Last updated 6/2021
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.80 GB | Duration: 5h 15m
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
Homepage
https://www.udemy.com/course/python-sql-tableau-integrating-python-sql-and-tableau/
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