Lesson 01 - Course Introduction 1.1 Course Introduction

Lesson 02 - Programming Basics 2.1 Learning Objectives 2.2 Introduction to Software 2.3 Categories of Software 2.4 Programming Models 2.5 Program Structure 2.6 Key Takeaways

Lesson 03 - Introduction to Python Programming

3.1 Learning Objectives 3.2 Introduction to Python 3.3 Python Installation 3.4 Python IDE 3.5 First Python Program 3.6 Key Takeaways

Lesson 04 - Python Data Types and Operators

4.1 Learning Objectives 4.2 Data Types and Data Assignment 4.3 Operators in Python 4.4 Strings in Python 4.5 Key Takeaways

Lesson 05 - Conditional Statements and Loops

5.1 Learning Objectives 5.2 Decision Control Structures in Python 5.3 Loops 5.4 Loop Control Statements 5.5 Loop Else Statements 5.6 Key Takeaways

Lesson 06 - Python Functions

6.1 Learning Objectives 6.2 Functions and Its Advantages 6.3 Function Arguments 6.4 Return Statement 6.5 Scope of a Variable 6.6 Generators Function 6.7 Function Types 6.8 Key Takeaways

Lesson 07 - OOPs Concepts with Python

7.1 Learning Objectives 7.2 Object-Oriented Programming Language 7.3 Objects and Classes 7.4 Methods and Attributes 7.5 Access Modifiers 7.6 Encapsulation 7.7 Inheritance 7.8 Polymorphism 7.9 Abstraction 7.10 Key Takeaways

Lesson 08 - Threading

8.1 Learning Objectives 8.2 Introduction to Threading 8.3 Introduction to Multi-Threading 8.4 Creating a New Thread 8.5 Synchronizing Threads 8.6 Key Takeaways

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 MySQL 2.07 Relationships in MySQL 2.08 Views in MySQL 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 MySQL: 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

Lesson 01: Introduction

Python for Data Science

Lesson 01 - Course Introduction

1.01 Course Introduction 1.02 What you will Learn

Lesson 02 - Introduction to Data Science

2.01 Introduction 2.02 Data Science and its Applications 2.03 The Data Science Process: Part 1 2.04 The Data Science Process: Part 2 2.05 Recap

Lesson 03 - Essentials of Python Programming

3.01 Introduction 3.02 Setting Up Jupyter Notebook: Part 1 3.03 Setting Up Jupyter Notebook: Part 2 3.04 Python Functions 3.05 Python Types and Sequences 3.06 Python Strings Deep Dive 3.07 Python Demo: Reading and Writing csv files 3.08 Date and Time in Python 3.09 Objects in Python Map 3.10 Lambda and List Comprehension 3.11 Why Python for Data Analysis? 3.12 Python Packages for Data Science 3.13 StatsModels Package: Part 1 3.14 StatsModels Package: Part 2 3.15 Scipy Package 3.16 Recap 3.17 Spotlight

Lesson 04 - NumPy

4.01 Introduction 4.02 Fundamentals of NumPy 4.03 Array shapes and axes in NumPy: Part A 4.04 NumPy Array Shapes and Axes: Part B 4.05 Arithmetic Operations 4.06 Conditional Logic 4.07 Common Mathematical and Statistical Functions in Numpy 4.08 Indexing And Slicing: Part 1 4.09 Indexing and Slicing: Part 2 4.10 File Handling 4.11 Recap

Lesson 05 - Linear Algebra

5.01 Introduction 5.02 Introduction to Linear Algebra 5.03 Scalars and Vectors 5.04 Dot Product of Two Vectors 5.05 Linear independence of Vectors 5.06 Norm of a Vector 5.07 Matrix 5.08 Matrix Operations 5.09 Transpose of a Matrix 5.10 Rank of a Matrix 5.11 Determinant of a matrix and Identity matrix or operator 5.12 Inverse of a matrix and Eigenvalues and Eigenvectors 5.13 Calculus in Linear Algebra 5.14 Recap

Lesson 06 - Statistics Fundamentals

6.01 Introduction 6.02 Importance of Statistics with Respect to Data Science 6.03 Common Statistical Terms 6.04 Types of Statistics 6.05 Data Categorization and Types 6.06 Levels of Measurement 6.07 Measures of Central Tendency 6.08 Measures of Central Tendency 6.09 Measures of Central Tendency 6.10 Measures of Dispersion 6.11 Random Variables 6.12 Sets 6.13 Measures of Shape (Skewness) 6.14 Measures of Shape (Kurtosis) 6.15 Covariance and Correlation 6.16 Recap

