**The Business Intelligence Analyst Course 2019**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 18.5 Hours | 10.7 GB

The skills you need to become a BI Analyst – Statistics, Database theory, SQL, Tableau – Everything is included

We are proud to present you this one-of-a-kind opportunity. There are several online courses teaching some of the skills related to the BI Analyst profession. The truth of the matter is that none of them completely prepare you.

Our program is different than the rest of the materials available online.

It is truly comprehensive. The Business Intelligence Analyst Course comprises of several modules:

- Introduction to Data and Data Science
- Statistics and Excel
- Database theory
- SQL
- Tableau
- SQL + Tableau

These are the precise technical skills recruiters are looking for when hiring BI Analysts. And today, you have the chance of acquiring an invaluable advantage to get ahead of other candidates. This course will be the secret to your success. And your success is our success, so let’s make it happen!

Here are some more details of what you get with The Business Intelligence Analyst Course:

- Introduction to Data and Data Science – Make sense of terms like business intelligence, traditional and big data, traditional statistical methods, machine learning, predictive analytics, supervised learning, unsupervised learning, reinforcement learning, and many more;
- Statistics and Excel – Understand statistical testing and build a solid foundation. Modern software packages and programming languages are automating most of these activities, but this part of the course gives you something more valuable – critical thinking abilities;
- Database theory – Before you start using SQL, it is highly beneficial to learn about the underlying database theory and acquire an understanding of why databases are created and how they can help us manage data
- SQL – when you can work with SQL, it means you don’t have to rely on others sending you data and executing queries for you. You can do that on your own. This allows you to be independent and dig deeper into the data to obtain the answers to questions that might improve the way your company does its business
- Tableau – one of the most powerful and intuitive data visualization tools available out there. Almost all large companies use such tools to enhance their BI capabilities. Tableau is the #1 best-in-class solution that helps you create powerful charts and dashboards
- Learning a programming language is meaningless without putting it to use. That’s why we integrate SQL and Tableau, and perform several real-life Business Intelligence tasks

Sounds amazing, right?

Our courses are unique because our team works hard to:

- Pre-script the entire content
- Work with real-life examples
- Provide easy to understand and complete explanations
- Create beautiful and engaging animations
- Prepare exercises, course notes, quizzes, and other materials that will enhance your course taking experience
- Be there for you and provide support whenever necessary

What you’ll learn

- Become an expert in Statistics, SQL, Tableau, and problem solving
- Boost your resume with in-demand skills
- Gather, organize, analyze and visualize data
- Use data for improved business decision-making
- Present information in the form of metrics, KPIs, reports, and dashboards
- Perform quantitative and qualitative business analysis
- Analyze current and historical data
- Discover how to find trends, market conditions, and research competitor positioning
- Understand the fundamentals of database theory
- Use SQL to create, design, and manipulate SQL databases
- Extract data from a database writing your own queries
- Create powerful professional visualizations in Tableau
- Combine SQL and Tableau to visualize data from the source
- Solve real-world business analysis tasks in SQL and Tableau

