Introduction 0.0 Introduction Lesson 1: Introduction to Business Analytics 1.01 Introduction to Business Analytics Knowledge Check Lesson 2: Data Cleaning and Preparation 2.01 Data Cleaning and Preparation Knowledge Check Lesson 3: Conditional Formatting and Important Functions 3.01 Conditional Formatting and Important Functions Knowledge Check Lesson 4: Analyzing Data with Pivot Tables 4.01 Analyzing Data with Pivot Tables Knowledge Check Lesson 5: Dashboarding 5.01 Dashboarding Knowledge Check Lesson 6: Analytics with Excel 6.01 Analytics with Excel Knowledge Check Lesson 7: Data Analysis using Statistics 7.01 Data Analysis using Statistics Knowledge Check Lesson 8: Macros for Analytics 8.01 Macros for Analytics Knowledge Check

Lesson 01: Course Introduction

1.01 Course Introduction

Lesson 02: Introduction to SQL

2.01 Introduction

2.02 Introduction to Databases

2.03 Introduction to Database Management System

2.04 DBMS vs. RDBMS

2.05 Introduction to MySQL

2.06 Tables in My SQL

2.07 Relationships in MySQL

2.08 Views in My SQL

2.09 Table vs. Views

2.10 Quick Recap

Lesson 03: Database Normalization and Entity Relationship (ER) Model

3.01 Introduction

3.02 Entity Relationship Model

3.03 Attributes

3.04 Relationship Set and Degree

3.05 Types Of Relationship

3.06 Mapping Cardinalities

3.07 Database Normalization

3.08 Types of Anomalies

3.09 Types of Normalization

3.10 Types of Normalization: One NF, Two NF, and Three NF

3.11 Types of Normalization: BCNF, Four NF, and Five NF

3.12 Recap

Lesson 04: MySQL - Installation and SetUp

4.01 Introduction

4.02 Downloading MySQL Community Setup

4.03 Installing MySQL Community

4.04 Configuring MySQL Community and Workbench

4.05 Connecting to MySQL Server

4.06 Downloading Sample MySQL Database in MySQL Workbench

4.07 Recap

Lesson 05: Working with Database and Tables

5.01 Introduction

5.02 Database Manipulation in MySQL

5.03 Transactions and ACID Properties in MySQL

5.04 MySQL Storage Engines

5.05 Creating and Managing Tables in MySQL

5.06 Creating and Managing Tables in MySQL: CREATE, DESCRIBE, and SHOW Table

5.07 Creating and Managing Tables in My SQL: ALTER, TRUNCATE, and DROP Tables

5.08 Inserting and Querying Data in Tables

5.09 Filtering Data From Tables in MySQL

5.10 Filtering Data From Tables in MySQL: WHERE and DISTINCT Clauses

5.11 Filtering Data From Tables in MySQL: AND and OR Operators

5.12 Filtering Data From Tables in MySQL: IN and NOT IN Operators

5.13 Filtering Data From Tables in MySQL: BETWEEN and LIKE Operators

5.14 Filtering Data From Tables in MySQL: LIMIT, IS NULL, and IS NOT NULL Operators

5.15 Sorting Table Data

5.16 Grouping Table Data and Roll Up in MySQL

5.17 Comments in MySQL

5.18 Recap

5.19 Spotlight

Lesson 06: Working with Operators Constraints and Data Types

6.01 Introduction

6.02 Operators in MySQL

6.03 Indexing in MySQL

6.04 Order of Execution in MySQL

6.05 Assisted Practice Constraint

6.06 Data Types in MySQL

6.07 Recap

Lesson 07: Functions in SQL

7.01 Introduction

7.02 Understanding SQL Functions

7.03 Aggregate Functions

7.04 Scalar Functions

7.05 String Functions

7.06 Numeric Functions

7.07 Date and Time Functions

7.08 Handling Duplicate Record

7.09 Miscellaneous Functions

7.10 General Functions

7.11 Recap

7.12 Spotlight

Lesson 08: Subqueries Operators and Derived Tables in SQL

8.01 Introduction

8.02 Introduction to Alias

8.03 Introduction to JOINS

8.04 Right Cross and Self Join

8.05 Operators in MySQL

8.06 Intersect and Emulation

8.07 Minus and Emulation

8.08 Subquery in SQL

8.09 Subqueries with Statements and Operators

8.10 Subqueries with Commands

8.11 Derived Tables in SQL

8.12 EXISTS Operator

8.13 NOT EXISTS Operator

8.14 EXISTS vs. IN Operators

8.15 Recap

Lesson 09: Windows Functions in SQL

9.01 Introduction

9.02 Introduction to Window Function

9.03 Window Function Syntax

9.04 Aggregate Window Functions

9.05 Ranking Window Functions

9.06 Miscellaneous Window Functions

9.07 Miscellaneous Window Functions: FIRST VALUE, NTH VALUE, and NTILE

9.08 Miscellaneous Window Functions: CUME DIST, LEAD, LAG and LAST VALUE

9.09 Recap

9.10 Spotlight

Lesson 10: Working with Views

10.01 Introduction

10.02 SQL Views and Manipulation Methods

10.03 Altering and Renaming Views

10.04 View Processing Algorithms

10.05 Updatable Views

10.06 Creating Views Using With Check Option Local

10.07 Creating Views Using With Cascaded Check Option

10.08 Creating Views Using With Local Check Option

