The Data Analysis Bundle is a comprehensive collection of courses designed to equip individuals with the skills and knowledge necessary for a successful career in data analysis. From foundational concepts to advanced techniques and tools, this bundle covers a wide range of topics in the field of data analysis. Whether you're a beginner looking to get started or a professional seeking to enhance your skills, these courses provide a well-rounded education in data analysis.

**Diploma in Data Analytics with Tableau**

• Introduction To The Course
• Project 1: Discount Mart (Sales And Profit Analytics)
• Project 2: Green Destinations (HR Analytics)
• Project 3: Superstore (Sales Agent Tracker)
• Northwind Trade (Shipping Analytics)
• Project 5: Tesla (Stock Price Analytics)
• Bonus: Introduction To Database Concepts
• Tableau Stories

**Data Analytics**

• Introduction To The World Of Data
• Basics Of Data Analytics
• Statistics For Data Analytics
• Actions Taken In The Data Analysis Process
• Gathering The Right Information
• Storing Data
• Data Mining
• Excel For Data Analytics
• Tools For Data Analytics
• Data-Analytic Thinking
• Data Visualisation That Clearly Describes Insights
• Data Visualisation Tools

**Google Data Studio: Data Analytics**

• Introduction
• Google Sheets
• Google Data Studio

**Big Data Analytics with PySpark Tableau Desktop and MongoDB**

• Introduction
• Setup And Installations
• Data Processing With PySpark And MongoDB
• Machine Learning With PySpark And MLlib
• Creating The Data Pipeline Scripts
• Tableau Data Visualization
• Source Code

**Learn MySQL from Scratch for Data Science and Analytics**

• Getting Started
• SQL Server Setting Up
• SQL Database Basics
• SQL DML (Data Manipulation Language)
• SQL DDL (Data Definition Language)
• SQL DCL (Data Control Language)
• SQL Statement Basic
• Filtering Data Rows
• Aggregate Functions For Data Analysis
• SQL Data Analyticstatements
• SQL Group By Statement
• JOINS
• SQL Constraints
• Views
• Advanced SQL Functions
• SQL Stored Procedures
• Import & Export Data
• Backup And Restore Database

**Data Analysis In Excel**

• Modifying A Worksheet
• Working With Lists
• Analyzing Data
• Visualizing Data With Charts
• Using PivotTables And PivotCharts
• Working With Multiple Worksheets And Workbooks
• Using Lookup Functions And Formula Auditing
• Automating Workbook Functionality
• Creating Sparklines And Mapping Data
• Forecasting Data

**Diploma in Data Analysis Fundamentals**

• Introduction
• Agenda And Principles Of Process Management
• The Voice Of The Process
• Working As One Team For Improvement
• Exercise: The Voice Of The Customer
• Tools For Data Analysis
• The Pareto Chart
• The Histogram
• The Run Chart
• Exercise: Presenting Performance Data
• Understanding Variation
• The Control Chart
• Control Chart Example
• Control Chart Special Cases
• Interpreting The Control Chart
• Control Chart Exercise
• Strategies To Deal With Variation
• Using Data To Drive Improvement
• A Structure For Performance Measurement
• Data Analysis Exercise
• Course Project
• Test Your Understanding

**Business Data Analysis**

• Introduction To Business Analysis
• Business Environment
• Business Processes
• Business Analysis Planning And Monitoring
• Strategic Analysis And Product Scope
• Solution Evaluation
• Investigation Techniques
• Ratio Analysis
• Stakeholder Analysis And Management
• Process Improvement With Gap Analysis
• Documenting And Managing Requirements
• Business Development And Succession Planning
• Planning & Forecasting Operations
• Business Writing Skills

**Data Science & Machine Learning with Python**

• Course Overview & Table Of Contents
• Introduction To Machine Learning
• System And Environment Preparation
• Learn Basics Of Python - Assignment
  
  • Functions
  • Data Structures
  • NumPy Array
  • NumPy Data
  • NumPy Arithmetic
  • Learn Basics Of Matplotlib
• Learn Basics Of Pandas
• Understanding The CSV Data File
• Load And Read CSV Data File Using Python Standard Library
  
  • Using NumPy
  • Using Pandas
• Dataset Summary - Peek, Dimensions And Data Types
  