**Table of Contents**

**Part 1 Introduction**

What Does the Course Cover

**Intro to Data and Data Science – The Different Data Science Fields**

Why Are There So Many Business and Data Science Buzzwords

Analysis vs Analytics

Intro to Business Analytics, Data Analytics, and Data Science

Intro to Business Analytics, Data Analytics, and Data Science

Adding Business Intelligence (BI), Machine Learning (ML), and AI to the Picture

Adding Business Intelligence (BI), Machine Learning (ML), and AI to the Picture

An Overview of our Data Science Infographic

An Overview of our Data Science Infographic

**Intro to Data and Data Science – The Relationship between Different Fields**

When are Traditional data, Big Data, BI, Traditional Data Science and ML applied

When are Traditional data, Big Data, BI, Traditional Data Science and ML applied

**Intro to Data and Data Science – What is the Purpose of each Data Science Field**

Why do we Need each of these Disciplines

Why do we Need each of these Disciplines

**Intro to Data and Data Science – Common Data Science Techniques**

Traditional Data Techniques

Traditional Methods Techniques

Traditional Methods Techniques

Traditional Methods Real-life Examples

Machine Learning (ML) Techniques

Machine Learning (ML) Techniques

Machine Learning (ML) Types of Machine Learning

Machine Learning (ML) Types of Machine Learning

Machine Learning (ML) Real-life Examples

Machine Learning (ML) Real-life Examples

Traditional Data Techniques

Traditional Data Real-life Examples

Big Data Techniques

Big Data Techniques

Big Data Real-life Examples

Business Intelligence (BI) Techniques

Business Intelligence (BI) Techniques

Business Intelligence (BI) Real-life Examples

**Intro to Data and Data Science – Common Data Science Tools**

Programming Languages & Software Employed in Data Science – All the Tools Needed

Programming Languages & Software Employed in Data Science – All the Tools Needed

**Intro to Data and Data Science – Data Science Career Paths**

Data Science Job Positions What do they Involve and What to Look out for

Data Science Job Positions What do they Involve and What to Look out for

**Intro to Data and Data Science – Dispelling Common Misconceptions**

Dispelling common Misconceptions

Dispelling common Misconceptions

**Part 2 Statistics – Population and Sample**

Population vs sample

Population and Sample

**Statistics – Descriptive Statistics**

Types of Data

Numerical Variables Exercise

The Histogram

The Histogram

Histogram Exercise

Cross Table and Scatter Plot

Cross Tables and Scatter Plots

Cross Tables and Scatter Plots Exercise

Mean, median and mode

Mean, Median and Mode Exercise

Skewness

Types of data

Skewness

Skewness Exercise

Variance

Variance Exercise

Standard Deviation and Coefficient of Variation

Standard deviation

Standard Deviation and Coefficient of Variation Exercise

Covariance

Covariance

Covariance Exercise

Levels of Measurement

Correlation Coefficient

Correlation Coefficient

Correlation Coefficient Exercise

Levels of measurement

Categorical Variables – Visualization Techniques

Categorical variables. Visualization Techniques

Categorical Variables Exercise

Numerical Variables – Frequency Distribution Table

Numerical variables. Using a frequency distribution table

**Statistics – Practical Example Descriptive Statistics**

Practical Example

Practical Example Exercise

**Statistics – Inferential Statistics Fundamentals**

Introduction

Central Limit Theorem

Standard error

Standard error

Estimators and Estimates

Estimators and Estimates

What is a Distribution

What is a Distribution

The Normal Distribution

The Normal Distribution

The Standard Normal Distribution

The Standard Normal Distribution

The Standard Normal Distribution Exercise

Central Limit Theorem

**Statistics – Inferential Statistics Confidence Intervals**

What are Confidence Intervals

Margin of Error

Margin of Error

Confidence intervals. Two means. Dependent samples

Confidence intervals. Two means. Dependent samples Exercise

Confidence intervals. Two means. Independent samples (Part 1)

Confidence intervals. Two means. Independent samples (Part 1) Exercise

Confidence intervals. Two means. Independent samples (Part 2)

Confidence intervals. Two means. Independent samples (Part 2) Exercise

Confidence intervals. Two means. Independent samples (Part 3)

What are Confidence Intervals

Confidence Intervals; Population Variance Known; z-score

Confidence Intervals; Population Variance Known; z-score Exercise

Confidence interval clarifications

Student’s T Distribution

Student’s T Distribution

Confidence Intervals; Population Variance Unknown; t-score

Confidence Intervals; Population Variance Unknown; t-score Exercise

**Statistics – Practical Example Inferential Statistics**

Practical Example Inferential Statistics

Practical Example Inferential Statistics Exercise

**Statistics – Hypothesis Testing**

The Null vs Alternative Hypothesis

p-value

p-value

Test for the Mean. Population Variance Unknown

Test for the Mean. Population Variance Unknown Exercise

Test for the Mean. Dependent Samples

Test for the Mean. Dependent Samples Exercise

Test for the mean. Independent samples (Part 1)