10.09 Recap

Lesson 11: Stored Procedures and Triggers in SQL

11.01 Introduction

11.02 Introduction to Stored Procedures

11.03 Advantages of Stored Procedures

11.04 Working With Stored Procedures

11.05 Compound Statements

11.06 Conditional Statements

11.07 IF Statement

11.08 IF-THEN Statement

11.09 IF-THEN- ELSE Statement

11.10 IF-THEN-ELSE-IF ELSE Statement

11.11 Case Statement

11.12 Simple Case Statement

11.13 Searched Case Statement

11.14 Loops In Stored Procedures

11.15 Loop Statement

11.16 While Loop

11.17 Repeat Loop

11.18 Leave Statement

11.19 Using Leave with Stored Procedures

11.20 Using Leave with Loop Statement

11.21 Using Leave with While Loop

11.22 Using Leave with Repeat Loop

11.23 Error Handling in Stored Procedures

11.24 Raising Errors in Error Handling

11.25 Cursors in Stored Procedures

11.26 Steps to Use Cursors

11.27 Stored Functions in Stored Procedures

11.28 Stored Program Security

11.29 SQL Trigger

11.30 Recap

11.31 Spotlight

Lesson 12: Performance Optimization and Best Practices in SQL

12.01 Introduction

12.02 Execution Plan in SQL

12.03 Differences Between CHAR VARCHAR and NVARCHAR

12.04 Index Guidelines and Clustered Indexes in MySQL

12.05 Common Table Expression

12.06 MySQL Best Practices

12.07 Recap

Section 01 - Programming Basics and Data Analytics with Python (Self Learning Curriculum) Lesson 1 Learning Objective Course End Objectives Lesson 2 Introduction Learning Objectives Getting Started Analyzing Data in Python Importing and Exporting Data in Python Introduction to Data Analysis with Python Python Packages for Data Science The Problem Understanding the Data Introduction Lesson 3 Data Wrangling Learning Objectives Binning in Python Data Formatting in Python Data Normalization in Python Dealing with Missing Values in Python Indicator variables in Python Pre-processing Data in Python REVIEW DATA WRANGLING Lesson 4 Exploratory Data Analysis Learning Objectives Analysis of Variance (ANOVA) Correlation - Statistics Correlation Descriptive Statistics Exploratory Data Analysis GroupBy in Python REVIEW: EXPLORATORY DATA ANALYSIS Lesson 5 Model Development Learning Objectives Introduction Linear Regression and Multiple Linear Regression Model Evaluation using Visualization Polynomial Regression and Pipelines Measures for In-Sample Evaluation Prediction and Decision Making REVIEW-MODEL DEVELOPMENT Lesson 6 Model Evaluation Learning Objectives Model Evaluation Overfitting Underfitting and Model Selection Grid Search Model Evaluation and Refinement Ridge Regression MODEL EVALUATION AND REFINEMENT Unlocking IBM Certificate Section 02 - Programming Basics and Data Analytics with Python (Live Classes Curriculum) Lesson 01 Course Introduction Course Objectives Course Prerequisites Why Python for Data Analytics? Course Outline Topics Covered Course Features Course-End Project Highlights Learning Outcomes Course Completion Criteria Lesson 02 Introduction to Programming Learning Objectives Program Programming language Algorithm, Pseudo code, and Flowchart Compiler and Interpreter Key Takeaways Lesson 3 Programming Environment Setup Learning Objectives Python Environments for Python Anaconda Installation of Anaconda Python Distribution Jupyter Notebook Assisted Practice: Install Python Assisted Practice: First Python Program Key takeaways Lesson 4 OOPs Concept with Python Learning Objectives Object Oriented Programming Language Objects and classes Methods and attributes Access Modifiers Assisted Practice: Objects and Classes Abstraction Assisted Practice: Abstraction Encapsulation Assisted Practice: Encapsulation Inheritance Assisted Practice: Inheritance Polymorphism Assisted Practice: Polymorphism Key takeaways Lesson 5 Programming Fundamentals of Python Learning Objectives Variables Data Types with Python Assisted Practice: Data Types in Python Keywords and Identifiers Expressions Basic Operators Assisted Practice: Operators in Python Functions Assisted Practice: Search for a Specific Element from a Sorted List Assisted Practice: Create a Banking System Using Functions String Operations Assisted Practice: String Operations in Python Tuples Assisted Practice: Tuples in Python Lists Assisted Practice: Lists in Python Sets Assisted Practice: Sets in Python Dictionaries Assisted Practice: Dictionary in Python Unassisted Practice: Dictionary and its Operations Conditions and Branching Assisted Practices: Check the Scores of a Course While Loop Assisted