Lesson 07 - Probability Distribution

7.01 Introduction 7.02 Probability,its Importance, and Probability Distribution 7.03 Probability Distribution: Binomial Distribution 7.04 Probability Distribution: Poisson Distribution 7.05 Probability Distribution: Normal Distribution 7.06 Probability Distribution: Uniform Distribution 7.07 Probability Distribution: Bernoulli Distribution 7.08 Probability Density Function and Mass Function 7.09 Cumulative Distribution Function 7.10 Central Limit Theorem 7.11 Estimation Theory 7.12 Recap

Lesson 08 - Advanced Statistics

8.01 Introduction 8.02 Distribution 8.03 Kurtosis Skewness and Student's T-distribution 8.04 Hypothesis Testing and Mechanism 8.05 Hypothesis Testing Outcomes: Type I and II Errors 8.06 Null Hypothesis and Alternate Hypothesis 8.07 Confidence Intervals 8.08 Margins of error 8.09 Confidence Level 8.10 T - Test and P - values (Lab) 8.11 Z - Test and P - values 8.12 Comparing and Contrasting T test and Z test 8.13 Bayes Theorem 8.14 Chi Sqare Distribution 8.15 Chi Square Distribution: Demo 8.16 Chi Square Test and Goodness of Fit 8.17 Analysis of Variance or ANOVA 8.18 ANOVA Termonologies 8.19 Assumptions and Types of ANOVA 8.20 Partition of Variance using Python 8.21 F - Distribution 8.22 F - Distribution using Python 8.23 F - Test 8.24 Recap 8.25 Spotlight

Lesson 09 - Pandas

9.01 Introduction 9.02 Introduction to Pandas 9.03 Pandas Series 9.04 Querying a Series 9.05 Pandas Dataframes 9.06 Pandas Panel 9.07 Common Functions In Pandas 9.08 Pandas Functions Data Statistical Function, Windows Function 9.09 Pandas Function Data and Timedelta 9.10 IO Tools Explain all the read function 9.11 Categorical Data 9.12 Working with Text Data 9.13 Iteration 9.14 Sorting 9.15 Plotting with Pandas 9.16 Recap

Lesson 10 - Data Analysis

10.01 Introduction 10.02 Understanding Data 10.03 Types of Data Structured Unstructured Messy etc 10.04 Working with Data Choosing appropriate tools, Data collection, Data wrangling 10.05 Importing and Exporting Data in Python 10.06 Regular Expressions in Python 10.07 Manipulating text with Regular Expressions 10.08 Accessing databases in Python 10.09 Recap 10.10 Spotlight

Lesson 11 - Data Wrangling

11.01 Introduction 11.02 Pandorable or Idiomatic Pandas Code 11.03 Loading Indexing and Reindexing 11.04 Merging 11.05 Memory Optimization in Python 11.06 Data Pre Processing: Data Loading and Dropping Null Values 11.07 Data Pre-processing Filling Null Values 11.08 Data Binning Formatting and Normalization 11.09 Data Binning Standardization 11.10 Describing Data 11.11 Recap

Lesson 12 - Data Visualization

12.01 Introduction 12.02 Principles of information visualization 12.03 Visualizing Data using Pivot Tables 12.04 Data Visualization Libraries in Python Matplotlib 12.05 Graph Types 12.06 Data Visualization Libraries in Python Seaborn 12.07 Data Visualization Libraries in Python Seaborn 12.08 Data Visualization Libraries in Python Plotly 12.09 Data Visualization Libraries in Python Plotly 12.10 Data Visualization Libraries in Python Bokeh 12.11 Data Visualization Libraries in Python Bokeh 12.12 Using Matplotlib to Plot Graphs 12.13 Plotting 3D Graphs for Multiple Columns using Matplotlib 12.14 Using Matplotlib with other python packages 12.15 Using Seaborn to Plot Graphs 12.16 Using Seaborn to Plot Graphs 12.17 Plotting 3D Graphs for Multiple Columns Using Seaborn 12.18 Introduction to Plotly 12.19 Introduction to Bokeh 12.20 Recap

Lesson 13 - End to End Statistics Application with Python

13.01 Introduction 13.02 Basic Statistics with Python Problem Statement 13.03 Basic Statistics with Python Solution 13.04 Scipy for Statistics Problem Statement 13.05 Scipy For Statistics Solution 13.06 Advanced Statistics Python 13.07 Advanced Statistics with Python Solution 13.08 Recap 13.09 Spotlight

Machine Learning Using Python

Lesson 01 - Course Introduction

1.01 Course Introduction 1.02 What You Will Learn

Lesson 02 - Introduction to Machine Learning

2.01 Introduction 2.02 What Is Machine Learning? 2.03 Types of Machine Learning 2.04 Machine Learning Pipeline and MLOP's 2.05 Introduction to Python Packages Used in Machine Learning 2.06 Recap