Test for the mean. Independent samples (Part 1). Exercise

Test for the mean. Independent samples (Part 2)

Test for the mean. Independent samples (Part 2)

Further Reading on Null and Alternative Hypothesis

Test for the mean. Independent samples (Part 2)

The Null vs Alternative Hypothesis

Rejection Region and Significance Level

Rejection Region and Significance Level

Type I Error and Type II Error

Type I Error and Type II Error

Test for the Mean. Population Variance Known

Test for the Mean. Population Variance Known Exercise

**Statistics – Practical Example Hypothesis Testing**

Practical Example Hypothesis Testing

Practical Example Hypothesis Testing Exercise

**Part 3 Relational Database Theory & Introduction to SQL**

Why use SQL

Comparing Databases and Spreadsheets

Important Database Terminology

Important Database Terminology

The Concept of Relational Schemas Primary Key

The Concept of Relational Schemas Primary Key

The Concept of Relational Schemas Foreign Key

The Concept of Relational Schemas Foreign Key

The Concept of Relational Schemas Unique Key and Null Values

The Concept of Relational Schemas Unique Key

The Concept of Relational Schemas Relationships Between Tables

Why use SQL

The Concept of Relational Schemas Relationships Between Tables

Why use MySQL

Why use MySQL

Introducing Databases

Introducing Databases

Relational Database Fundamentals

Relational Database Fundamentals

Comparing Databases and Spreadsheets

**SQL – Install and get to know MySQL**

Installing MySQL Workbench and Server

Installing Visual C

The Client-Server Model

Linking GUI with the MySQL Server

Creating a New User and a New Connection to it

Familiarize Yourself with the MySQL Interface

**SQL – Best SQL Practices**

Coding Tips and Best Practices – I

Coding Tips and Best Practices – I

Coding Tips and Best Practices – II

Coding Tips and Best Practices – II

**SQL – Loading the ’employees’ Database**

Loading the ’employees’ Database

Loading the ’employees’ Database

**SQL – Practical Application of the SQL SELECT Statement**

Using SELECT – FROM

Using OR

Using OR – Exercise

Using OR – Solution

Operator Precedence and Logical Order

Operator Precedence and Logical Order – Exercise

Operator Precedence and Logical Order – Solution

Using IN – NOT IN

Using IN – NOT IN – Exercise 1

Using IN – NOT IN – Solution 1

Using IN – NOT IN – Exercise 2

Using SELECT – FROM – Exercise

Using IN – NOT IN – Solution 2

Using LIKE – NOT LIKE

Using LIKE – NOT LIKE – Exercise

Using LIKE – NOT LIKE – Solution

Using Wildcard Characters

Using Wildcard characters – Exercise

Using Wildcard characters – Solution

Using BETWEEN – AND

Using BETWEEN – AND – Exercise

Using BETWEEN – AND – Solution

Using SELECT – FROM – Solution

Using IS NOT NULL – IS NULL

Using IS NOT NULL – IS NULL – Exercise

Using IS NOT NULL – IS NULL – Solution

Using Other Comparison Operators

Using Other Comparison Operators – Exercise

Using Other Comparison Operators – Solution

Using SELECT DISTINCT

Using SELECT DISTINCT – Exercise

Using SELECT DISTINCT – Solution

Getting to Know Aggregate Functions

Using WHERE

Getting to Know Aggregate Functions – Exercise

Getting to Know Aggregate Functions – Solution

Using ORDER BY

Using ORDER BY – Exercise

Using ORDER BY – Solution

Using GROUP BY

Using Aliases (AS)

Using Aliases (AS) – Exercise

Using Aliases (AS) – Solution

Using HAVING

Using WHERE – Exercise

Using HAVING – Exercise

Using HAVING – Solution

Using WHERE vs HAVING – Part I

Using WHERE vs HAVING – Part II

Using WHERE vs HAVING – Part II – Exercise

Using WHERE vs HAVING – Part II – Solution

Using LIMIT

Using LIMIT – Exercise

Using LIMIT – Solution

Using WHERE – Solution

Using AND

Using AND – Exercise

Using AND – Solution

**SQL – Expanding on MySQL Aggregate Functions**

Applying COUNT()

