Advanced Data Analysis Course

Topics Covered:

• •What is Data Analysis?
• •Types of Data (Structured vs. Unstructured)
• •Data Analysis Process (Collection → Cleaning → Exploration → Modeling → Interpretation)
• •Key Roles: Data Analyst vs. Data Scientist vs. Business Analyst
• •Real: Real-world Applications of Data Analysis

Practical Exercises:

Topics Covered:

• •Primary vs. Secondary Data Sources
• •Data Collection tch Tools and Techniques
• •Data Formats: CSV, Excel, JSON, SQL Databases, APIs
• •Introduction to Web Scraping and APIs (Optional)

Practical Exercises:

• ✓Hands-on Activity: Importing data from different sources

Topics Covered:

• •Understanding Data Quality
• •Handling Missing Data
• •Dealing with Outliers and Duplicates
• •Data Type Conversion
• •Normalization and Standardization
• •Data Transformation Techniques

Practical Exercises:

• ✓Hands-on Exercise: Cleaning raw data in Excel or Python

Topics Covered:

• •Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• •Data Visualization for EDA
• •Detecting Patterns, Trends, and Relationships
• •Correlation and Covariance
• •Tools: Excel, Python (pandas, matplotlib, seaborn)

Practical Exercises:

• ✓Case Study: EDA on Sales or Health Data

Topics Covered:

• •Principles of Effective Data Visualization
• •Charts: Bar, Line, Pie, Scatter, Histogram, Boxplot, Heatmaps
• •Interactive Dashboards (Power BI/Tableau)
• •Choosing the Right Chart Type
• •Storytelling with Data

Practical Exercises:

• ✓Hands-on: Build a dynamic dashboard in Power BI

Topics Covered:

• •Probability Basics
• •Hypothesis Testing (t-test, chi-square, ANOVA)
• •Confidence Intervals
• •Regression Analysis: Linear, Logistic
• •Statistical Significance

Practical Exercises:

• ✓Hands-on with Python (SciPy, statsmodels)

Topics Covered:

• •Excel Functions: VLOOKUP, INDEX-MATCH, IF, COUNTIF, etc.
• •Pivot Tables and Pivot Charts
• •Excel for Statistical Analysis
• •E-mail Data Models and Power Query

Practical Exercises:

• ✓Case Study: Sales Forecasting using Excel

Topics Covered:

• •Introduction to Python for Data Analysis
• •Working with pandas, numpy, matplotlib, seaborn
• •DataFrames and Series
• •Writing Custom Functions
• •Data Cleaning with Python
• •EDA with Python Libraries
• •Optional: Intro to Jupyter Notebook/Google Colab

Practical Exercises:

Topics Covered:

• •Introduction to Relational Databases
• •Basic SQL Queries (SELECT, WHERE, JOIN, GROUP BY)
• •Aggregation and Filtering
• •Nested Queries and Subqueries

Practical Exercises:

• ✓Case Study: Analyzing HR or Sales Data using SQL

Topics Covered:

• •Introduction to Power BI Interface
• •Importing and Cleaning Data
• •Creating Data Models and Relationships
• •DAX Basics
• •Interactive Visuals and Dashboards
• •Publishing Reports and Sharing Insights

Practical Exercises:

Topics Covered:

• •Introduction to Machine Learning Concepts
• •Supervised vs. Unsupervised Learning
• •Common Algorithms: Linear Regression, KNN, Decision Trees
• •Model Evaluation Metrics
• •Use of Scikit-learn

Practical Exercises:

• ✓Hands-on Project: Predicting Customer Churn

Topics Covered:

• •Real-World Dataset
• •End-to-End Project: Clean, Analyze, Visualize, and Present
• •Tools: SQL + Python + Power BI
• •Final Presentation to Instructors/Peers

Practical Exercises: