The Business Intelligence Analyst Course 2024

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

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

Requirements
No prior experience is required. We will start from the very basics
You’ll need to install MySQL, Tableau Public, and Anaconda. We will show you how to do it step by step
Microsoft Excel 2003, 2010, 2013, 2016, or 365

Description
Hi! Welcome to The Business Intelligence Analyst Course, the only course you need to become a BI Analyst. 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 We love teaching and we are really excited about this journey. It will get your foot in the door of an exciting and rising profession. Don’t hesitate and subscribe today. The only regret you will have is that you didn’t find this course sooner!

Overview
Section 1: Part 1: Introduction

Lecture 1 What Does the Course Cover

Lecture 2 Download All Resources

Section 2: Intro to Data and Data Science – The Different Data Science Fields

Lecture 3 Why Are There So Many Business and Data Science Buzzwords?

Lecture 4 Analysis vs Analytics

Lecture 5 Intro to Business Analytics, Data Analytics, and Data Science

Lecture 6 Adding Business Intelligence (BI), Machine Learning (ML), and AI to the Picture

Lecture 7 An Overview of our Data Science Infographic

Section 3: Intro to Data and Data Science – The Relationship between Different Fields

Lecture 8 When are Traditional data, Big Data, BI, Traditional Data Science and ML applied

Section 4: Intro to Data and Data Science – What is the Purpose of each Data Science Field

Lecture 9 Why do we Need each of these Disciplines?

Section 5: Intro to Data and Data Science – Common Data Science Techniques

Lecture 10 Traditional Data: Techniques

Lecture 11 Traditional Data: Real-life Examples

Lecture 12 Big Data: Techniques

Lecture 13 Big Data: Real-life Examples

Lecture 14 Business Intelligence (BI): Techniques

Lecture 15 Business Intelligence (BI): Real-life Examples

Lecture 16 Traditional Methods: Techniques

Lecture 17 Traditional Methods: Real-life Examples

Lecture 18 Machine Learning (ML): Techniques

Lecture 19 Machine Learning (ML): Types of Machine Learning

Lecture 20 Machine Learning (ML): Real-life Examples

Section 6: Intro to Data and Data Science – Common Data Science Tools

Lecture 21 Programming Languages & Software Employed in Data Science – All the Tools Needed

Section 7: Intro to Data and Data Science – Data Science Career Paths

Lecture 22 Data Science Job Positions: What do they Involve and What to Look out for?

Section 8: Intro to Data and Data Science – Dispelling Common Misconceptions

Lecture 23 Dispelling common Misconceptions

Section 9: Part 2: Statistics – Population and Sample

Lecture 24 Population vs sample

Section 10: Statistics – Descriptive Statistics

Lecture 25 Types of Data

Lecture 26 Levels of Measurement

Lecture 27 Categorical Variables – Visualization Techniques

Lecture 28 Categorical Variables Exercise

Lecture 29 Numerical Variables – Frequency Distribution Table

Lecture 30 Numerical Variables Exercise

Lecture 31 The Histogram

Lecture 32 Histogram Exercise

Lecture 33 Cross Table and Scatter Plot

Lecture 34 Cross Tables and Scatter Plots Exercise

Lecture 35 Mean, median and mode

Lecture 36 Mean, Median and Mode Exercise

Lecture 37 Skewness

Lecture 38 Skewness Exercise

Lecture 39 Variance

Lecture 40 Variance Exercise

Lecture 41 Standard Deviation and Coefficient of Variation

Lecture 42 Standard Deviation and Coefficient of Variation Exercise

Lecture 43 Covariance

Lecture 44 Covariance Exercise

Lecture 45 Correlation Coefficient

Lecture 46 Correlation Coefficient Exercise

Section 11: Statistics – Practical Example: Descriptive Statistics

Lecture 47 Practical Example

Lecture 48 Practical Example Exercise

Section 12: Statistics – Inferential Statistics Fundamentals

Lecture 49 Introduction

Lecture 50 What is a Distribution

Lecture 51 The Normal Distribution

Lecture 52 The Standard Normal Distribution

Lecture 53 The Standard Normal Distribution Exercise

Lecture 54 Central Limit Theorem

Lecture 55 Standard error

Lecture 56 Estimators and Estimates

Section 13: Statistics – Inferential Statistics: Confidence Intervals

Lecture 57 What are Confidence Intervals?

Lecture 58 Confidence Intervals; Population Variance Known; z-score

Lecture 59 Confidence Intervals; Population Variance Known; z-score Exercise

Lecture 60 Confidence interval clarifications

Lecture 61 Student’s T Distribution

Lecture 62 Confidence Intervals; Population Variance Unknown; t-score

Lecture 63 Confidence Intervals; Population Variance Unknown; t-score Exercise

Lecture 64 Margin of Error

Lecture 65 Confidence intervals. Two means. Dependent samples

Lecture 66 Confidence intervals. Two means. Dependent samples Exercise

Lecture 67 Confidence intervals. Two means. Independent samples (Part 1)

Lecture 68 Confidence intervals. Two means. Independent samples (Part 1) Exercise

Lecture 69 Confidence intervals. Two means. Independent samples (Part 2)

Lecture 70 Confidence intervals. Two means. Independent samples (Part 2) Exercise

Lecture 71 Confidence intervals. Two means. Independent samples (Part 3)

Section 14: Statistics – Practical Example: Inferential Statistics

Lecture 72 Practical Example: Inferential Statistics

Lecture 73 Practical Example: Inferential Statistics Exercise

Section 15: Statistics – Hypothesis Testing

Lecture 74 The Null vs Alternative Hypothesis

Lecture 75 Further Reading on Null and Alternative Hypothesis

Lecture 76 Rejection Region and Significance Level

Lecture 77 Type I Error and Type II Error

Lecture 78 Test for the Mean. Population Variance Known

Lecture 79 Test for the Mean. Population Variance Known Exercise

Lecture 80 p-value

Lecture 81 Test for the Mean. Population Variance Unknown

Lecture 82 Test for the Mean. Population Variance Unknown Exercise

Lecture 83 Test for the Mean. Dependent Samples

Lecture 84 Test for the Mean. Dependent Samples Exercise

Lecture 85 Test for the mean. Independent samples (Part 1)

Lecture 86 Test for the mean. Independent samples (Part 1). Exercise

Lecture 87 Test for the mean. Independent samples (Part 2)

Lecture 88 Test for the mean. Independent samples (Part 2)

Section 16: Statistics – Practical Example: Hypothesis Testing

Lecture 89 Practical Example: Hypothesis Testing

Lecture 90 Practical Example: Hypothesis Testing Exercise

Section 17: Part 3: Relational Database Theory & Introduction to SQL

Lecture 91 Why use SQL?

Lecture 92 Why use MySQL?

Lecture 93 Introducing Databases

Lecture 94 Relational Database Fundamentals

Lecture 95 Comparing Databases and Spreadsheets

Lecture 96 Important Database Terminology

Lecture 97 The Concept of Relational Schemas: Primary Key

Lecture 98 The Concept of Relational Schemas: Foreign Key

Lecture 99 The Concept of Relational Schemas: Unique Key and Null Values

Lecture 100 The Concept of Relational Schemas: Relationships Between Tables

Section 18: SQL – Install and get to know MySQL

Lecture 101 Installing MySQL Workbench and Server

Lecture 102 Installing Visual C

Lecture 103 Installing MySQL on macOS and Unix systems

Lecture 104 The Client-Server Model

Lecture 105 Linking GUI with the MySQL Server

Lecture 106 Read me!!!

Lecture 107 Creating a New User and a New Connection to it

Lecture 108 Familiarize Yourself with the MySQL Interface

Lecture 109 SQL Fundamentals – MySQL Session and Databases

Lecture 110 SQL Fundamentals – DROP, CREATE, SELECT, INSERT, DELETE

Section 19: SQL – Best SQL Practices

Lecture 111 Coding Tips and Best Practices – I

Lecture 112 Coding Tips and Best Practices – II

Section 20: SQL – Loading the ’employees’ Database

Lecture 113 Loading the ’employees’ Database

Lecture 114 Loading the ’employees’ Database

Section 21: SQL – Practical Application of the SQL SELECT Statement

Lecture 115 Using SELECT – FROM

Lecture 116 Using SELECT – FROM – Exercise

Lecture 117 Using SELECT – FROM – Solution

Lecture 118 Using WHERE

Lecture 119 Using WHERE – Exercise

Lecture 120 Using WHERE – Solution

Lecture 121 Using AND

Lecture 122 Using AND – Exercise

Lecture 123 Using AND – Solution

Lecture 124 Using OR

Lecture 125 Using OR – Exercise

Lecture 126 Using OR – Solution

Lecture 127 Operator Precedence and Logical Order

Lecture 128 Operator Precedence and Logical Order – Exercise

Lecture 129 Operator Precedence and Logical Order – Solution

Lecture 130 Using IN – NOT IN

Lecture 131 Using IN – NOT IN – Exercise 1

Lecture 132 Using IN – NOT IN – Solution 1

Lecture 133 Using IN – NOT IN – Exercise 2

Lecture 134 Using IN – NOT IN – Solution 2

Lecture 135 Using LIKE – NOT LIKE

Lecture 136 Using LIKE – NOT LIKE – Exercise

Lecture 137 Using LIKE – NOT LIKE – Solution

Lecture 138 Using Wildcard Characters

Lecture 139 Using Wildcard characters – Exercise

Lecture 140 Using Wildcard characters – Solution

Lecture 141 Using BETWEEN – AND

Lecture 142 Using BETWEEN – AND – Exercise

Lecture 143 Using BETWEEN – AND – Solution

Lecture 144 Using IS NOT NULL – IS NULL

Lecture 145 Using IS NOT NULL – IS NULL – Exercise

Lecture 146 Using IS NOT NULL – IS NULL – Solution

Lecture 147 Using Other Comparison Operators

Lecture 148 Using Other Comparison Operators – Exercise

Lecture 149 Using Other Comparison Operators – Solution

Lecture 150 Using SELECT DISTINCT

Lecture 151 Using SELECT DISTINCT – Exercise

Lecture 152 Using SELECT DISTINCT – Solution

Lecture 153 Getting to Know Aggregate Functions

Lecture 154 Getting to Know Aggregate Functions – Exercise

Lecture 155 Getting to Know Aggregate Functions – Solution

Lecture 156 Using ORDER BY

Lecture 157 Using ORDER BY – Exercise

Lecture 158 Using ORDER BY – Solution

Lecture 159 Using GROUP BY

Lecture 160 Using Aliases (AS)

Lecture 161 Using Aliases (AS) – Exercise

Lecture 162 Using Aliases (AS) – Solution

Lecture 163 Using HAVING

Lecture 164 Using HAVING – Exercise

Lecture 165 Using HAVING – Solution

Lecture 166 Using WHERE vs HAVING – Part I

Lecture 167 Using WHERE vs HAVING – Part II

Lecture 168 Using WHERE vs HAVING – Part II – Exercise

Lecture 169 Using WHERE vs HAVING – Part II – Solution

Lecture 170 Using LIMIT

Lecture 171 Using LIMIT – Exercise

Lecture 172 Using LIMIT – Solution

Section 22: SQL – Expanding on MySQL Aggregate Functions

Lecture 173 Applying COUNT()

Lecture 174 Applying COUNT() – Exercise

Lecture 175 Applying COUNT() – Solution

Lecture 176 Applying SUM()

Lecture 177 Applying SUM() – Exercise

Lecture 178 Applying SUM() – Solution

Lecture 179 MIN() and MAX()

Lecture 180 MIN() and MAX() – Exercise

Lecture 181 MIN() and MAX() – Solution

Lecture 182 Applying AVG()

Lecture 183 Applying AVG() – Exercise

Lecture 184 Applying AVG() – Solution

Lecture 185 Rounding Numbers with ROUND()

Lecture 186 Rounding Numbers with ROUND() – Exercise

Lecture 187 Rounding Numbers with ROUND() – Solution

Section 23: SQL – SQL JOINs

Lecture 188 What are JOINs?

Lecture 189 What are JOINs? – Exercise 1

Lecture 190 What are JOINs? – Exercise 2

Lecture 191 The Functionality of INNER JOIN – Part I

Lecture 192 The Functionality of INNER JOIN – Part II

Lecture 193 The Functionality of INNER JOIN – PART II – Exercise

Lecture 194 The Functionality of INNER JOIN – PART II – Solution

Lecture 195 Extra Info on Using Joins

Lecture 196 Duplicate Rows

Lecture 197 The Functionality of LEFT JOIN – Part I

Lecture 198 The Functionality of LEFT JOIN – Part II

Lecture 199 The Functionality of LEFT JOIN – Part II – Exercise

Lecture 200 The Functionality of LEFT JOIN – Part II – Solution

Lecture 201 The Functionality of RIGHT JOIN

Lecture 202 Differences between the New and the Old Join Syntax

Lecture 203 Differences between the New and the Old Join Syntax – Exercise

Lecture 204 Differences between the New and the Old Join Syntax – Solution

Lecture 205 Using JOIN and WHERE Together

Lecture 206 Important – Prevent Error Code: 1055!

Lecture 207 Using JOIN and WHERE Together – Exercise

Lecture 208 Using JOIN and WHERE Together – Solution

Lecture 209 The Functionality of CROSS JOIN

Lecture 210 The Functionality of CROSS JOIN – Exercise 1

Lecture 211 The Functionality of CROSS JOIN – Solution 1

Lecture 212 The Functionality of CROSS JOIN – Exercise 2

Lecture 213 The Functionality of CROSS JOIN – Solution 2

Lecture 214 Combining Aggregate Functions with Joins

Lecture 215 JOIN More than Two Tables

Lecture 216 JOIN More than Two Tables – Exercise

Lecture 217 JOIN More than Two Tables – Solution

Lecture 218 Top Tips for Joins

Lecture 219 Top Tips for Joins – Exercise

Lecture 220 Top Tips for Joins – Solution

Lecture 221 The Differences Between UNION and UNION ALL

Lecture 222 The Differences Between UNION and UNION ALL – Exercise

Lecture 223 The Differences Between UNION and UNION ALL – Solution

Section 24: SQL – SQL Subqueries

Lecture 224 SQL Subqueries with IN Embedded Inside WHERE

Lecture 225 SQL Subqueries with IN Embedded Inside WHERE – Exercise

Lecture 226 SQL Subqueries with IN Embedded Inside WHERE – Solution

Lecture 227 SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE

Lecture 228 SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE – Exercise

Lecture 229 SQL Subqueries with EXISTS-NOT EXISTS Embedded Inside WHERE – Solution

Lecture 230 SQL Subqueries Nested in SELECT and FROM

Lecture 231 SQL Subqueries Embedded in SELECT and FROM – Exercise 1

Lecture 232 SQL Subqueries Embedded in SELECT and FROM – Exercise 2

Lecture 233 SQL Subqueries Nested in SELECT and FROM – Solution 2

Section 25: SQL – Stored Routines

Lecture 234 Defining Stored Routines

Lecture 235 Create Stored Procedures with MySQL Syntax

Lecture 236 An Example of Stored Procedures Part I

Lecture 237 An Example of Stored Procedures Part II

Lecture 238 An Example of Stored Procedures Part II – Exercise

Lecture 239 An Example of Stored Procedures Part II – Solution

Lecture 240 Creating a Procedure in MySQL Another Way

Lecture 241 Create Stored Procedures with an Input Parameter

Lecture 242 Create Stored Procedures with an Output Parameter

Lecture 243 Create Stored Procedures with an Output Parameter – Exercise

Lecture 244 Stored Procedures with an Output Parameter – Solution

Lecture 245 SQL Variables

Lecture 246 SQL Variables – Exercise

Lecture 247 SQL Variables – Solution

Lecture 248 The Benefit of User-Defined Functions in MySQL

Lecture 249 Error Code: 1418.

Lecture 250 The Benefit of User-Defined Functions in MySQL – Exercise

Lecture 251 The Benefit of User-Defined Functions in MySQL – Solution

Lecture 252 Concluding Stored Routines

Section 26: SQL – The CASE Statement

Lecture 253 The SQL CASE Statement

Lecture 254 The SQL CASE Statement – Exercise 1

Lecture 255 THE SQL CASE Statement – Solution 1

Lecture 256 THE SQL CASE Statement – Exercise 2

Lecture 257 THE SQL CASE Statement – Solution 2

Lecture 258 THE SQL CASE Statement – Exercise 3

Lecture 259 THE SQL CASE Statement – Solution 3

Section 27: SQL – Window Functions

Lecture 260 Introduction to MySQL Window Functions

Lecture 261 The ROW_NUMBER() Ranking Window Function and the Relevant MySQL Syntax