Practice: Find Even Digit Numbers Unassisted Practice: Fibonacci Series Using While Loop For Loop Assisted Practice: Calculate the Number of Letters and Digits Unassisted Practice: Create a Pyramid of Stars Break and Continue Statements Key takeaways Tic-Tac-Toe Game Lesson 6 File handling, Exception handling, and Package handling Learning Objectives File Handling File Opening and Closing Reading and Writing Files Directories in File Handling Assisted Practice: File Handling Errors and Exceptions Assisted Practice: Exception Handling Modules and Packages Assisted Practice: Package Handling Key Takeaways Student Data Handling Lesson 7 Data Analytics Overview Learning objectives Data Analytics Data Analytics Process Hypothesis Data Visualization Lesson 8 Statistical Computing Learning objectives Statistics Probability Density Function Types of Probability Density Function Central limit theorem Confidence Intervals Hypothesis Testing: Parametric Hypothesis Testing: Nonparametric What is A/B Testing? Case Study: A/B Testing Key takeaways A/B Testing Lesson 9 Mathematical Computing using NumPy Learning objectives NumPy Assisted Practice: Create and Print Numpy Arrays Operations Assisted Practice: Executing Basic Operations in Numpy Array Unassisted Practice: Performing Operations Using Numpy Array Assisted Practice: Demonstrate the Use of Copy and Use Assisted Practice: Manipulate the Shape of an Array Key takeaways Country GDP Olympic 2012 Medal Tally Lesson 10 - Data Manipulation with Pandas Learning Objectives Introduction to Pandas Data Structures Assisted Practice: Create Pandas Series DataFrame Assisted Practice: Create Pandas DataFrames Unassisted Practice: Create Pandas DataFrames Missing Values Assisted Practice: Handle Missing Values Data Operation Assisted Practice: Data Operations in Pandas DataFrame Unassisted Practice: Data Operations in Pandas DataFrame Data Standardization Assisted Practice: Pandas SQL Operations Unassisted Practice: Pandas SQL Operations Key takeaways Analyze the Federal Aviation Authority (FAA) Dataset using Pandas Analyzing the Dataset Lesson 11 - Data visualization with Python Learning objectives Data Visualization Considerations of Data Visualization Factors of Data Visualization Python Libraries Assisted Practice: Create Your First Plot Using Matplotlib Line Properties Assisted Practice: Create a Line Plot for Football Analytics Multiple Plots and Subplots Assisted Practice: Create a Plot with Annotation Unassisted Practice: Create Multiple Plots to Analyze the Skills of the Players Assisted Practice: Create Multiple Subplots Using plt.subplots Types of plots Assisted Practice: Create a Stacked Histogram Assisted Practice: Create a Scatter Plot of Pretest scores and Posttest Scores Assisted Practice: Create a Heat Map to Analyze the Sepal Width, Petal Length, and Petal Width of an Iris Dataset Assisted Practice: Create a Pie Chart Assisted Practice: Create an Error Bar Assisted Practice: Area Chart to Display the Skills of the Players Assisted Practice: Create a Word Cloud of a Random Data Assisted Practice: Create a Bar Chart Assisted Practice: Create Box Plots Assisted Practice: Create a Waffle Chart Seaborn and Regression Plots Introduction to Folium Maps with Markers Kernel Density Estimate Plots Analyzing Variables Individually Key Takeaways Visualize the Sales Data Lesson 12 - Introduction to Model Building Learning objectives Introduction to Machine Learning Machine Learning Approach Scikit-Learn Supervised Learning Models: Linear Regression Assisted Practice: Loading a Dataset Assisted Practice: Linear Regression Model Supervised Learning Models: Logistic Regression Supervised Learning Models: K-Nearest Neighbors Assisted Practice: K-NN and Logistic Regression Models Unsupervised Learning Models: Clustering Assisted Practice: K-Means Clustering to Classify Data Points Unsupervised Learning Models: Dimensionality Reduction Unsupervised Learning Models: Principal Component Analysis Assisted Practice: Principal Component Analysis (PCA) Assisted Practice: Build Pipelines Assisted Practice: Persist a Model for Future Use Key Takeaways Create a Model to Predict the Sales Outcome List the Glucose Level Readings Section 03 - Practice Projects Practice Projects Bike-Sharing Demand Analysis Python for Data Science (Free Course) Lesson 01: Introduction Python for Data Science Data Visualization With Python (Free Course) Lesson 01: Data Visualization with Python Data Visualization with Python

