Course Content

Topics Covered:

• What is Data Analytics? - Definition and Scope
• Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
• The Data Analytics Lifecycle
• Role of a Data Analyst in Organizations
• Industry Applications: BFSI, Healthcare, Retail, IT
• Career Paths in Data Analytics

Topics Covered:

• Structured vs Unstructured vs Semi-structured Data
• Data Types: Numerical (Discrete, Continuous)
• Data Types: Categorical (Nominal, Ordinal)
• Date/Time Data Handling
• Data Quality Dimensions: Accuracy, Completeness, Consistency
• Common Data Formats: CSV, Excel, JSON, XML

Topics Covered:

• The Art of Asking the Right Questions
• SMART Framework for Business Questions
• Translating Business Problems to Analytical Questions
• Stakeholder Requirements Gathering
• Scope Definition and Constraints
• Hypothesis Formation

Topics Covered:

• What are KPIs? Metrics vs KPIs
• Types of KPIs: Leading vs Lagging Indicators
• Industry-specific KPIs:
  • Sales: Revenue, Conversion Rate, AOV, CAC, LTV
  • Marketing: CTR, CPC, ROAS, Engagement Rate
  • Operations: Efficiency, Utilization, Defect Rate
  • Finance: ROI, Profit Margin, Cash Flow
• Setting KPI Targets and Benchmarks
• KPI Dashboards: What to Include

Topics Covered:

• Excel Interface & Navigation for Analysts
• Data Entry Best Practices
• Essential Functions:
  • SUM, AVERAGE, COUNT, COUNTA, COUNTBLANK
  • MIN, MAX, MEDIAN, MODE
  • IF, AND, OR, NOT
  • TEXT, LEFT, RIGHT, MID, CONCATENATE
• Cell References: Relative, Absolute, Mixed
• Named Ranges and Tables
• Data Validation Rules

Topics Covered:

• Lookup Functions:
  • VLOOKUP & HLOOKUP
  • INDEX-MATCH (Why it's better)
  • XLOOKUP (Modern approach)
• Conditional Aggregations:
  • SUMIF, SUMIFS
  • COUNTIF, COUNTIFS
  • AVERAGEIF, AVERAGEIFS
• Pivot Tables:
  • Creating and Formatting Pivot Tables
  • Grouping Data
  • Calculated Fields
  • Pivot Charts
• Sorting, Filtering & Conditional Formatting

Project Brief:

Topics Covered:

• What is a Database? Types of Databases
• Relational Database Management Systems (RDBMS)
• Tables, Rows, Columns, and Primary Keys
• Entity-Relationship (ER) Diagrams
• Introduction to MySQL/PostgreSQL
• Setting up your SQL environment

Topics Covered:

• SELECT statement syntax
• Selecting specific columns vs SELECT *
• Column aliases with AS
• DISTINCT for unique values
• LIMIT and OFFSET for pagination
• Comments in SQL

Topics Covered:

• WHERE clause for filtering
• Comparison operators: =, <>, <, >, <=, >=
• Logical operators: AND, OR, NOT
• BETWEEN for range filtering
• IN for multiple value matching
• LIKE and wildcards (%, _) for pattern matching
• IS NULL and IS NOT NULL

Topics Covered:

• ORDER BY for sorting (ASC, DESC)
• Sorting by multiple columns
• Aggregate functions: COUNT, SUM, AVG, MIN, MAX
• GROUP BY for grouping data
• HAVING clause for filtering groups
• Difference between WHERE and HAVING

Topics Covered:

• String functions: UPPER, LOWER, CONCAT, SUBSTRING, LENGTH, TRIM
• Numeric functions: ROUND, CEIL, FLOOR, ABS, MOD
• Date functions: NOW, CURDATE, DATE_FORMAT, DATEDIFF, DATE_ADD
• CASE WHEN for conditional logic
• COALESCE and IFNULL for handling NULLs
• Type conversion: CAST and CONVERT

Topics Covered:

• Why JOINs? Combining related tables
• INNER JOIN - matching records only
• LEFT JOIN - all from left table
• RIGHT JOIN - all from right table
• FULL OUTER JOIN - all records
• Table aliases for readability
• Joining on multiple conditions

Project Brief:

Topics Covered:

• What are Subqueries? Types and Use Cases
• Scalar Subqueries (Single Value)
• Column Subqueries (List of Values)
• Table Subqueries (Derived Tables)
• Subqueries in SELECT, WHERE, FROM clauses
• EXISTS and NOT EXISTS operators