Lecture 262 The ROW_NUMBER Ranking Window Function – Exercise

Lecture 263 The ROW_NUMBER Ranking Window Function – Solution

Lecture 264 A Note on Using Several Window Functions in a Query

Lecture 265 A Note on Using Several Window Functions in a Query – Exercise

Lecture 266 A Note on Using Several Window Functions in a Query – Solution

Lecture 267 MySQL Window Functions Syntax

Lecture 268 MySQL Window Functions Syntax – Exercise

Lecture 269 MySQL Window Functions Syntax – Solution

Lecture 270 The PARTITION BY Clause vs the GROUP BY Clause

Lecture 271 The PARTITION BY Clause vs the GROUP BY Clause – Exercise

Lecture 272 The PARTITION BY Clause vs the GROUP BY Clause – Solution

Lecture 273 The MySQL RANK() and DENSE_RANK() Window Functions

Lecture 274 The MySQL RANK() and DENSE_RANK() Window Functions – Exercise

Lecture 275 The MySQL RANK() and DENSE_RANK() Window Functions – Solution

Lecture 276 Working with MySQL Ranking Window Functions and Joins Together

Lecture 277 Working with MySQL Ranking Window Functions and Joins Together – Exercise

Lecture 278 Working with MySQL Ranking Window Functions and Joins Together – Solution

Lecture 279 The LAG() and LEAD() Value Window Functions

Lecture 280 The LAG() and LEAD() Value Window Functions – Exercise

Lecture 281 The LAG() and LEAD() Value Window Functions – Solution

Lecture 282 MySQL Aggregate Functions in the Context of Window Functions – Part I

Lecture 283 MySQL Aggregate Functions in the Context of Window Functions – Part I-Exercise

Lecture 284 MySQL Aggregate Functions in the Context of Window Functions – Part I-Solution

Lecture 285 MySQL Aggregate Functions in the Context of Window Functions – Part II

Lecture 286 MySQL Aggregate Functions in the Context of Window Functions – Part II-Exercise

Lecture 287 MySQL Aggregate Functions in the Context of Window Functions – Part II-Solution

Section 28: SQL Common Table Expressions (CTEs)

Lecture 288 MySQL Common Table Expressions – Introduction

Lecture 289 An Alternative Solution to the Same Task

Lecture 290 An Alternative Solution to the Same Task-Exercise

Lecture 291 An Alternative Solution to the Same Task-Solution

Lecture 292 Using Multiple Subclauses in a WITH Clause – Part I

Lecture 293 Using Multiple Subclauses in a WITH Clause – Part II

Lecture 294 Using Multiple Subclauses in a WITH Clause-Exercise

Lecture 295 Using Multiple Subclauses in a WITH Clause-Solution

Lecture 296 Referring to Common Table Expressions in a WITH Clause

Section 29: SQL Temporary Tables

Lecture 297 MySQL Temporary Tables – Introduction

Lecture 298 MySQL Temporary Tables in Action

Lecture 299 MySQL Temporary Tables in Action-Exercise

Lecture 300 MySQL Temporary Tables in Action-Solution

Lecture 301 Other Features of MySQL Temporary Tables

Lecture 302 Other Features of MySQL Temporary Tables-Exercise

Lecture 303 Other Features of MySQL Temporary Tables-Solution

Section 30: Part 4: Introduction to Tableau

Lecture 304 Why Use Tableau: Make Your Data Make an Impact

Lecture 305 Let’s Download Tableau Public

Lecture 306 Connecting Data in Tableau

Lecture 307 Exploring Tableau’s Interface

Lecture 308 Let’s Create our first Chart in Tableau!

Section 31: Tableau – Tableau functionalities

Lecture 309 Duplicating a Sheet

Lecture 310 Creating a Table

Lecture 311 Creating Custom Fields

Lecture 312 Creating a Custom Field and Adding Calculations to a Table

Lecture 313 Adding Totals and Subtotals

Lecture 314 Adding a Custom Calculation

Lecture 315 Inserting a Filter

Lecture 316 Working with Joins in Tableau

Section 32: Tableau – The Tableau Exercise

Lecture 317 Introduction to the Exercise

Lecture 318 Let’s Create a Dashboard – Visualizing the Three Charts We Want to Create

Lecture 319 Using Joins in Tableau

Lecture 320 Performing a Numbers Check – Attempt #1

Lecture 321 Blending Data in Tableau

Lecture 322 Performing a Numbers Check – Attempt #2

Lecture 323 First Chart

Lecture 324 Second Chart

Lecture 325 Third Chart

Lecture 326 Creating and Formatting a Dashboard

Lecture 327 Adding Interactive Filters for Improved Analysis

Lecture 328 Interactive Filters – fix

Section 33: Part 5: Combining SQL and Tableau – Introduction

Lecture 329 Introduction to Software Integration

Lecture 330 Combining SQL and Tableau

Lecture 331 Loading the Database

Lecture 332 Loading the Database

Section 34: Combining SQL and Tableau – Problem 1

Lecture 333 Problem 1: Task

Lecture 334 Problem 1: Task – Text

Lecture 335 Important clarification!