Lesson 01: R Programming for Data Science

1.01 R Programming for Data Science

Lesson 01: Course Introduction

1.01 Course Introduction

1.02 Rstudio Demo - WalkThrough

Lesson 02: Introduction to Data Analytics

2.01 Learning Objectives

2.02 Data Analytics

2.03 Data Analytics Types and Tools

2.04 Careers in Data Analytics

2.05 Data Science vs Data Analytics

2.06 Importance of Statistics in Data Analytics

2.07 Roles and Responsibilities of Data Analyst

2.08 Industrial Use Case and Applications of Analytics

2.09 Recap

Lesson 03: Introduction to R Programming

3.01 Learning Objectives

3.02 Overview and History of R

3.03 Importance of R

3.04 Variables and Operators in R

3.05 Data Types and Structures in R

3.06 Demo Identifying Data Structures

3.07 Vectors

3.08 Matrices

3.09 Arrays, Factors, Dataframes, and Lists

3.10 Names Attributes

3.11 Subsetting in R

3.12 Missing Values

3.13 Vectorized Operations

3.14 Demo Assigning Values and Applying Functions

3.15 Recap

Lesson 04: Programming Fundamentals of R

4.01 Learning Objectives

4.02 Decision Making in R

4.03 Nested ifs

4.04 Multiple Conditions

4.05 Loops in R

4.06 Functions in R

4.07 Scoping

4.08 Packages in R

4.09 Built-in Functions in R

4.10 Apply Family Function

4.11 Dates in R

4.12 R Markdown

4.13 Recap

4.14 Spotlight

Lesson 05: Data Manipulation Using R

5.01 Learning Objectives

5.02 Data Wrangling

5.03 Reading Data in R

5.04 Reading Excel File in R

5.05 Exporting Data in R

5.06 Exporting Data in Text File

5.07 Exporting Data in CSV File

5.08 Exporting Data in Excel File

5.09 Database Connectivity in R

5.10 Attributes of a Dataframe

5.11 Subsetting Dataframes

5.12 Conditional Filtering

5.13 Slicing and Dicing

5.14 Creating New Variables

5.15 Sorting Data

5.16 Summarizing Data

5.17 Aggregate and Summarize

5.18 Merging Data Tables

5.19 Types of Merge

5.20 The dplyr Package

5.21 The Select and Filter Functions

5.22 The Mutate and Arrange Functions

5.23 The Summarise and Group By Functions

5.24 Pipeline Operator

5.25 Recap

Lesson 06: Data Visualization in R

6.01 Learning Objectives

6.02 Data Visualization

6.03 Plots in R

6.04 Bar Chart

6.05 Histogram and Kernel Density Plot

6.06 Box and Whisker Plot

6.07 Scatter Plot

6.08 Line Chart, Heatmap, and Wordcloud

6.09 ggplot For Plotting

6.10 Geometry Functions of ggplot2

6.11 File Formats of Graphics Outputs

6.12 Recap

Lesson 07: Hypothesis Testing

7.01 Learning Objectives

7.02 Hypothesis Test

7.03 Type 1 and Type 2 Error

7.04 Typical Hypothesis Test

7.05 The Audi R-18 e-Tron Quattro Case

7.06 One- Sample Hypothesis Testing

7.07 Two- Sample Hypothesis Testing

7.08 Analysis of Variance

7.09 Non Parametric Test

7.10 Chi Square Test For Independence

7.11 Chi- Square Test For Goodness of Fit

7.12 Recap

7.13 Spotlight

Lesson 08: Introduction to Machine Learning

8.01 Learning Objectives

8.02 Machine Learning

8.03 Types of Machine learning