Lesson 03 - Supervised Learning

3.01 Introduction 3.02 Supervised Learning 3.03 Applications of Supervised Learning 3.04 Preparing and Shaping Data 3.05 What is overfitting and underfitting? 3.06 Detecting and Preventing Overfitting and Underfitting 3.07 Regularization 3.08 Recap

Lesson 04 - Regression and Applications

4.01 Introduction 4.02 What is Regression? 4.03 Regression Types: Introduction 4.04 Linear Regression 4.05 Working with Linear Regression 4.06 Critical Assumptions for Linear Regression 4.07 Logistic Regression 4.08 Data Exploration Using SMOTE 4.09 Over Sampling Using SMOTE 4.10 Polynomial Regression 4.11 Data Preparation Model Building and Performance Evaluation Part A 4.12 Ridge Regression 4.13 Data Preparation Model Building: Part B 4.14 LASSO Regression 4.15 Data Preparation Model Building: Part C 4.16 Recap 4.17 Spotlight

Lesson 05 - Classification and Applications

5.01 Introduction 5.02 What are Classification Algorithms? 5.03 Types of Classification 5.04 Types and selection of performance parameters 5.05 Naive Bayes 5.06 Applying Naive Bayes Classifier 5.07 Stochastic Gradient Descent 5.08 Applying Stochastic Gradient Descent 5.09 K Nearest Neighbors 5.10 Applying K Nearest Neighbors 5.11 Decision Tree 5.12 Applying Decision Tree 5.13 Random Forest 5.14 Applying Random Forest 5.15 Boruta Explained 5.16 Automatic Feature Selection with Boruta 5.17 Support Vector Machine 5.18 Applying Support Vector Machine 5.19 Cohens Kappa Measure 5.20 Recap

Lesson 06 - Unsupervised Algorithms

6.01 Introduction 6.02 What are Unsupervised Algorithms? 6.03 Types of Unsupervised Algorithms Clustering and Associative 6.04 When to Use Unsupervised Algorithms? 6.05 Visualizing Outputs 6.06 Performance Parameters 6.07 Clustering Types 6.08 Hierarchical Clustering 6.09 Applying Hierarchical Clustering 6.10 K means Clustering: Part 1 6.11 K means Clustering: Part 2 6.12 Applying K Means Clustering 6.13 KNN-K Nearest Neighbors 6.14 Outlier Detection 6.15 Outlier Detection Algorithms in PyOD 6.16 Demo: K NN for Anomaly Detection 6.17 Principal Component Analysis 6.18 Applying Principal Component Analysis: PCA 6.19 Correspondence Analysis Multiple correspondence analysis: MCA 6.20 Singular Value Decomposition 6.21 Applying Singular Value Decomposition 6.22 Independent Component Analysis 6.23 Applying Independent Component Analysis 6.24 BIRCH 6.25 Applying BIRCH 6.26 Recap 6.27 Spotlight

Lesson 07 - Ensemble Learning

7.01 Introduction 7.02 What is Ensemble Learning? 7.03 Categories in Ensemble Learning 7.04 Sequential Ensemble Technique 7.05 Parallel Ensemble Technique 7.06 Types of Ensemble Methods 7.07 Bagging 7.08 Demo: Bagging 7.09 Boosting 7.10 Demo: Boosting 7.11 Stacking 7.12 Demo: Stacking 7.13 Reducing Errors with Ensembles 7.14 Applying Averaging and Max Voting 7.15 Hello World Tensorflow 7.16 Hands on with TensorFlow: Part A 7.17 Keras 7.18 Hands on with TensorFlow: Part B 7.19 Recap

Lesson 08 - Recommender System

8.01 Introduction 8.02 How do recommendation engines work 8.03 Recommendation Engine: Use Cases 8.04 Examples of Recommender System and Their Designs 8.05 Leveraging PyTorch to Build a Recommendation Engine 8.06 Collaborative Filtering and Memory Based Modeling 8.07 Item Based Collaborative Filtering 8.08 User Based Collaborative Filtering 8.09 Model Based Collaborative Filtering 8.10 Dimensionality Reduction and Matrix Factorization 8.11 Accuracy Matrices in ML 8.12 Recap 8.13 Spotlight

Math Refresher (free course)

Lesson 01: Course Introduction

1.01 About Simplilearn 1.02 Introduction to Mathematics 1.03 Types of Mathematics 1.04 Applications of Math in Data Industry 1.05 Learning Path 1.06 Course Components

