Git and GitHub.

Module Lessons:

1. ➠ Python environment setup (Jupyter Notebook and VS Code)
2. ➠ Python syntax: variables, operators, loops, and conditionals
3. ➠ Core data structures: lists, dictionaries, tuples, and sets
4. ➠ Functions and lambda expressions
5. ➠ Working with files and directories
6. ➠ Error handling and debugging techniques
7. ➠ Version control fundamentals using Git
8. ➠ Collaborative development using GitHub

Tools and Technologies Covered:

Learn how to use Pandas and NumPy for efficient data cleaning, transformation, and exploratory analysis. Work with different file formats like CSV, Excel, and JSON, handle missing values, duplicates, and perform tasks like filtering, merging, and aggregating data. Gain hands-on experience with EDA using real-world datasets and clean unstructured data using Regex.

Module Lessons:

1. ➠ Introduction to Pandas and NumPy
2. ➠ Reading/writing CSV, Excel, and JSON data
3. ➠ Data cleaning: handling missing values, duplicates, nulls
4. ➠ Filtering, slicing, sorting, merging datasets
5. ➠ GroupBy operations and aggregations
6. ➠ Exploratory Data Analysis (EDA) with real-world datasets
7. ➠ Regex & text cleaning for unstructured data

Tools and Technologies Covered:

Explore how AI-driven analytics enhances data insights using Python. Learn to integrate OpenAI APIs, use ChatGPT for dynamic summaries, and build natural language query systems. Apply sentiment analysis with Hugging Face Transformers and automate data summarization and anomaly detection using machine learning techniques.

Module Lessons:

1. ➠ What is AI-driven analytics?
2. ➠ Integrating OpenAI API for data insights
3. ➠ Using ChatGPT or similar LLMs for dynamic summaries
4. ➠ Building Natural Language Query systems
5. ➠ Automating summary generation from large datasets
6. ➠ Using ML-based outlier detection for anomaly analytics

Tools and Technologies Covered:

Master data visualization in Python using Matplotlib, Seaborn, and Plotly to create clear, informative, and interactive charts. Learn to build various chart types, enhance visuals with styling and color coding, and apply these skills to real-world cases like marketing and operations performance dashboards.

Module Lessons:

1. ➠ Chart types: bar, line, histogram, scatter, box plot
2. ➠ Heatmaps, pair plots, and correlation matrices
3. ➠ Styling and color coding for clarity
4. ➠ Plotly for interactive dashboards
5. ➠ Real use case: visualize marketing performance / sales data
6. ➠ Real use case: visualize operational performance / operations data

Tools and Technologies Covered:

Build a strong foundation in business statistics essential for data analysis. Learn descriptive and inferential statistics, explore probability distributions, and understand the difference between correlation and causation. Apply techniques like hypothesis testing and regression analysis using scikit-learn, and discover the practical scope of statistics in business analytics

Module Lessons:

1. ➠ Descriptive statistics: mean, median, mode, SD, variance
2. ➠ Probability basics and distributions (normal, binomial)
3. ➠ Correlation vs causation
4. ➠ Inferential statistics: confidence intervals, p-values
5. ➠ Hypothesis testing (t-test, z-test basics)
6. ➠ Simple & multiple linear regression with scikit-learn
7. ➠ Scopes of Business Statistics in Analytics

Tools and Technologies Covered:

Learn how to transform data into compelling visual stories using Tableau. Master the essentials of the Tableau interface, create various chart types, and apply filters, parameters, and calculated fields. Explore dashboard design, build interactive Tableau Stories, and publish your work online—including a final project tied to the Capstone.

Module Lessons:

1. ➠ Tableau essentials: UI, connecting to files and databases
2. ➠ Building bar, line, pie, heat maps, scatter plots
3. ➠ Filtering, parameters, calculated fields
4. ➠ Dashboard design & best practices
5. ➠ Storytelling with Tableau Stories
6. ➠ Publishing dashboards (Tableau Public & resume linkage)
7. ➠ Final Tableau project linked with Capstone

Tools and Technologies Covered:

Apply everything you’ve learned in a hands-on Capstone Project that simulates a real-world business scenario. Design a database, clean and analyze sales data using SQL, and uncover trends and insights. Then, use Tableau to visualize the results with interactive dashboards and deliver a compelling data story to present key business findings.

Module Lessons:

1. ➠ Design a database schema with relevant business tables
2. ➠ Clean and prepare raw sales data using SQL
3. ➠ Write SQL queries to analyze sales by region, category, and customer segment
4. ➠ Identify monthly and quarterly sales trends using SQL
5. ➠ Determine top-performing customers and products
6. ➠ Detect underperforming regions and product categories
7. ➠ Export query results for Tableau visualization
8. ➠ Create visualizations using bar, line, pie, and map charts in Tableau
9. ➠ Build an interactive Tableau dashboard with filters and parameters
10. ➠ Present business insights with visual storytelling

Tools and Technologies Covered:

Put your AI and Python skills into action with a customer feedback analytics project. Use Pandas to clean and structure text data, apply sentiment analysis using AI models or APIs, and generate automated insights. Visualize the results using Python libraries like Matplotlib, Seaborn, and Plotly, then compile everything into a complete Jupyter Notebook for a professional presentation of findings and AI-driven recommendations.

Module Lessons:

1. ➠ Load and preprocess customer feedback data using Python
2. ➠ Clean and structure data using Pandas
3. ➠ Perform sentiment analysis using AI APIs or pre-trained models
4. ➠ Generate automated insights using OpenAI or similar APIs
5. ➠ Create visualizations using Matplotlib, Seaborn, or Plotly
6. ➠ Display sentiment distribution and keyword frequency
7. ➠ Build interactive plots and dashboards within Python
8. ➠ Summarize customer feedback into key action points using AI
9. ➠ Compile a complete Jupyter Notebook with code, insights, and visuals
10. ➠ Present findings and AI-driven recommendations to stakeholders

Tools and Technologies Covered: