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Data Analytics – Intermediate Level

Course Overview

This course builds on the beginner-level foundation by diving deeper into data preparation, analysis, visualization, and interpretation. Learners will handle realistic datasets, explore advanced Excel tools, get an introduction to SQL and Python for analytics, and learn how to present insights for decision-making.

Duration: 4–6 weeks
Format: Hands-on exercises + projects
Certification: Certificate of Completion

🔹 Key Modules

Module 1: Advanced Excel for Analytics

• Advanced formulas (INDEX-MATCH, TEXT, IFERROR, ARRAY Formulas).

• Advanced PivotTables & PivotCharts (calculated fields, slicers, timelines).

• Building dynamic dashboards in Excel.

• Exercise: Create a sales dashboard with KPIs (revenue, top product, regional sales).

Module 2: Data Cleaning & Transformation

• Handling large, messy datasets.

• Power Query basics (merge, append, reshape).

• Data formatting for consistency.

• Exercise: Clean and restructure a multi-sheet dataset.

Module 3: Data Visualization Principles

• Best practices for storytelling with data.

• Choosing the right chart for the question.

• Introduction to interactive visuals (Excel + Power BI/Google Data Studio).

• Exercise: Rebuild poor charts into clear, accurate visuals.

Module 4: Introduction to SQL for Analytics

• SELECT with multiple conditions (AND/OR/IN/BETWEEN).

• Aggregations (GROUP BY, HAVING).

• JOIN basics (INNER JOIN, LEFT JOIN).

• Subqueries for deeper insights.

• Exercise: Use SQL to analyze the multi-table sales & customers dataset.

Module 5: Introduction to Python for Analytics

• Why Python for analytics?

• Getting started with Pandas & NumPy.

• Importing, cleaning, and summarizing datasets.

• Basic plotting with Matplotlib/Seaborn.

• Exercise: Analyze a CSV dataset in Jupyter Notebook.

Module 6: Applied Business Case Studies

• Business (e.g., sales trend analysis).

• Economics (e.g., inflation & unemployment trends).

• Finance (e.g., portfolio performance & risk).

• Capstone Project: Learners pick a dataset, clean it, analyze it, and present findings in a dashboard or report.

🔹 Skills Gained

• Efficient use of Excel beyond basics.

• Data cleaning & preparation with Power Query.

• SQL for querying and joining datasets.

• Python for basic automation and visualization.

• Turning data into business insights.

👉 This intermediate course bridges the gap between basic tools and real-world application

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