Lecture 336 Problem 1: Solution in SQL

Lecture 337 Problem 1: Solution in SQL – Code

Lecture 338 Exporting Your Output from SQL and Loading it in Tableau

Lecture 339 Chart 1: Visualizing the Solution in Tableau – Part I

Lecture 340 Chart 1: Visualizing the Solution in Tableau – Part II

Section 35: Combining SQL and Tableau – Problem 2

Lecture 341 Problem 2: Task

Lecture 342 Problem 2: Task – Text

Lecture 343 Problem 2: Solution in SQL

Lecture 344 Problem 2: Solution in SQL – Code

Lecture 345 Chart 2: Visualizing the Solution in Tableau

Section 36: Combining SQL and Tableau – Problem 3

Lecture 346 Problem 3: Task

Lecture 347 Problem 3: Task – Text

Lecture 348 Problem 3: Solution in SQL

Lecture 349 Problem 3: Solution in SQL – Code

Lecture 350 Chart 3: Visualizing the Solution in Tableau

Section 37: Combining SQL and Tableau – Problem 4

Lecture 351 Problem 4: Task

Lecture 352 Problem 4: Task – Text

Lecture 353 Problem 4: Solution in SQL

Lecture 354 Problem 4: Solution in SQL – Code

Lecture 355 Chart 4: Visualizing the Solution in Tableau

Section 38: Combining SQL and Tableau – Problem 5

Lecture 356 Problem 5: Organizing Charts 1-4 into a Beautiful Dashboard

Section 39: Part 6: Introduction to Programming with Python

Lecture 357 A 5-minute explanation of Programming

Lecture 358 Why use Python?

Lecture 359 Why use Jupyter?

Lecture 360 How to Install Python and Jupyter

Lecture 361 Understanding Jupyter’s Interface – Dashboard

Lecture 362 Understanding Jupyter’s Interface – Prerequisites for Coding

Lecture 363 Python 2 vs Python 3

Section 40: Python – Python Variables and Data Types

Lecture 364 Python Variables

Lecture 365 Understanding Numbers and Boolean Values

Lecture 366 Strings

Section 41: Python – Python Syntax Fundamentals

Lecture 367 The Arithmetic Operators of Python

Lecture 368 What is the Double Equality Sign?

Lecture 369 How to Reassign Values

Lecture 370 How to Add Comments

Lecture 371 Understanding Line Continuation

Lecture 372 How to Index Elements

Lecture 373 How to Structure Your Code with Indentation

Section 42: Python – Other Python Operators

Lecture 374 Python’s Comparison Operators

Lecture 375 Python’s Logical and Identity Operators

Section 43: Python – Conditional Statements

Lecture 376 Getting to know the IF Statement

Lecture 377 Adding an ELSE statement

Lecture 378 Else if, for Brief – ELIF

Lecture 379 An Additional Explanation of Boolean Values

Section 44: Python – Functions

Lecture 380 How to Define a Function in Python

Lecture 381 How to Create a Function with a Parameter

Lecture 382 Define a Function in Another Way

Lecture 383 How to Use a Function within a Function

Lecture 384 Use Conditional Statements and Functions Together

Lecture 385 How to Create Functions Which Contain a Few Arguments

Lecture 386 Built-In Functions in Python Worth Knowing

Section 45: Python – Python Sequences

Lecture 387 Introduction to Lists

Lecture 388 Using Methods in Python

Lecture 389 What is List Slicing?

Lecture 390 Working with Tuples

Lecture 391 Python Dictionaries

Section 46: Python – Using Iterations

Lecture 392 Using For Loops

Lecture 393 Using While Loops and Incrementing

Lecture 394 Use the range() Function to Create Lists

Lecture 395 Combine Conditional Statements and Loops

Lecture 396 All In – Conditional Statements, Functions, and Loops

Lecture 397 How to Iterate over Dictionaries

Section 47: Python – Advanced Python tools

Lecture 398 Introduction to Object Oriented Programming (OOP)

Lecture 399 Using Modules and Packages

Lecture 400 What is the Standard Library?

Lecture 401 How to Import Modules in Python

Section 48: Part 7: Integration – Software Integration

Lecture 402 Getting Started with Data, Servers, Clients, Requests, and Responses

Lecture 403 Getting Started with Data Connectivity, APIs, and Endpoints

Lecture 404 Become Better Acquainted with APIs

Lecture 405 Communication through Text Files

Lecture 406 What is Software Integration and How is it Applied?

Section 49: Integration – What is contained in this Course?

Lecture 407 Solving a Business Exercise with Python, SQL, and Tableau

Lecture 408 Presenting the Task: Absenteeism at Work

Lecture 409 Presenting the Data Set

Section 50: Integration – Data Preprocessing Step by Step

Lecture 410 How is the Content in the Next Sections Organized?

Lecture 411 How to Import the Data Set in Python

Lecture 412 Exploring the Data Set

Lecture 413 Programming vs the Rest of the World

Lecture 414 A Brief Summary of Regression Analysis

Lecture 415 The Approach we will Take to Solve this Exercise

Lecture 416 Dropping Variables We Don’t Need

Lecture 417 EXERCISE – Dropping Variables We Don’t Need

Lecture 418 SOLUTION – Dropping Variables We Don’t Need

Lecture 419 A Deeper Look at the ‘Reasons for Absence’ Column

Lecture 420 Splitting a Variable into Multiple Dummy Variables

Lecture 421 EXERCISE – Splitting a Variable into Multiple Dummy Variables

Lecture 422 SOLUTION – Splitting a Variable into Multiple Dummy Variables

Lecture 423 How to Drop a Dummy Variable from the Data Set

Lecture 424 A Statistical Perspective on Dummy Variables

Lecture 425 Categorizing the Various Reasons for Absence

Lecture 426 Concatenation in Python

Lecture 427 EXERCISE – Concatenation in Python

Lecture 428 SOLUTION – Concatenation in Python

Lecture 429 How to Reorder Columns in a DataFrame in Python

Lecture 430 EXERCISE – How to Reorder Columns in a DataFrame in Python

Lecture 431 SOLUTION – How to Reorder Columns in a DataFrame in Python

Lecture 432 Using Checkpoints to Ease Your Work in Jupyter

Lecture 433 EXERCISE – Using Checkpoints to Ease Your Work in Jupyter

Lecture 434 SOLUTION – Using Checkpoints to Ease Your Work in Jupyter

Lecture 435 Analyzing the “Date” Column

Lecture 436 Retrieving the Month Value From the “Date” Column

Lecture 437 Adding the “Day of the Week” Column

Lecture 438 EXERCISE – Dropping Columns

Lecture 439 Analysis of the Next 5 Columns in DF

Lecture 440 Dealing with More Numerical Features which may Behave like Categorical Ones

Lecture 441 A Final Note on this Section

Section 51: Integration – Integrating Python and SQL

Lecture 442 How to Use the ‘absenteeism_module’ in Python – Part I

Lecture 443 How to Use the ‘absenteeism_module’ in Python – Part II

Lecture 444 Creating the ‘predicted_outputs’ Database in MySQL

Lecture 445 Importing ‘pymysql’ in Python

Lecture 446 Creating a Connection and Cursor

Lecture 447 EXERCISE – Creating ‘df_new_obs’

Lecture 448 Creating the ‘predicted_outputs’ Table in MySQL

Lecture 449 Executing and SQL SELECT Statement from Python

Lecture 450 Sending Data from Jupyter to Workbench – Part I

Lecture 451 Sending Data from Jupyter to Workbench – Part II

Lecture 452 Sending Data from Jupyter to Workbench – Part III

Section 52: Integration – Using Tableau to Analyze the Predicted Outputs

Lecture 453 EXERCISE – Age vs Probability

Lecture 454 Using Tableau to Analyze Age vs Probability

Lecture 455 EXERCISE – Reasons vs Probability

Lecture 456 Using Tableau to Analyze Reasons vs Probability

Lecture 457 EXERCISE – Transportation Expense vs Probability

Lecture 458 Using Tableau to Analyze Transportation Expense vs Probability

Lecture 459 Completing 100%

Beginners to programming and data science,Students eager to learn about job opportunities in the field of data science,Candidates willing to boost their resume by learning how to combine the knowledge of Statistics, SQL, and Tableau in a real-world working environment,SQL Programmers who want to develop business reasoning and apply their knowledge to the solution of various business tasks,People interested in a Business Intelligence Analyst career