Master the Core Data Analyst Tools & Techniques

Python | SQL | MS Excel | Power BI / Tableau

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🔹 Module 1: Python Basics for Data Analysis

• Introduction to Python
• Python Installation & IDEs
• Variables & Data Types
• Operators
• Control Flow (if-else, loops)
• Functions
• Lambda Functions
• File Handling (CSV, Excel)
• Exception Handling

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🔹 Module 2: NumPy

• Introduction to NumPy
• Arrays & Data Types
• Indexing & Slicing
• Mathematical Operations
• Statistical Functions
• Broadcasting
• NumPy Use Cases

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🔹 Module 3: Pandas

• Pandas Introduction
• Series & DataFrames
• Reading & Writing Data
• Data Cleaning Techniques
• Handling Missing Values
• Data Transformation
• GroupBy & Aggregation
• Merging & Joining DataFrames
• Real-World Pandas Projects

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🔹 Module 4: SQL for Data Analysis

• Introduction to Databases
• Relational Database Concepts
• SQL Data Types
• Data Retrieval using SELECT
• WHERE, BETWEEN, IN, LIKE
• ORDER BY, LIMIT
• Aggregate Functions
  • COUNT, SUM, AVG, MIN, MAX
• GROUP BY & HAVING
• Joins
  • INNER, LEFT, RIGHT, FULL
• Subqueries
• Views
• Window Functions (Basics)
• SQL Case Studies

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🔹 Module 5: Data Visualization with Python

• Python with MySQL
• Importance of Data Visualization
• Visualization Best Practices
• Matplotlib
  • Line, Bar, Pie, Histogram, Box Plot
• Seaborn
  • Advanced Statistical Plots
• Customizing Charts
• Data Storytelling Concepts

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🔹 Module 6: Introduction to Data Analytics

• What is Data Analytics?
• Role & Responsibilities of a Data Analyst
• Types of Data
  • Structured, Semi-Structured, Unstructured
• Data Analyst vs Data Scientist vs Business Analyst
• Data Analytics Lifecycle
• Real-World Use Cases
• Career Path & Job Opportunities

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🔹 Module 7: Statistics & Business Mathematics

• Types of Data (Categorical, Numerical)
• Measures of Central Tendency
  • Mean, Median, Mode
• Measures of Dispersion
  • Range, Variance, Standard Deviation
• Probability Basics
• Data Distribution
  • Normal Distribution
• Correlation & Covariance
• Sampling Techniques
• Introduction to Hypothesis Testing
• Business Metrics & KPIs

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🔹 Module 8: MS Excel for Data Analysis

• Excel Interface & Shortcuts
• Data Cleaning Techniques
• Sorting & Filtering
• Conditional Formatting
• Important Excel Functions
  • SUM, AVERAGE, COUNT, IF
  • VLOOKUP, HLOOKUP, XLOOKUP
  • INDEX & MATCH
• Text & Date Functions
• Pivot Tables & Pivot Charts
• Data Validation
• Excel Dashboards
• Real-World Excel Case Studies

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🔹 Module 9: Exploratory Data Analysis (EDA)

• What is EDA?
• Univariate Analysis
• Bivariate Analysis
• Multivariate Analysis
• Outlier Detection
• Correlation Analysis
• EDA Case Studies using Python

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🔹 Module 10: Power BI / Tableau

• Introduction to BI Tools
• Connecting Data Sources
• Data Cleaning using Power Query
• Data Modeling
• Calculated Columns & Measures
• Basic DAX Concepts
• Creating Interactive Dashboards
• Publishing & Sharing Reports
• Power BI / Tableau Projects
  (Tableau Public for practice)

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🔹 Module 11: Business & Domain Knowledge

• Understanding Business Problems
• Data-Driven Decision Making
• KPI & Metrics Analysis
• Domain Knowledge
  • Sales
  • Marketing
  • Finance
  • HR
  • E-Commerce
• Stakeholder Communication
• Reporting & Insights

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🔹 Module 12: Capstone Projects

• Sales Performance Analysis
• Customer Behavior Analysis
• HR Analytics Dashboard
• E-Commerce Data Analysis
• End-to-End Analytics Project

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🔹 Module 13: Interview Preparation & Career Skills

• Data Analyst Interview Questions
• SQL Query Practice
• Python & Excel Interview Tasks
• Case Study Questions
• Resume Building
• Portfolio (GitHub & Dashboards)
• Mock Interviews

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