Applying AVG()

Applying AVG() – Exercise

Applying AVG() – Solution

Rounding Numbers with ROUND()

Rounding Numbers with ROUND() – Exercise

Rounding Numbers with ROUND() – Solution

Applying COUNT() – Exercise

Applying COUNT() – Solution

Applying SUM()

Applying SUM() – Exercise

Applying SUM() – Solution

MIN() and MAX()

MIN() and MAX() – Exercise

MIN() and MAX() – Solution

**SQL – SQL JOINs**

What are JOINs

The Functionality of LEFT JOIN – Part I

The Functionality of LEFT JOIN – Part II

The Functionality of LEFT JOIN – Part II – Exercise

The Functionality of LEFT JOIN – Part II – Solution

The Functionality of RIGHT JOIN

Differences between the New and the Old Join Syntax

Differences between the New and the Old Join Syntax – Exercise

Differences between the New and the Old Join Syntax – Solution

Using JOIN and WHERE Together

Using JOIN and WHERE Together – Exercise

What are JOINs – Exercise 1

Using JOIN and WHERE Together – Solution

The Functionality of CROSS JOIN

The Functionality of CROSS JOIN – Exercise 1

The Functionality of CROSS JOIN – Solution 1

The Functionality of CROSS JOIN – Exercise 2

The Functionality of CROSS JOIN – Solution 2

Combining Aggregate Functions with Joins

JOIN More than Two Tables

JOIN More than Two Tables – Exercise

JOIN More than Two Tables – Solution

What are JOINs – Exercise 2

Top Tips for Joins

Top Tips for Joins – Exercise

Top Tips for Joins – Solution

The Differences Between UNION and UNION ALL

The Differences Between UNION and UNION ALL – Exercise

The Differences Between UNION and UNION ALL – Solution

The Functionality of INNER JOIN – Part I

The Functionality of INNER JOIN – Part II

The Functionality of INNER JOIN – PART II – Exercise

The Functionality of INNER JOIN – PART II – Solution

Extra Info on Using Joins

Duplicate Rows

**SQL – SQL Subqueries**

SQL Subqueries with IN Embedded Inside WHERE

SQL Subqueries Nested in SELECT and FROM – Solution 2

SQL Subqueries with IN Embedded Inside WHERE – Exercise

SQL Subqueries with IN Embedded Inside WHERE – Solution

SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE

SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE – Exercise

SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE – Solution

SQL Subqueries Nested in SELECT and FROM

SQL Subqueries Embedded in SELECT and FROM – Exercise 1

SQL Subqueries Embedded in SELECT and FROM – Exercise 2

**SQL – Stored Routines**

Defining Stored Routines

Create Stored Procedures with an Output Parameter

Create Stored Procedures with an Output Parameter – Exercise

Stored Procedures with an Output Parameter – Solution

SQL Variables

SQL Variables – Exercise

SQL Variables – Solution

The Benefit of User-Defined Functions in MySQL

The Benefit of User-Defined Functions in MySQL – Exercise

The Benefit of User-Defined Functions in MySQL – Solution

Concluding Stored Routines

Defining Stored Routines

Concluding Stored Routines

Create Stored Procedures with MySQL Syntax

An Example of Stored Procedures Part I

An Example of Stored Procedures Part II

An Example of Stored Procedures Part II – Exercise

An Example of Stored Procedures Part II – Solution

Creating a Procedure in MySQL Another Way

Create Stored Procedures with an Input Parameter

**SQL – The CASE Statement**

The SQL CASE Statement

The SQL CASE Statement – Exercise 1

THE SQL CASE Statement – Solution 1

THE SQL CASE Statement – Exercise 2

THE SQL CASE Statement – Solution 2

THE SQL CASE Statement – Exercise 3

THE SQL CASE Statement – Solution 3

**Part 4 Introduction to Tableau**

Why Use Tableau Make Your Data Make an Impact

Let’s Download Tableau Public

Connecting Data in Tableau

Exploring Tableau’s Interface

Let’s Create our first Chart in Tableau!