Topics Covered:

• Understanding Correlated Subqueries
• Difference from Regular Subqueries
• Row-by-Row Processing Concept
• Using IN, ANY, ALL with Subqueries
• Performance Considerations
• When to Use vs When to Avoid

Topics Covered:

• Introduction to Window Functions
• OVER() clause and PARTITION BY
• Ranking Functions: ROW_NUMBER, RANK, DENSE_RANK
• NTILE for percentile grouping
• ORDER BY within windows
• Difference from GROUP BY

Topics Covered:

• Aggregate Window Functions: SUM, AVG, COUNT OVER()
• Running Totals and Moving Averages
• LAG and LEAD for accessing adjacent rows
• FIRST_VALUE and LAST_VALUE
• Frame Specifications: ROWS vs RANGE
• UNBOUNDED PRECEDING/FOLLOWING

Topics Covered:

• What are CTEs? WITH clause syntax
• Benefits: Readability and Reusability
• Multiple CTEs in a single query
• Recursive CTEs for hierarchical data
• CTEs vs Subqueries vs Temp Tables
• Best Practices for CTE Usage

Topics Covered:

• Understanding Query Execution Plans
• Introduction to Indexing
• EXPLAIN and ANALYZE statements
• Common Performance Pitfalls
• Writing Efficient WHERE Clauses
• Best Practices for Production Queries

Project Brief:

Topics Covered:

• Why Statistics for Data Analysts?
• Mean: Arithmetic, Weighted, Trimmed
• Median: Finding and Interpreting
• Mode: Single and Multi-modal Data
• When to Use Each Measure
• Effect of Outliers on Central Tendency

Topics Covered:

• Range and Interquartile Range (IQR)
• Variance: Population vs Sample
• Standard Deviation and Its Meaning
• Coefficient of Variation
• Percentiles and Quartiles
• Five-Number Summary and Box Plots

Topics Covered:

• Frequency Distributions and Histograms
• Skewness: Left, Right, Symmetric
• Kurtosis: Peaks and Tails
• Normal Distribution Properties
• Empirical Rule (68-95-99.7)
• Z-Scores and Standardization

Topics Covered:

• Basic Probability Concepts
• Types: Classical, Empirical, Subjective
• Addition Rule (OR)
• Multiplication Rule (AND)
• Conditional Probability
• Bayes' Theorem Introduction

Topics Covered:

• Discrete vs Continuous Distributions
• Binomial Distribution
• Poisson Distribution
• Normal Distribution Deep Dive
• Exponential Distribution
• When to Use Each Distribution

Topics Covered:

• Population vs Sample
• Sampling Methods: Random, Stratified, Cluster
• Sampling Distribution of the Mean
• Central Limit Theorem
• Standard Error
• Confidence Intervals Introduction

Project Brief:

Topics Covered:

• What is Hypothesis Testing?
• Null and Alternative Hypotheses
• Type I and Type II Errors
• P-values and Significance Levels
• One-tailed vs Two-tailed Tests
• Steps in Hypothesis Testing

Topics Covered:

• Z-Test for Large Samples
• T-Test: One Sample, Two Sample, Paired
• Chi-Square Test for Independence
• ANOVA: Analysis of Variance
• Choosing the Right Test
• Assumptions and Limitations

Topics Covered:

• Correlation vs Causation
• Pearson Correlation Coefficient
• Spearman Rank Correlation
• Scatter Plots and Trend Lines
• Correlation Matrix
• Introduction to Linear Regression

Topics Covered:

• EDA Process and Methodology
• Univariate Analysis Techniques
• Bivariate Analysis Techniques
• Multivariate Analysis Overview
• Handling Missing Data
• Dealing with Outliers

Topics Covered:

• Introduction to A/B Testing
• Designing Experiments
• Sample Size Calculations
• Statistical Significance in A/B Tests
• Common Pitfalls to Avoid
• Interpreting A/B Test Results

Topics Covered:

• Choosing the Right Chart Type
• Effective Chart Design Principles
• Common Visualization Mistakes
• Telling Stories with Data
• Dashboard Design Basics
• Tools: Excel Charts, Python Matplotlib

Project Brief:

Topics Covered:

• Why R for Data Analysis?
• Installing R and RStudio
• RStudio Interface Tour
• R as a Calculator
• Variables and Assignment
• Getting Help in R