Lesson 02: Probability and Statistics

2.01 Learning Objectives 2.02 Basics of Statistics and Probability 2.03 Introduction to Descriptive Statistics 2.04 Measures of Central Tendencies​ 2.05 Measures of Asymmetry 2.06 Measures of Variability​ 2.07 Measures of Relationship​ 2.08 Introduction to Probability 2.09 Key Takeaways 2.10 Knowledge check

Lesson 03: Coordinate Geometry

3.01 Learning Objectives 3.02 Introduction to Coordinate Geometry​ 3.03 Coordinate Geometry Formulas​ 3.04 Key Takeaways 3.05 Knowledge Check

Lesson 04: Linear Algebra

4.01 Learning Objectives 4.02 Introduction to Linear Algebra 4.03 Forms of Linear Equation 4.04 Solving a Linear Equation 4.05 Introduction to Matrices 4.06 Matrix Operations 4.07 Introduction to Vectors 4.08 Types and Properties of Vectors 4.09 Vector Operations 4.10 Key Takeaways 4.11 Knowledge Check

Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

5.01 Learning Objectives 5.02 Eigenvalues 5.03 Eigenvectors 5.04 Eigendecomposition 5.05 Key Takeaways 5.06 Knowledge Check

Lesson 06: Introduction to Calculus

6.01 Learning Objectives 6.02 Basics of Calculus 6.03 Differential Calculus 6.04 Differential Formulas 6.05 Integral Calculus 6.06 Integration Formulas 6.07 Key Takeaways 6.08 Knowledge Check

Statistics Essential for Data Science (free course)

Lesson 01: Course Introduction

1.01 Course Introduction 1.02 What Will You Learn

Lesson 02: Introduction to Statistics

2.01 Learning Objectives 2.02 What Is Statistics 2.03 Why Statistics 2.04 Difference between Population and Sample 2.05 Different Types of Statistics 2.06 Importance of Statistical Concepts in Data Science 2.07 Application of Statistical Concepts in Business 2.08 Case Studies of Statistics Usage in Business 2.09 Applications of Statistics in Business: Time Series Forecasting 2.10 Applications of Statistics in Business: Sales Forecasting 2.11 Recap

Lesson 03: Understanding the Data

3.01 Learning Objectives 3.02 Types of Data in Business Contexts 3.03 Data Categorization and Types of Data 3.04 Types of Data Collection 3.05 Structured vs. Unstructured Data 3.06 Sources of Data 3.07 Data Quality Issues 3.08 Recap

Lesson 04: Descriptive Statistics

4.01 Learning Objectives 4.02 Descriptive Statistics 4.03 Mathematical and Positional Averages 4.04 Measures of Central Tendancy: Part A 4.05 Measures of Central Tendancy: Part B 4.06 Measures of Dispersion 4.07 Range, Outliers, Quartiles, Deviation 4.08 Mean Absolute Deviation (MAD), Standard Deviation, Variance 4.09 Z Score and Empirical Rule 4.10 Coefficient of Variation and Its Application 4.11 Measures of Shape 4.12 Summarizing Data 4.13 Recap 4.14 Case Study One: Descriptive Statistics

Lesson 05: Data Visualization

5.01 Learning Objectives 5.02 Data Visualization 5.03 Basic Charts 5.04 Advanced Charts 5.05 Interpretation of the Charts 5.06 Selecting the Appropriate Chart 5.07 Charts Do's and Don'ts 5.08 Storytelling with Charts 5.09 Data Visualization: Example 5.10 Recap 5.11 Case Study Two: Data Visualization

Lesson 06: Probability

6.01 Learning Objectives 6.02 Introduction to Probability 6.03 Probability Example 6.04 Key Terms in Probability 6.05 Conditional Probability 6.06 Types of Events: Independent and Dependent 6.07 Addition Theorem of Probability 6.08 Multiplication Theorem of Probability 6.09 Bayes Theorem 6.10 Recap

Lesson 07: Probability Distributions

7.01 Learning Objectives 7.02 Probability Distribution 7.03 Random Variable 7.04 Probability Distributions: Discrete vs. Continuous: Part A 7.05 Probability Distributions: Discrete vs. Continuous: Part B 7.06 Commonly Used Discrete Probability Distributions: Part A 7.07 Discrete Probability Distributions: Poisson 7.08 Binomial by Poisson Theorem 7.09 Commonly Used Continuous Probability Distribution 7.10 Application of Normal Distribution 7.11 Recap