**Tableau – Tableau functionalities**

Duplicating a Sheet

Creating a Table

Creating Custom Fields

Creating a Custom Field and Adding Calculations to a Table

Adding Totals and Subtotals

Adding a Custom Calculation

Inserting a Filter

Working with Joins in Tableau

**Tableau – The Tableau Exercise**

Introduction to the Exercise

Creating and Formatting a Dashboard

Adding Interactive Filters for Improved Analysis

Let’s Create a Dashboard – Visualizing the Three Charts We Want to Create

Using Joins in Tableau

Performing a Numbers Check – Attempt #1

Blending Data in Tableau

Performing a Numbers Check – Attempt #2

First Chart

Second chart

Third Chart

**Part 5 Combining SQL and Tableau – Introduction**

Introduction to Software Integration

Combining SQL and Tableau

Loading the Database

Loading the Database

**Combining SQL and Tableau – Problem 1**

Problem 1 Task

Problem 1 Task – Text

Problem 1 Solution in SQL

Problem 1 Solution in SQL – Code

Exporting Your Output from SQL and Loading it in Tableau

Chart 1 Visualizing the Solution in Tableau – Part I

Chart 1 Visualizing the Solution in Tableau – Part II

**Combining SQL and Tableau – Problem 2**

Problem 2 Task

Problem 2 Task – Text

Problem 2 Solution in SQL

Problem 2 Solution in SQL – Code

Chart 2 Visualizing the Solution in Tableau

**Combining SQL and Tableau – Problem 3**

Problem 3 Task

Problem 3 Task – Text

Problem 3 Solution in SQL

Problem 3 Solution in SQL – Code

Chart 3 Visualizing the Solution in Tableau

**Combining SQL and Tableau – Problem 4**

Problem 4 Task

Problem 4 Task – Text

Problem 4 Solution in SQL

Problem 4 Solution in SQL – Code

Chart 4 Visualizing the Solution in Tableau

**Combining SQL and Tableau – Problem 5**

Problem 5 Organizing Charts 1-4 into a Beautiful Dashboard

**Part 6 Introduction to Programming with Python**

A 5-minute explanation of Programming

Understanding Jupyter’s Interface

A 5-minute explanation of Programming

Why use Python

Why Use Python

Why use Jupyter

Why Use Jupyter

How to Install Python and Jupyter

Understanding Jupyter’s Interface – Dashboard

Understanding Jupyter’s Interface – Prerequisites for Coding

**Python – Python Variables and Data Types**

Python Variables

Python Variables

Understanding Numbers and Boolean Values

Understanding Numbers and Boolean Values

Strings

Strings

**Python – Python Syntax Fundamentals**

The Arithmetic Operators of Python

How to Index Elements

How to Index Elements

How to Structure Your Code with Indentation

How to Structure Your Code with Indentation

Using Arithmetic Operators in Python

What is the Double Equality Sign

What is the Double Equality Sign

How to Reassign Values

How to Reassign Values

How to Add Comments

How to Add Comments

Understanding Line Continuation

**Python – Other Python Operators**

Python’s Comparison Operators

Python’s Comparison Operators

Python’s Logical and Identity Operators

Python’s Logical and Identity Operators

**Python – Conditional Statements**

Getting to know the IF Statement

Getting to know the IF Statement

Adding an ELSE statement

Else if, for Brief – ELIF

An Additional Explanation of Boolean Values

An Additional Explanation of Boolean Values

**Python – Functions**

How to Define a Function in Python

How to Create a Function with a Parameter

Define a Function in Another Way

How to use a Function within a Function

Use Conditional Statements and Functions Together

How to Create Functions Which Contain a Few Arguments

Built-In Functions in Python Worth Knowing

Python – Functions

**Python – Python Sequences**

Introduction to Lists

Introduction to Lists

Using Methods in Python

Using Methods in Python

What is List Slicing

Working with Tuples

Python Dictionaries

Python Dictionaries

**Python – Using Iterations**

Using For Loops

For Loops

Using While Loops and Incrementing

Use the range() Function to Create Lists

Use the range() Function to Create Lists

Combine Conditional Statements and Loops

All In – Conditional Statements, Functions, and Loops

How to Iterate over Dictionaries

**Python – Advanced Python tools**

Introduction to Object Oriented Programming (OOP)