Topics Covered:

• Atomic Types: numeric, character, logical
• Vectors: Creating and Indexing
• Lists: Heterogeneous Collections
• Matrices and Arrays
• Data Frames: The Core Structure
• Factors for Categorical Data

Topics Covered:

• Conditional Statements: if, else, ifelse
• Loops: for, while, repeat
• Apply Family: lapply, sapply, mapply
• Writing Custom Functions
• Function Arguments and Defaults
• Scope and Environments

Topics Covered:

• Reading CSV, Excel, and Databases
• readr Package for Fast Imports
• Introduction to tidyverse Philosophy
• The Pipe Operator %>%
• Tibbles vs Data Frames
• Data Export Options

Topics Covered:

• select() - Choose Columns
• filter() - Subset Rows
• mutate() - Create/Modify Columns
• arrange() - Sort Data
• summarize() - Aggregate Data
• group_by() - Grouped Operations

Topics Covered:

• Tidy Data Principles
• pivot_longer() - Wide to Long
• pivot_wider() - Long to Wide
• separate() and unite()
• Handling Missing Values
• Joining Tables: left_join, inner_join

Project Brief:

Topics Covered:

• Grammar of Graphics Philosophy
• ggplot() and aes() Basics
• Geometric Objects (geoms)
• Bar Charts, Line Charts, Scatter Plots
• Histograms and Box Plots
• Adding Titles, Labels, Legends

Topics Covered:

• Faceting: facet_wrap, facet_grid
• Themes and Customization
• Color Palettes and Scales
• Annotations and Text
• Combining Multiple Plots
• Saving Plots: ggsave()

Topics Covered:

• String Basics in R
• stringr Functions Overview
• Pattern Matching: str_detect, str_extract
• String Manipulation: str_replace, str_split
• Regular Expressions Basics
• Cleaning Text Data

Topics Covered:

• Date/Time Classes in R
• Parsing Dates with lubridate
• Extracting Components: year, month, day
• Date Arithmetic
• Time Zones and Durations
• Time Series Data Basics

Topics Covered:

• across() for Multiple Columns
• Window Functions in dplyr
• Conditional Mutations: case_when
• Working with Nested Data
• purrr for Functional Programming
• Error Handling in R

Topics Covered:

• Project Organization
• R Scripts vs R Markdown
• Code Style Guidelines
• Documentation and Comments
• Debugging Techniques
• Introduction to R Packages

Project Brief:

Topics Covered:

• What is R Shiny? Why It Matters
• Shiny vs Traditional BI Tools
• Shiny App Structure: ui.R and server.R
• Your First Shiny App
• Running and Debugging Apps
• Shiny App Templates

Topics Covered:

• Input Widgets: selectInput, sliderInput, textInput
• Output Elements: plotOutput, tableOutput, textOutput
• Layout Functions: fluidPage, sidebarLayout
• Tabs and Panels: tabsetPanel, navbarPage
• Conditional UI Elements
• HTML and CSS in Shiny

Topics Covered:

• Understanding Reactivity Model
• Reactive Sources and Endpoints
• reactive() Expressions
• observe() and observeEvent()
• reactiveValues() for State
• Isolate and Controlling Reactivity

Topics Covered:

• Rendering ggplot2 in Shiny
• Interactive Plots with plotly
• Dynamic Plot Updates
• Click and Brush Events
• Interactive Tables with DT
• Downloadable Plots and Data

Topics Covered:

• shinydashboard Package
• Dashboard Layout Components
• Value Boxes and Info Boxes
• Sidebar Menus
• bslib for Modern Themes
• Useful Shiny Extensions

Topics Covered:

• Shinyapps.io Deployment
• Preparing Apps for Deployment
• Environment and Dependencies
• App Performance Optimization
• Error Handling in Production
• Sharing and Embedding Apps

Project Brief:

Topics Covered:

• Power BI Ecosystem Overview
• Power BI Desktop vs Service vs Mobile
• When to Use Power BI vs R Shiny
• Interface Navigation
• Your First Power BI Report
• Power BI Community Resources

Topics Covered:

• Data Source Types in Power BI
• Connecting to Excel, CSV, Databases
• Import vs DirectQuery Mode
• Web Data Connections
• Data Refresh Options
• Managing Data Source Settings

Topics Covered:

• Power Query Editor Interface
• Common Transformations
• Filtering and Sorting
• Merging and Appending Queries
• Handling Dates and Text
• Applied Steps and M Language Basics

