🔍 Module 1: Introduction to Data Analytics
- What is Data Analytics?
- Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Role of a Data Analyst in organizations
- Analytics workflow: From raw data to insights
- Tools overview: Excel, SQL, Python, BI tools
📊 Module 2: Excel for Data Analysis
- Data entry, formatting, and cleaning in Excel
- Formulas & functions (VLOOKUP, INDEX-MATCH, IF, etc.)
- Pivot Tables & Pivot Charts
- Conditional formatting
- Data visualization basics in Excel
- Introduction to Power Query & Power Pivot
📈 Module 3: Statistics & Probability for Data Analysis
- Descriptive statistics: Mean, Median, Mode, Variance, Standard Deviation
- Probability concepts: Independent & dependent events
- Probability distributions: Normal, Binomial, Poisson
- Hypothesis testing (t-test, chi-square, ANOVA)
- Correlation & Regression basics
- Real-world applications in decision making
🗄️ Module 4: SQL for Data Analysts
- Introduction to Databases & SQL
- Basic queries: SELECT, WHERE, ORDER BY, LIMIT
- Filtering with logical operators
- Aggregate functions: COUNT, SUM, AVG, MIN, MAX
- GROUP BY & HAVING
- Joins (INNER, LEFT, RIGHT, FULL OUTER)
- Subqueries & CTEs
- Window functions (ROW_NUMBER, RANK, PARTITION BY)
- Case studies with business datasets
🐍 Module 5: Python for Data Analysis
- Python basics: Data types, loops, functions
- Working with Jupyter Notebook
- NumPy: Arrays, vectorized operations, indexing
- Pandas: Series, DataFrames, data cleaning, grouping, merging, pivoting
- Matplotlib & Seaborn: Data visualization basics
- Handling missing data & outliers
- Exploratory Data Analysis (EDA) with Python
📊 Module 6: Data Visualization & BI Tools
- Principles of effective data visualization
- Choosing the right chart (bar, line, scatter, heatmap, etc.)
- Dashboard creation
- Tools: Tableau / Power BI
- Storytelling with data
- Hands-on: Create interactive dashboards
🤖 Module 7: Advanced Analytics with Python
- Feature engineering & scaling
- Time-series analysis basics
- Introduction to Scikit-learn
- Linear & Logistic Regression
- Decision Trees
- Clustering (K-Means)
- Model evaluation: Accuracy, Precision, Recall, F1 Score
- Case study: Predictive modeling for business
☁️ Module 8: Big Data & Cloud Basics
- Introduction to Big Data concepts
- Overview of Hadoop & Spark
- Cloud platforms: AWS, GCP, Azure for Analysts
- Using BigQuery for SQL on large datasets
💼 Module 9: Business Applications of Data Analytics
- Marketing analytics (customer segmentation, churn analysis)
- Sales & Revenue analytics
- Operations & Supply chain analytics
- Finance & Risk analytics
- Case studies across industries
🏆 Module 10: Capstone Project
End-to-end project combining:
- Data extraction (SQL)
- Data cleaning & wrangling (Pandas)
- Statistical analysis (Hypothesis testing, regression)
- Visualization (Tableau/Power BI)
- Business insights & storytelling
✨ Extras (Soft Skills & Career Prep)
- Writing data-driven business reports
- Storytelling with data
- Resume building for Data Analysts
- Interview preparation (SQL, case studies, problem-solving)
- Peer networking & mock interviews
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