Introduction to Object Oriented Programming (OOP)

Using Modules and Packages

Using Modules and Packages

What is the Standard Library

What is the Standard Library

How to Import Modules in Python

How to Import Modules in Python

**Integration – Software Integration**

Getting Started with Data, Servers, Clients, Requests, and Responses

What is Software Integration and How is it Applied

Getting Started with Data, Servers, Clients, Requests, and Responses

Getting Started with Data Connectivity, APIs, and Endpoints

Getting Started with Data Connectivity, APIs, and Endpoints

Become Better Acquainted with APIs

Become Better Acquainted with APIs

Communication through Text Files

Communication through Text Files

What is Software Integration and How is it Applied

**Integration – What is contained in this Course**

Solving a Business Exercise with Python, SQL, and Tableau

Presenting the Task Absenteeism at Work

Presenting the Data Set

Presenting the Data Set

**Integration – Data Preprocessing Step by Step**

How is the Content in the Next Sections Organized

A Deeper Look at the ‘Reasons for Absence’ Column

Splitting a Variable into Multiple Dummy Variables

EXERCISE – Splitting a Variable into Multiple Dummy Variables

SOLUTION – Splitting a Variable into Multiple Dummy Variables

How to Drop a Dummy Variable from the Data Set

A Statistical Perspective on Dummy Variables

Categorizing the Various Reasons for Absence

Concatenation in Python

EXERCISE – Concatenation in Python

SOLUTION – Concatenation in Python

How to Import the Data Set in Python

How to Reorder Columns in a DataFrame in Python

EXERCISE – How to Reorder Columns in a DataFrame in Python

SOLUTION – How to Reorder Columns in a DataFrame in Python

Using Checkpoints to Ease Your Work in Jupyter

EXERCISE – Using Checkpoints to Ease Your Work in Jupyter

SOLUTION – Using Checkpoints to Ease Your Work in Jupyter

Analyzing the Date Column

Retrieving the Month Value From the Date Column

Adding the Day of the Week Column

EXERCISE – Dropping Columns

Exploring the Data Set

Analysis of the Next 5 Columns in DF

Dealing with More Numerical Features which may Behave like Categorical Ones

A Final Note on this Section

Programming vs the Rest of the World

A Brief Summary of Regression Analysis

The Approach we will Take to Solve this Exercise

Dropping Variables We Don’t Need

EXERCISE – Dropping Variables We Don’t Need

SOLUTION – Dropping Variables We Don’t Need

**Integration – Integrating Python and SQL**

How to Use the ‘absenteeism module’ in Python – Part I

Sending Data from Jupyter to Workbench – Part II

Sending Data from Jupyter to Workbench – Part III

How to Use the ‘absenteeism module’ in Python – Part II

Creating the ‘predicted outputs’ Database in MySQL

Importing ‘pymysql’ in Python

Creating a Connection and Cursor

EXERCISE – Creating ‘df new obs’

Creating the ‘predicted outputs’ Table in MySQL

Executing and SQL SELECT Statement from Python

Sending Data from Jupyter to Workbench – Part I

**Integration – Using Tableau to Analyze the Predicted Outputs**

EXERCISE – Age vs Probability

Using Tableau to Analyze Age vs Probability

EXERCISE – Reasons vs Probability

Using Tableau to Analyze Reasons vs Probability

EXERCISE – Transportation Expense vs Probability

Using Tableau to Analyze Transportation Expense vs Probability

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