Topics Covered:

• Star Schema Concepts
• Creating Relationships
• Cardinality and Cross-Filter Direction
• Calculated Columns vs Measures
• Date Tables and Time Intelligence
• Model Optimization

Topics Covered:

• Chart Types and When to Use
• Building Interactive Visuals
• Slicers and Filters
• Formatting and Themes
• Drill-down and Drill-through
• Custom Visuals from Marketplace

Topics Covered:

• What is DAX?
• Basic DAX Functions: SUM, AVERAGE, COUNT
• CALCULATE and Filter Context
• Time Intelligence Functions
• RELATED and RELATEDTABLE
• Best Practices for DAX

Project Brief:

Topics Covered:

• Tableau Product Family
• Tableau Public vs Desktop vs Server
• Interface Overview
• Connecting to Data
• Dimensions vs Measures
• First Visualization

Topics Covered:

• Bar Charts and Column Charts
• Line Charts and Area Charts
• Scatter Plots
• Maps and Geographic Data
• Marks Card Controls
• Show Me Panel

Topics Covered:

• Dual Axis Charts
• Combination Charts
• Tree Maps and Heat Maps
• Box Plots and Histograms
• Reference Lines and Bands
• Trend Lines

Topics Covered:

• Creating Calculated Fields
• String and Date Functions
• Logical Functions (IF, CASE)
• Aggregation Functions
• Table Calculations Basics
• Parameters for Interactivity

Topics Covered:

• Dashboard Canvas Layout
• Adding Worksheets to Dashboards
• Filters and Filter Actions
• Highlight Actions
• URL Actions
• Dashboard Best Practices

Topics Covered:

• Tableau Public Publishing
• Embedding Visualizations
• Data Extracts
• Performance Optimization
• Story Points
• Tableau vs Power BI vs R Shiny Comparison

Project Brief:

Topics Covered:

• Capstone Project Options Overview
• Choosing Your Domain (Retail, Finance, Healthcare, etc.)
• Defining Business Problem Statement
• Setting Project Scope and Objectives
• Creating Project Timeline
• Success Criteria Definition

Topics Covered:

• Public Dataset Sources (Kaggle, UCI, Government)
• API Data Collection
• Web Scraping Basics (if needed)
• Data Quality Assessment
• Data Documentation
• Ethical Data Usage

Topics Covered:

• Data Cleaning Pipeline
• Database Design for Project
• Loading Data into SQL Database
• Writing Analytical Queries
• Creating Views for Dashboards
• Performance Optimization

Topics Covered:

• Comprehensive EDA on Project Data
• Statistical Testing for Hypotheses
• Feature Engineering
• Correlation Analysis
• Key Insights Identification
• Documentation of Findings

Topics Covered:

• Dashboard Design Planning
• Wireframing Your App
• Setting Up App Structure
• Building UI Framework
• Implementing Core Visualizations
• Adding Interactive Filters

Topics Covered:

• Advanced Interactivity
• Multi-Page Navigation
• KPI Boxes and Summary Stats
• Error Handling
• Performance Optimization
• User Experience Polish

Checkpoint Review:

Topics Covered:

• Final App Testing and QA
• Bug Fixes and Polish
• Deployment to shinyapps.io
• Custom Domain Setup (Optional)
• Performance Monitoring
• App Maintenance Considerations

Topics Covered:

• Writing Professional README Files
• Code Documentation Standards
• GitHub Repository Best Practices
• Creating a GitHub Profile README
• Organizing Your Portfolio
• Showcasing Project Highlights

Topics Covered:

• Data Analyst Resume Format
• Highlighting Technical Skills
• Quantifying Achievements
• ATS-Friendly Resume Tips
• LinkedIn Profile Optimization
• Building Your Personal Brand

Topics Covered:

• Common SQL Interview Questions
• Statistics Interview Questions
• R/Python Coding Challenges
• Case Study Approach
• Explaining Your Projects
• Technical Problem-Solving

Topics Covered:

• STAR Method for Answers
• Common Behavioral Questions
• Discussing Your Projects
• Handling "Tell Me About Yourself"
• Questions to Ask Interviewers
• Salary Negotiation Basics

Topics Covered:

• Structuring Your Presentation
• Storytelling with Data
• Executive Summary Creation
• Demo Walkthrough Best Practices
• Handling Q&A
• Presentation Delivery Tips

Graduation Day: