Python + Sql + Tableau: Integrating Python, Sql, And Tableau

See the full picture: Learn how to combine the three most important tools in data science: Python, SQL, and Tableau

What you'll learn
How to use Python, SQL, and Tableau together
Software integration
Data preprocessing techniques
Apply machine learning
Create a module for later use of the ML model
Connect Python and SQL to transfer data from Jupyter to Workbench
Visualize data in Tableau
Analysis and interpretation of the exercise outputs in Jupyter and Tableau
Requirements
Basic coding skills in Python
Basic knowledge of SQL
Basic ability to use Tableau for data visualization
Description
Python, SQL, and Tableau are three of the most widely used tools in the world of data science. Python is the leading programming language;SQL is the most widely used means for communication with database systems;Tableau is the preferred solution for data visualization;To put it simply – SQL helps us store and manipulate the data we are working with, Python allows us to write code and perform calculations, and then Tableau enables beautiful data visualization. A well-thought-out integration stepping on these three pillars could save a business millions of dollars annually in terms of reporting personnel.Therefore, it goes without saying that employers are looking for Python, SQL, and Tableau when posting Data Scientist and Business Intelligence Analyst job descriptions. Not only that, but they would want to find a candidate who knows how to use these three tools simultaneously. This is how recurring data analysis tasks can be automated.So, in this course we will to teach you how to integrate Python, SQL, and Tableau. An essential skill that would give you an edge over other candidates. In fact, the best way to differentiate your job resume and get called for interviews is to acquire relevant skills other candidates lack. And because, we have prepared a topic that hasn’t been addressed elsewhere, you will be picking up a skill that truly has the potential to differentiate your profile.Many people know how to write some code in Python.Others use SQL and Tableau to a certain extent.Very few, however, are able to see the full picture and integrate Python, SQL, and Tableau providing a holistic solution. In the near future, most businesses will automate their reporting and business analysis tasks by implementing the techniques you will see in this course. It would be invaluable for your future career at a corporation or as a consultant, if you end up being the person automating such tasks.Our experience in one of the large global companies showed us that a consultant with these skills could charge a four-figure amount per hour. And the company was happy to pay that money because the end-product led to significant efficiencies in the long run.The course starts off by introducing software integration as a concept. We will discuss some important terms such as servers, clients, requests, and responses. Moreover, you will learn about data connectivity, APIs, and endpoints.Then, we will continue by introducing   the real-life example exercise the course is centered around – the ‘Absenteeism at Work’ dataset. The preprocessing part that follows will give you a taste of how BI and data science look like in real-life on the job situations. This is extremely important because a significant amount of a data scientist’s work consists in preprocessing, but many learning materials omit that Then we would continue by applying some Machine Learning on our data. You will learn how to explore the problem at hand from a machine learning perspective, how to create targets, what kind of statistical preprocessing is necessary for this part of the exercise, how to train a Machine Learning model, and how to test it. A truly comprehensive ML exercise.Connecting Python and SQL is not immediate. We have shown how that’s done in an entire section of the course. By the end of that section, you will be able to transfer data from Jupyter to Workbench.And finally, as promised, Tableau will allow us to visualize the data we have been working with. We will prepare several insightful charts and will interpret the results together.As you can see, this is a truly comprehensive data science exercise. There is no need to think twice. If you take this course now, you will acquire invaluable skills that will help you stand out from the rest of the candidates competing for a job.Also, we are happy to offer a 30-day unconditional no-questions-asked-money-back-in-full guarantee that you will enjoy the course.So, let’s do this! The only regret you will have is that you didn’t find this course sooner!

Overview

Section 1: Introduction

Lecture 1 What Does the Course Cover?

Section 2: What is software integration?

Lecture 2 Properties and Definitions: Data, Servers, Clients, Requests and Responses

Lecture 3 Properties and Definitions: Data Connectivity, APIs, and Endpoints

Lecture 4 Further Details on APIs

Lecture 5 Text Files as Means of Communication

Lecture 6 Definitions and Applications

Section 3: Setting up the working environment

Lecture 7 Setting Up the Environment - An Introduction (Do Not Skip, Please)!

Lecture 8 Why Python and why Jupyter?

Lecture 9 Installing Anaconda

Lecture 10 The Jupyter Dashboard - Part 1

Lecture 11 The Jupyter Dashboard - Part 2

Lecture 12 Jupyter Shortcuts

Lecture 13 Installing sklearn

Lecture 14 Installing Packages - Exercise

Lecture 15 Installing Packages - Solution

Section 4: What's next in the course?

Lecture 16 Up Ahead

Lecture 17 Real-Life Example: Absenteeism at Work

Lecture 18 Real-Life Example: The Dataset

Lecture 19 Important Notice Regarding Datasets

Section 5: Preprocessing

Lecture 20 What to Expect from the Next Couple of Sections

Lecture 21 Data Sets in Python

Lecture 22 Data at a Glance

Lecture 23 A Note on Our Usage of Terms with Multiple Meanings

Lecture 24 ARTICLE - A Brief Overview of Regression Analysis

Lecture 25 Picking the Appropriate Approach for the Task at Hand

Lecture 26 Removing Irrelevant Data

Lecture 27 EXERCISE - Removing Irrelevant Data

Lecture 28 SOLUTION - Removing Irrelevant Data

Lecture 29 Examining the Reasons for Absence

Lecture 30 Splitting a Column into Multiple Dummies

Lecture 31 EXERCISE - Splitting a Column into Multiple Dummies

Lecture 32 SOLUTION - Splitting a Column into Multiple Dummies

Lecture 33 ARTICLE - Dummy Variables: Reasoning

Lecture 34 Dummy Variables and Their Statistical Importance

Lecture 35 Grouping - Transforming Dummy Variables into Categorical Variables

Lecture 36 Concatenating Columns in Python

Lecture 37 EXERCISE - Concatenating Columns in Python

Lecture 38 SOLUTION - Concatenating Columns in Python

Lecture 39 Changing Column Order in Pandas DataFrame

Lecture 40 EXERCISE - Changing Column Order in Pandas DataFrame

Lecture 41 SOLUTION - Changing Column Order in Pandas DataFrame

Lecture 42 Implementing Checkpoints in Coding

Lecture 43 EXERCISE - Implementing Checkpoints in Coding

Lecture 44 SOLUTION - Implementing Checkpoint in Coding

Lecture 45 Exploring the Initial "Date" Column

Lecture 46 Using the "Date" Column to Extract the Appropriate Month Value

Lecture 47 Introducing "Day of the Week"

Lecture 48 EXERCISE - Removing Columns

Lecture 49 Further Analysis of the DataFrame: Next 5 Columns

Lecture 50 Further Analysis of the DaraFrame: "Education", "Children", "Pets"

Lecture 51 A Final Note on Preprocessing

Lecture 52 A Note on Exporting Your Data as a *.csv File

Section 6: Machine Learning

Lecture 53 Exploring the Problem from a Machine Learning Point of View

Lecture 54 Creating the Targets for the Logistic Regression

Lecture 55 Selecting the Inputs

Lecture 56 A Bit of Statistical Preprocessing

Lecture 57 Train-test Split of the Data

Lecture 58 Training the Model and Assessing its Accuracy

Lecture 59 Extracting the Intercept and Coefficients from a Logistic Regression

Lecture 60 Interpreting the Logistic Regression Coefficients

Lecture 61 Omitting the dummy variables from the Standardization

Lecture 62 Interpreting the Important Predictors

Lecture 63 Simplifying the Model (Backward Elimination)

Lecture 64 Testing the Machine Learning Model

Lecture 65 How to Save the Machine Learning Model and Prepare it for Future Deployment

Lecture 66 ARTICLE - More about 'pickling'

Lecture 67 EXERCISE - Saving the Model (and Scaler)

Lecture 68 Creating a Module for Later Use of the Model

Section 7: Installing MySQL and Getting Acquainted with the Interface

Lecture 69 Installing MySQL

Lecture 70 Installing MySQL on macOS and Unix systems

Lecture 71 Setting Up a Connection

Lecture 72 Introduction to the MySQL Interface

Section 8: Connecting Python and SQL

Lecture 73 Are you sure you're all set?

Lecture 74 Implementing the 'absenteeism_module' - Part I

Lecture 75 Implementing the 'absenteeism_module' - Part II

Lecture 76 Creating a Database in MySQL

Lecture 77 Importing and Installing 'pymysql'

Lecture 78 Creating a Connection and Cursor

Lecture 79 EXERCISE - Create 'df_new_obs'

Lecture 80 Creating the 'predicted_outputs' table in MySQL

Lecture 81 Running an SQL SELECT Statement from Python

Lecture 82 Transferring Data from Jupyter to Workbench - Part I

Lecture 83 Transferring Data from Jupyter to Workbench - Part II

Lecture 84 Transferring Data from Jupyter to Workbench - Part III

Section 9: Analyzing the Obtained data in Tableau

Lecture 85 EXERCISE - Age vs Probability

Lecture 86 Analysis in Tableau: Age vs Probability

Lecture 87 EXERCISE - Reasons vs Probability

Lecture 88 Analysis in Tableau: Reasons vs Probability

Lecture 89 EXERCISE - Transportation Expense vs Probability

Lecture 90 Analysis in Tableau: Transportation Expense vs Probability

Section 10: Bonus lecture

Lecture 91 Bonus Lecture: Next Steps

Intermediate and advanced students,Students eager to differentiate their resume,Individuals interested in a career in Business Intelligence and Data Science