🔹 Data Analytics Advanced: Course Overview

1. Objective

• Equip learners with advanced tools to analyze large, messy datasets.

• Enable predictive analytics, automation, and interactive visualization.

• Prepare learners for real-world data projects in business, finance, and economics.

2. Key Modules & Topics

Module 1: Advanced Excel Analytics

• Advanced formulas: INDEX-MATCH, ARRAY formulas, IFERROR, TEXT.

• Advanced PivotTables & PivotCharts: calculated fields, slicers, timelines.

• Building dynamic dashboards for interactive reporting.

• Handling large messy datasets efficiently.

• Power Query basics: merge, append, reshape datasets.

• Best practices for data storytelling.

Module 2: Advanced SQL Analytics

• Subqueries and correlated subqueries for deeper insights.

• JOINs: INNER, LEFT, RIGHT, FULL OUTER, and CROSS JOIN.

• Aggregations with GROUP BY, HAVING, and advanced window functions (ROW_NUMBER(), RANK(), LEAD(), LAG()).

• Creating views and temporary tables for complex analysis.

• Optimizing queries for large datasets.

Module 3: Python for Advanced Analytics

• Working with large datasets using Pandas and NumPy.

• Data cleaning at scale: missing values, duplicates, type conversion, outlier handling.

• Feature engineering: creating new variables for analysis or modeling.

• Advanced visualization: Matplotlib + Seaborn + Plotly for interactive dashboards.

• Introduction to time series analysis (trend, seasonality, rolling averages).

• Basics of predictive modeling using scikit-learn (linear regression, classification).

Module 4: Business, Economics & Finance Applications

• Business: sales forecasting, customer segmentation, churn analysis.

• Economics: inflation trends, unemployment trends, macroeconomic indicators analysis.

• Finance: portfolio performance, risk assessment, correlation, Sharpe ratios, risk-return analysis.

Module 5: Data Storytelling & Dashboards

• Principles of effective visualization: clarity, simplicity, accuracy.

• Choosing the right chart for the question: bar, line, scatter, pie, histogram.

• Combining Excel, Python, and Power BI/Google Data Studio for interactive dashboards.

• Best practices for communicating insights to decision-makers.

3. Hands-On Projects

• Real datasets from business, economics, and finance.

• Step-by-step guided exercises:

  • Clean, transform, and summarize datasets.

  • Create interactive dashboards.

  • Perform predictive analytics on time series or portfolio data.

• Capstone project: an end-to-end analysis, integrating all tools and techniques.

4. Learning Outcomes

By the end of this module, learners will be able to:

1. Handle large and messy datasets efficiently.

2. Perform complex queries and aggregations using SQL.

3. Clean, transform, and analyze data with Python at scale.

4. Build interactive dashboards and visualizations for business insights.

5. Apply predictive analytics techniques to forecast trends.

6. Communicate insights effectively to support decision-making.

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