8.04 Supervised Learning

8.05 Unsupervised Learning

8.06 Regression

8.07 Simple Linear Regression

8.08 Demo Simple Linear Regression

8.09 How Good Is Regression

8.10 Multiple Linear Regression

8.11 Demo Regression Analysis with Multiple Variables

8.12 Assumptions of Regression

8.13 Correlation

8.14 Multicollinearity

8.15 Non Linear Regression

8.16 Validation

8.17 Demo K-Fold Cross Validation

8.18 Recap

Lesson 09: Classification

9.01 Learning Objectives

9.02 Introduction to Classification

9.03 Logistic Regression

9.04 k Nearest Neighbors

9.05 Decision Trees Scenario

9.06 Decision Tree Techniques

9.07 Demo Decision Tree Classification

9.08 Random Forest

9.09 Hyperplane

9.10 Support Vector Machines

9.11 Demo Support Vector Machines

9.12 Naïve Bayes Classification

9.13 Demo Naive Bayes Classifier

9.14 Bayes Theorem Example

9.15 Model Evaluation

9.16 Recap

Lesson 10: Clustering

10.01 Learning Objectives

10.02 Clustering

10.03 Clustering Methods

10.04 Demo K-means Clustering

10.05 Hierarchical Clustering

10.06 Demo Hierarchical Clustering

10.07 Density Based Clustering

10.08 Principal Component Analysis

10.09 Principal Components

10.10 Recap

Lesson 11: Association Mining

11.01 Learning Objectives

11.02 Association Mining

11.03 Transaction Data

11.04 Apriori Basic Concepts

11.05 Working of Apriori Algorithm

11.06 Demo Perform Association Using the Apriori Algorithm

11.07 Recap

11.08 Spotlight

Lesson 01: Course Introduction

1.01 Course Introduction

1.02 What you will Learn

Lesson 02: Data Visualization

2.01 Introduction

2.02 Data Visualization

2.03 Examples of Effective Visualizations

2.04 Storytelling with Data

2.05 Data Visualization Best Practices

2.06 Recap

Lesson 03: Introduction to Tableau

3.01 Introduction

3.02 Connect Open and Discover Sections

3.03 Connect Different Types of Files Used to Import Data

3.04 Introduction to Tableau Public and Tableau Desktop (Intro, similarities, and differences)

3.05 Recap

Lesson 04: Tableau: Workspace

4.01 Introduction

4.02 Importing Data from Various File Types

4.03 Previewing and Modifying Data

4.04 Creating Data Union Aggregate Data

4.05 Introduction to Workspace

4.06 Green vs. Blue Pills in Data Pane

4.07 Working with Sheets in Tableau

4.08 Introduction to Cards in Tableau

4.09 Recap

4.10 Spotlight

Lesson 05: Types of Charts in Tableau

5.01 Introduction

5.02 Basic Charts: Part 1

5.03 Basic Charts: Part 2

5.04 Basic Charts: Part 3

5.05 Effective Charts: Part 1

5.06 Effective Charts: Part 2

5.07 Other Charts: Part 1 Slope Graph

5.08 Other Charts: Part 2 Waterfall Chart

5.09 Other Charts: Part 3 Histogram and Heat Map

5.10 Other Charts: Part 4 Box and Whiskers Plot and Violin Plot

5.11 Other Charts: Part 5 Bubble Chart, Donut Chart, and Lollipop Chart

5.12 Other Charts: Part 6 Map Chart and Scatter Plot

5.13 Other Charts: Part 7 Area Chart, Bridge

Lesson 01: Introduction Introduction