Lesson 08: Sampling and Sampling Techniques

8.01 Learning Objectives 8.02 Introduction to Sampling and Sampling Errors 8.03 Advantages and Disadvantages of Sampling 8.04 Probability Sampling Methods: Part A 8.05 Probability Sampling Methods: Part B 8.06 Non-Probability Sampling Methods: Part A 8.07 Non-Probability Sampling Methods: Part B 8.08 Uses of Probability Sampling and Non-Probability Sampling 8.09 Sampling 8.10 Probability Distribution 8.11 Theorem Five Point One 8.12 Center Limit Theorem 8.13 Stratified Sampling: Example 8.14 Probability Sampling: Example 8.15 Recap 8.16 Case Study Three: Sample and Sampling Techniques 8.17 Spotlight

Lesson 09: Inferential Statistics

Lesson 10: Application of Inferential Statistics

10.01 Learning Objectives 10.02 Bivariate Analysis 10.03 Selecting the Appropriate Test for EDA 10.04 Parametric vs. Non-Parametric Tests 10.05 Test of Significance 10.06 Z Test 10.07 T Test 10.08 Parametric Tests: ANOVA 10.09 Chi-Square Test 10.10 Sign Test 10.11 Kruskal Wallis Test 10.12 Mann Whitney Wilcoxon Test 10.13 Run Test for Randomness 10.14 Recap

Lesson 11: Relation between Variables

11.01 Learning Objectives 11.02 Correlation 11.03 Karl Pearson's Coefficient of Correlation 11.04 Karl Pearson's: Use Cases 11.05 Correlation Example 11.06 Spearman's Rank Correlation Coefficient 11.07 Causation 11.08 Example of Regression 11.09 Coefficient of Determination 11.10 Quantifying Quality 11.11 Recap

Lesson 12: Application of Statistics in Business

12.01 Learning Objectives 12.02 How to Use Statistics In Day-to-Day Business 12.03 Example: How to Not Lie With Statistics 12.04 How to Not Lie With Statistics (Repeated) 12.05 Lying Through Visualizations 12.06 Lying About Relationships 12.07 Recap 12.08 Spotlight

Lesson 13: Assisted Practice

Assisted Practice: Problem Statement Assisted Practice: Solution

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 Whisker Plot, Violin Plot 5.11 Other Charts: Part 5 - Bubble Chart, Donut Chart, and Lollipop Chart 5.12 Other Charts: Part 6 - Map Chart, Scatter Plot 5.13 Other Charts: Part 7 - Area Chart, Bridge Chart, Radar Chart 5.14 Advantages of Charts 5.15 Recap

Lesson 06: Creating Charts

6.01 Introduction 6.02 Horizontal and Vertical Bar Chart 6.03 Line Chart 6.04 Horizontal and Vertical Stacked Bar Charts 6.05 Map Chart 6.06 Pie Chart 6.07 Treemap 6.08 Highlight Tables 6.09 Recap

Lesson 07: Data Preparation

7.01 Introduction 7.02 Data Blending 7.03 When to Use Data Blending 7.04 Data Blending: Establishing a Link and Steps for Data Blending in Tableau 7.05 Introduction to Data Extraction 7.06 Extracts and Live Connections: Differences and Advantages 7.07 Introduction to Calculated Fields 7.08 Row Calculations 7.09 Aggregate Calculations 7.10 Table Calculations 7.11 Best Practices 7.12 Recap

Lesson 08: Preparation Techniques

8.01 Introduction 8.02 LOD Expressions and Their Types 8.03 Fixed Level of Detail 8.04 Include or Exclude Level of Detail 8.05 Filters and LOD Expressions 8.06 Pivoting Data 8.07 Creating Parameters 8.08 Creating Calculated Fields Using Parameters 8.09 Recap 8.10 Spotlight

Lesson 09: Filters and Analytics in Tableau

9.01 Introduction 9.02 Why Filters 9.03 Types of Filters in Tableau: Part A 9.04 Types of Filters in Tableau: Part B 9.05 Types of Filters in Tableau: Part C 9.06 Introduction to Analytics 9.07 Different Types of Analytics Options in Tableau 9.08 Using Medians and Averages 9.09 Recap

Lesson 10: Dashboards in Tableau

10.01 Introduction 10.02 Dashboard Introduction 10.03 Introduction to Dashboards 10.04 Elements in Dashboard Building 10.05 Fixed Size Dashboards 10.06 Using Actions Feature in Dashboards 10.07 Using Device Designer 10.08 Tips on Fitting Your Dashboard to a Device 10.09 Recap

Lesson 11: Stories in Tableau

11.01 Introduction 11.02 Stories 11.03 Creating Stories 11.04 Recap 11.05 Spotlight

Lesson 01: Course Introduction

1.01 Introduction