Data Analyst Course

1.01 Course Introduction

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

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

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

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: CREATE, DESCRIBE, and SHOW Table
5.07 Creating and Managing Tables: 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: WHERE and DISTINCT Clauses
5.11 Filtering Data: AND and OR Operators
5.12 Filtering Data: IN and NOT IN Operators
5.13 Filtering Data: BETWEEN and LIKE Operators
5.14 Filtering Data: 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

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

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 Records
7.09 Miscellaneous Functions
7.10 General Functions
7.11 Recap
7.12 Spotlight

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

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

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

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

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

1.01 Course Learning Objectives
1.02 Course End Objectives

2.01 Learning Objectives
2.02 Getting Started Analyzing Data in Python
2.03 Importing and Exporting Data in Python
2.04 Introduction to Data Analysis with Python
2.05 Python Packages for Data Science
2.06 The Problem
2.07 Understanding the Data
2.08 Introduction

3.01 Learning Objectives
3.02 Binning in Python
3.03 Data Formatting in Python
3.04 Data Normalization in Python
3.05 Dealing with Missing Values in Python
3.06 Indicator Variables in Python
3.07 Pre-processing Data in Python
3.08 Review: Data Wrangling

4.01 Learning Objectives
4.02 Analysis of Variance (ANOVA)
4.03 Correlation – Statistics
4.04 Correlation
4.05 Descriptive Statistics
4.06 Exploratory Data Analysis
4.07 GroupBy in Python
4.08 Review: Exploratory Data Analysis

5.01 Learning Objectives
5.02 Introduction
5.03 Linear Regression and Multiple Linear Regression
5.04 Model Evaluation using Visualization
5.05 Polynomial Regression and Pipelines
5.06 Measures for In-Sample Evaluation
5.07 Prediction and Decision Making
5.08 Review: Model Development

6.01 Learning Objectives
6.02 Model Evaluation
6.03 Overfitting, Underfitting, and Model Selection
6.04 Grid Search
6.05 Model Evaluation and Refinement
6.06 Ridge Regression
6.07 Review: Model Evaluation and Refinement

1.01 Course Objectives
1.02 Course Prerequisites
1.03 Why Python for Data Analytics?
1.04 Course Outline
1.05 Topics Covered
1.06 Course Features
1.07 Course-End Project Highlights
1.08 Learning Outcomes
1.09 Course Completion Criteria

2.01 Learning Objectives
2.02 Program
2.03 Programming Language
2.04 Algorithm, Pseudo Code, and Flowchart
2.05 Compiler and Interpreter
2.06 Key Takeaways

3.01 Learning Objectives
3.02 Python
3.03 Environments for Python
3.04 Anaconda
3.05 Installation of Anaconda Python Distribution
3.06 Jupyter Notebook
3.07 Assisted Practice: Install Python
3.08 Assisted Practice: First Python Program
3.09 Key Takeaways

4.01 Learning Objectives
4.02 Object Oriented Programming Language
4.03 Objects and Classes
4.04 Methods and Attributes
4.05 Access Modifiers
4.06 Assisted Practice: Objects and Classes
4.07 Abstraction
4.08 Assisted Practice: Abstraction
4.09 Encapsulation
4.10 Assisted Practice: Encapsulation
4.11 Inheritance
4.12 Assisted Practice: Inheritance
4.13 Polymorphism
4.14 Assisted Practice: Polymorphism
4.15 Key Takeaways

5.01 Learning Objectives
5.02 Variables
5.03 Data Types with Python
5.04 Assisted Practice: Data Types in Python
5.05 Keywords and Identifiers
5.06 Expressions
5.07 Basic Operators
5.08 Assisted Practice: Operators in Python
5.09 Functions
5.10 Assisted Practice: Search for a Specific Element from a Sorted List
5.11 Assisted Practice: Create a Banking System Using Functions
5.12 String Operations
5.13 Assisted Practice: String Operations in Python
5.14 Tuples
5.15 Assisted Practice: Tuples in Python
5.16 Lists
5.17 Assisted Practice: Lists in Python
5.18 Sets
5.19 Assisted Practice: Sets in Python
5.20 Dictionaries
5.21 Assisted Practice: Dictionary in Python
5.22 Unassisted Practice: Dictionary and its Operations
5.23 Conditions and Branching
5.24 Assisted Practice: Check the Scores of a Course
5.25 While Loop
5.26 Assisted Practice: Find Even Digit Numbers
5.27 Unassisted Practice: Fibonacci Series Using While Loop
5.28 For Loop
5.29 Assisted Practice: Calculate the Number of Letters and Digits
5.30 Unassisted Practice: Create a Pyramid of Stars
5.31 Break and Continue Statements
5.32 Key Takeaways
5.33 Tic-Tac-Toe Game

6.01 Learning Objectives
6.02 File Handling
6.03 File Opening and Closing
6.04 Reading and Writing Files
6.05 Directories in File Handling
6.06 Assisted Practice: File Handling
6.07 Errors and Exceptions
6.08 Assisted Practice: Exception Handling
6.09 Modules and Packages
6.10 Assisted Practice: Package Handling
6.11 Key Takeaways
6.12 Student Data Handling

