The goal of teaching statistics is not to focus on mathematics but to develop a mindset and understanding for effective data analysis.

• Basics for Statistics
  • Types of Statistics
  • Population and Sample Data
  • Types od Sampling Techniques
  • Types of Data
  • Scale of Measurement of Data
• Descriptive Statistics
  • Measures of Central Tendency (Mean, Median, Mode)
  • Measures of Dispersion (Variance, Standard Deviation)
  • Why Sample Variance is divided by n-1
  • Random Variables
  • 5 Number Summary
  • Histogram And Skewness
  • Covariance And Correlation
• Probability Theory
  • Basic Probability Concepts - PMF and PDF
  • Types Probability Distributions
  • Bernoulli Distribution
  • Binomial Distribution
  • Poisson Distribution
  • Normal or Gaussian Distribution
  • Standard Normal Distribution
  • Log Normal Distribution
  • 10-Power Law Distribution
  • 11-Pareto Distribution
  • Central Limit Theorem
• Inferential Statistics
  • Hypothesis Testing And Mechanism
  • P value And Hypothesis Testing

SQL is crucial for a data analyst job as it enables efficient data extraction, manipulation, and analysis from large databases, a core requirement in data-driven decision-making.

• Introduction to SQL
  • Basics of Databases and SQL
  • Types of SQL Commands (DDL, DML, DCL)
• Data Querying
  • SELECT Statements, WHERE Clauses, and Aliases
  • Filtering Data with Conditions
  • Sorting and Ordering Results
• Joins and Subqueries
  • INNER, LEFT, RIGHT, FULL Joins
  • Nested Queries and Correlated Subqueries
• Data Aggregation
  • GROUP BY and HAVING Clauses
  • Aggregate Functions (SUM, AVG, COUNT, etc.)
• Data Manipulation
  • INSERT, UPDATE, DELETE
  • Managing Transactions and Rollbacks
• Database Design and Optimization
  • Indexing and Views
  • Normalization and Relationships
  • Query Optimization Techniques

Python is essential for data analysis jobs as it is the industry standard, widely used for data manipulation, visualization, and machine learning, making it a key skill for data-driven careers.

• Python Fundamentals
  • Variables, Data Types (Numbers, Strings, Lists, Tuples, Sets, Dictionaries).
  • Conditional Statements (if, if-else, if-elif-else).
  • Loops (For Loop, While Loop).
  • Functions (Built-in & User-defined).
  • File Handling (Reading & Writing CSV, Excel, JSON).
  • Exception Handling and Debugging Techniques.
  • Object-Oriented Programming (Classes, Objects, Inheritance, Encapsulation).
• NumPy - Numerical Computing
  • Creating and manipulating NumPy arrays.
  • Indexing, Slicing, and Iterating over arrays.
  • Array operations: Mathematical, Statistical, and Logical functions.
  • Random Variables
  • Reshaping and Resizing arrays.
• Pandas - Data Manipulation
  • Introduction to Pandas and its role in data analysis.
  • Creating and working with DataFrames and Series.
  • Data Cleaning: Handling missing values, duplicates, and outliers.
  • Data Transformation: Filtering, Sorting, and Grouping.
  • Merging and Joining datasets.
• Matplotlib & Seaborn - Data Visualization
  • Introduction to Matplotlib: Line plots, Bar plots, Histograms, Scatter plots.
  • Customizing plots (Titles, Labels, Legends, Grid, Annotations).
  • Introduction to Seaborn: Advanced visualizations.
  • Creating Heatmaps, Pairplots, Boxplots, Violin plots.

• Introduction to Power BI
  • Overview of Power BI Desktop and Service
  • Installing Power BI Desktop
  • Interface and Navigation
• Connecting to Data Sources
  • Importing Data
  • Connecting to Databases and Online Services
  • Importing from Web/API
• Data Preparation with Power Query
  • Data Cleaning and Transformation
  • Merge and Append Queries
  • Advanced Editor and M Code Basics
• Data Modeling
  • Understanding Relationships
  • Creating Measures with DAX
  • Common DAX Functions
• Data Visualization
  • Visual Elements (Tables, Charts, Maps)
  • Using Filters, Slicers, and Drill-through
  • Building Interactive Dashboards
• Sharing and Publishing
  • Publishing to Power BI Service
  • Sharing Reports and Dashboards
  • Setting up Gateways for Real-time Data

The goal of the Excel module is to empower learners to design and develop dynamic, visually appealing, and interactive dashboards in Excel, enhancing data presentation and analysis capabilities.

• Introduction to Excel
  • Interface Overview
  • Data Entry and Shortcuts
• Formulas and Functions
  • Arithmetic and Logical Functions
  • Lookup Functions (VLOOKUP, HLOOKUP)
  • Text and Date Functions
• Data Analysis
  • Sorting and Filtering
  • Pivot Tables and Pivot Charts
• Advanced Features
  • Conditional Formatting
  • Data Validation

• Retail: Sales Analysis Dashboard to visualize revenue trends and identify top-performing products.
• Finance: Budgeting and Expense Tracker with advanced DAX and forecasting.
• HR: Employee Attrition Analysis to identify patterns in workforce turnover.
• Healthcare: Patient Records Dashboard to monitor critical health KPIs.
• Energy: Power Consumption Dashboard with peak-load analysis using IoT datasets.
• Education: Student Performance Analysis to track academic outcomes.