  • Class Distribution And Data Summary
  • Explaining Correlation
  • Explaining Skewness - Gaussian And Normal Curve
• Dataset Visualization - Using Histograms
• Dataset Visualization - Using Density Plots
• Dataset Visualization - Box And Whisker Plots
• Multivariate Dataset Visualization - Correlation Plots
• Multivariate Dataset Visualization - Scatter Plots
• Data Preparation (Pre-Processing) - Introduction
  
  • Re-Scaling Data
  • Standardizing Data
  • Normalizing Data
  • Binarizing Data
• Feature Selection - Introduction
  
  • Uni-Variate Part
  • Recursive Feature Elimination
  • Principal Component Analysis (PCA)
  • Feature Importance
• Refresher Session - The Mechanism Of Re-Sampling, Training And Testing
• Algorithm Evaluation Techniques - Introduction
  
  • Train And Test Set
  • K-Fold Cross Validation
  • Leave One Out Cross Validation
  • Repeated Random Test-Train Splits
• Algorithm Evaluation Metrics - Introduction
  
  • Classification Accuracy
  • Log Loss
  • Area Under ROC Curve
  • Confusion Matrix
  • Classification Report
  • Mean Absolute Error - Dataset Introduction
  • Mean Absolute Error
  • Mean Square Error
  • R Squared
• Classification Algorithm Spot Check - Logistic Regression
  
  • Linear Discriminant Analysis
  • K-Nearest Neighbors
  • Naive Bayes
  • CART
  • Support Vector Machines
• Regression Algorithm Spot Check - Linear Regression
  
  • Ridge Regression
  • Lasso Linear Regression
  • Elastic Net Regression
  • K-Nearest Neighbors
  • CART
  • Support Vector Machines (SVM)
• Compare Algorithms
• Pipelines
• Performance Improvement: Ensembles - Voting
  
  • Bagging
  • Boosting
• Performance Improvement: Parameter Tuning Using Grid Search
• Performance Improvement: Parameter Tuning Using Random Search
• Export, Save And Load Machine Learning Models : Pickle
• Export, Save And Load Machine Learning Models : Joblib
• Finalizing A Model - Introduction And Steps
• Finalizing A Classification Model - The Pima Indian Diabetes Dataset
• Quick Session: Imbalanced Data Set - Issue Overview And Steps
• Iris Dataset : Finalizing Multi-Class Dataset
• Finalizing A Regression Model - The Boston Housing Price Dataset
• Real-Time Predictions: Using The Pima Indian Diabetes Classification Model
• Real-Time Predictions: Using Iris Flowers Multi-Class Classification Dataset
• Real-Time Predictions: Using The Boston Housing Regression Model

**R Programming for Data Science**

• Data Science Overview
• R And RStudio
• Introduction To Basics
• Vectors
• Matrices
• Factors
• Data Frames
• Lists
• Relational Operators
• Logical Operators
• Conditional Statements
• Loops
• Functions
• R Packages
• The Apply Family - Lapply
• The Apply Family – Sapply & Vapply
• Useful Functions
• Regular Expressions
• Dates And Times
• Getting And Cleaning Data
• Plotting Data In R
• Data Manipulation With Dplyr

**Learn Python, JavaScript, and Microsoft SQL for Data science**

• JavaScript Introduction
• JavaScript Basics
• JavaScript Operators
• JavaScript Conditional Statements
• JavaScript Control Flow Statements
• JavaScript Functions
• JavaScript Error Handling
• JavaScript Client-Side Validations
• Python Introduction
• Python Basic
• Python Strings
• Python Operators
• Python Data Structures
• Python Conditional Statements
• Python Control Flow Statements
• Python Core Games
• Python Functions
• Python Args, KW Args For Data Science
• Python Project
• Python Object Oriented Programming [OOPs]
• Python Methods
• Python Class And Objects
• Python Inheritance And Polymorphism
• Python Encapsulation And Abstraction
• Python OOPs Games
• Python Modules And Packages
• Python Error Handling
• Microsoft SQL[MS] Introduction
• MS SQL Statements
• MS SQL Filtering Data
• MS SQL Functions
• MS SQL Joins
• MS SQL Advanced Commands
• MS SQL Structure And Keys
• MS SQL Queries
• MS SQL Structure Queries
• MS SQL Constraints
• MS SQL Backup And Restore