“Welcome to Data Analytics with Excel.”

Data is one of the world's most valuable resources, and the ability to collect, organize, analyze, and communicate data has become an essential skill across every profession. Whether you aspire to become a Data Analyst, Business Analyst, Accountant, Financial Analyst, Human Resource Professional, Researcher, Healthcare Analyst, Entrepreneur, or Project Manager, Microsoft Excel remains one of the most powerful and widely used analytical tools.

This course has been carefully designed to take you from having little or no knowledge of Excel to becoming proficient in using Excel for real-world data analytics. Unlike many Excel courses that focus only on formulas or spreadsheet management, this handbook adopts a practical, project-based approach that mirrors how Excel is used in businesses, government agencies, financial institutions, hospitals, schools, NGOs, and multinational organizations.

Throughout this course, you will learn not only how to use Excel but also how to think like a data analyst. You will discover how to clean messy datasets, analyze information, identify trends, build reports, create dashboards, and present insights that support informed decision-making.

The course combines theory with extensive hands-on practice. Every lesson includes worked examples, practical exercises, real-life business scenarios, review questions, assignments, and mini projects to reinforce your understanding.

Whether you are preparing for employment, improving your current skills, or transitioning into the field of data analytics, this course will serve as both your classroom guide and a long-term reference manual.

This course provides a practical introduction to Python, one of the most popular and beginner‑friendly programming languages. You will learn the basics of Python syntax, how to write simple programs, and how to work with data. Key topics include:

• Understanding variables, data types, and operators

• Writing control structures (loops and conditionals)

• Using functions to organize code

• Working with lists, dictionaries, and other data structures

• Reading and writing files

• An introduction to libraries for data analysis and visualization

Data Analytics with Tableau is a hands-on, industry-focused course designed to equip learners with the practical skills required to transform raw data into meaningful insights using Tableau. The course covers the complete analytics workflow, from data connection and preparation to interactive dashboard development and data storytelling.

Students will learn how to connect Tableau to various data sources, clean and structure datasets, create calculated fields, build dynamic visualizations, and design professional dashboards that support data-driven decision-making. Emphasis is placed on analytical thinking, business problem-solving, and effective visual communication.

Through real-world case studies and guided projects, learners will develop the ability to:

• Explore and analyze large datasets efficiently

• Build insightful charts and visualizations

• Apply filters, parameters, and calculated metrics

• Design interactive dashboards for business reporting

• Communicate insights clearly to stakeholders

By the end of the course, participants will be able to independently conduct data analysis tasks and produce executive-level dashboards suitable for portfolio presentation or organizational use.

In this course, you'll learn the following:

• Define data analytics as a professional field
• Distinguish data analytics from other data-related fields
• Define who a data analyst is and what they do
• Explain data analysis as a structured process
• Identify major niches within the data analytics field
• Understand how data analytics creates business value
• Differentiate between intuition-based and data-driven decisions
• Break vague business problems into clear analytical questions
• Formulate simple, testable hypotheses
• Apply “So what?” thinking to derive business insights

SQL (Structured Query Language) is the standard programming language used to communicate with, manage, and manipulate data stored in Relational Database Management Systems (RDBMS).
Think of SQL not just as a tool for storage, but as the "operating system" for business intelligence. While basic SQL allows you to fetch rows and columns, Intermediate SQL transforms that raw data into answers. It serves as the bridge between static files (like the CSVs we will be using) and dynamic business decisions.

In this intermediate course, we define SQL as a tool for logic application, not just data retrieval. We move beyond asking "What happened?" (SELECT *) to asking "Why did it happen, and how does it compare to last month?" (Window Functions & Complex Joins).

Here is what we will cover to take you from basic querying to advanced analysis:

• Week 1: Advanced Structures & Relationships

  • Mastering Common Table Expressions (CTEs) for readable code.

  • Writing Scalar and Correlated Subqueries.

  • Executing complex Joins (Self Joins, Cross Joins) to link Channels, Products, and Sales.

• Week 2: The Power of Window Functions

  • Performing analysis without collapsing rows using OVER and PARTITION BY.

  • Creating Rankings (Top sellers per Zone).

  • Calculating Year-over-Year growth and Moving Averages using LEAD and LAG.

• Week 3: Data Logic & Cleaning

  • Using Conditional Logic (CASE WHEN) to segment customers and orders.

  • Handling dirty data and NULLs with COALESCE.

  • Manipulating text and extracting dates to fix formatting issues.

• Week 4: Optimization & Engineering

  • Creating Views to automate repetitive reporting.

  • Writing Stored Procedures to encapsulate business logic.

  • Understanding Indexing and Execution Plans to make queries run faster.