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

Learn to analyze, interpret, and visualize data using Microsoft Excel. From basics to advanced techniques, this course equips learners to turn raw data into actionable insights.

Learn to analyse, interpret, and visualise data using Microsoft Excel. From basics to advanced techniques, this course equips learners to turn raw data into actionable 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.

Python for Data Analysis is a beginner friendly course to assist with data analysis using python programming language. The concepts are easy to follow and implement. Take the topics step by step while implementing as well to get a full grasp of the content. 

Welcome onboard.