7.01 Learning Objectives
7.02 Data Analytics
7.03 Data Analytics Process
7.04 Hypothesis
7.05 Data Visualization

8.01 Learning Objectives
8.02 Statistics
8.03 Probability Density Function
8.04 Types of Probability Density Function
8.05 Central Limit Theorem
8.06 Confidence Intervals
8.07 Hypothesis Testing: Parametric
8.08 Hypothesis Testing: Nonparametric
8.09 What is A/B Testing?
8.10 Case Study: A/B Testing
8.11 Key Takeaways
8.12 A/B Testing

9.01 Learning Objectives
9.02 NumPy
9.03 Assisted Practice: Create and Print NumPy Arrays
9.04 Operations
9.05 Assisted Practice: Executing Basic Operations in NumPy Array
9.06 Unassisted Practice: Performing Operations Using NumPy Array
9.07 Assisted Practice: Demonstrate the Use of Copy and Use
9.08 Assisted Practice: Manipulate the Shape of an Array
9.09 Key Takeaways
9.10 Country GDP
9.11 Olympic 2012 Medal Tally

10.01 Learning Objectives
10.02 Introduction to Pandas
10.03 Data Structures
10.04 Assisted Practice: Create Pandas Series
10.05 DataFrame
10.06 Assisted Practice: Create Pandas DataFrames
10.07 Unassisted Practice: Create Pandas DataFrames
10.08 Missing Values
10.09 Assisted Practice: Handle Missing Values
10.10 Data Operation
10.11 Assisted Practice: Data Operations in Pandas DataFrame
10.12 Unassisted Practice: Data Operations in Pandas DataFrame
10.13 Data Standardization
10.14 Assisted Practice: Pandas SQL Operations
10.15 Unassisted Practice: Pandas SQL Operations
10.16 Key Takeaways
10.17 Analyze the Federal Aviation Authority (FAA) Dataset using Pandas
10.18 Analyzing the Dataset

11.01 Learning Objectives
11.02 Data Visualization
11.03 Considerations of Data Visualization
11.04 Factors of Data Visualization
11.05 Python Libraries
11.06 Assisted Practice: Create Your First Plot Using Matplotlib
11.07 Line Properties
11.08 Assisted Practice: Create a Line Plot for Football Analytics
11.09 Multiple Plots and Subplots
11.10 Assisted Practice: Create a Plot with Annotation
11.11 Unassisted Practice: Create Multiple Plots to Analyze the Skills of the Players
11.12 Assisted Practice: Create Multiple Subplots Using plt.subplots
11.13 Types of Plots
11.14 Assisted Practice: Create a Stacked Histogram
11.15 Assisted Practice: Create a Scatter Plot of Pretest Scores and Posttest Scores
11.16 Assisted Practice: Create a Heat Map to Analyze the Sepal Width, Petal Length, and Petal Width of an Iris Dataset
11.17 Assisted Practice: Create a Pie Chart
11.18 Assisted Practice: Create an Error Bar
11.19 Assisted Practice: Area Chart to Display the Skills of the Players
11.20 Assisted Practice: Create a Word Cloud of Random Data
11.21 Assisted Practice: Create a Bar Chart
11.22 Assisted Practice: Create Box Plots
11.23 Assisted Practice: Create a Waffle Chart
11.24 Seaborn and Regression Plots
11.25 Introduction to Folium
11.26 Maps with Markers
11.27 Kernel Density Estimate Plots
11.28 Analyzing Variables Individually
11.29 Key Takeaways
11.30 Visualize the Sales Data

12.01 Learning Objectives
12.02 Introduction to Machine Learning
12.03 Machine Learning Approach
12.04 Scikit-Learn
12.05 Supervised Learning Models: Linear Regression
12.06 Assisted Practice: Loading a Dataset
12.07 Assisted Practice: Linear Regression Model
12.08 Supervised Learning Models: Logistic Regression
12.09 Supervised Learning Models: K-Nearest Neighbors
12.10 Assisted Practice: K-NN and Logistic Regression Models
12.11 Unsupervised Learning Models: Clustering
12.12 Assisted Practice: K-Means Clustering to Classify Data Points
12.13 Unsupervised Learning Models: Dimensionality Reduction
12.14 Unsupervised Learning Models: Principal Component Analysis
12.15 Assisted Practice: Principal Component Analysis (PCA)
12.16 Assisted Practice: Build Pipelines
12.17 Assisted Practice: Persist a Model for Future Use
12.18 Key Takeaways
12.19 Create a Model to Predict the Sales Outcome
12.20 List the Glucose Level Readings

Lesson 01 – Practice Project

1.01 R Programming for Data Science

1.01 Course Introduction
1.02 Rstudio Demo - WalkThrough

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 Responsibilites of Data Analyst
2.08 Industrial Use Case and Applications of Analytics
2.09 Recap

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

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

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

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

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

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

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

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

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

1.01 Course Introduction
1.02 What you will Learn

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

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

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

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 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 Chart, and Radar Chart
5.14 Advantages of Charts
5.15 Recap

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

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

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

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
9.09 Using Medians and Averages
9.10 Recap

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

11.01 Introduction
11.02 Stories
11.03 Creating Stories
11.04 Recap
11.05 Spotlight

1.01 Introduction

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