The ‘Python + SQL + Tableau: Integrating Python, SQL, and Tableau’ course teach you how to integrate Python, SQL, and Tableau. In this course, you will learn how to connect Python and SQL to transfer data from Jupyter to Workbench, Data preprocessing techniques, and Visualization of data in Tableu.

The course will also teach you about data connectivity, APIs, and endpoints. The course is usually available for INR 2,799 on Udemy but you can click on the link and get the ‘Python + SQL + Tableau: Integrating Python, SQL, and Tableau’ for INR 499.

Who all can opt for this course?

  • Learners who are in advanced and intermediate levels.
  • Students want to stand out on their resumes.
  • Those who want to work in business intelligence and data science.

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 499 (INR 2,79980% off
Duration05 Hours
Student Enrollment37,972 students
Instructor365 Careers
Topics CoveredMySQL, Software Integration, Data Preprocessing, Machine Learning
Course LevelIntermediate
Total Student Reviews4,472

Learning Outcomes

  • How to combine Tableau, Python, and SQL.
  • Integration of software.
  • Ways for preparing data.
  • Put machine learning to use
  • Make a module so that you may utilise the ML model later.
  • How to connect Python and SQL to transmit data from Jupyter to Workbench.
  • Use Tableau to visualise data.
  • Jupyter and Tableau analysis and interpretation of the exercise outputs.

Course Content

S.No.Module (Duration)Topics
1.Introduction (03 minutes)What Does the Course Cover?
2.What is software integration? (29 minutes)Properties and Definitions: Data, Servers, Clients, Requests and Responses
Properties and Definitions: Data, Servers, Clients, Requests and Responses
Properties and Definitions: Data Connectivity, APIs, and Endpoints
Properties and Definitions: Data Connectivity, APIs, and Endpoints
Further Details on APIs
Further Details on APIs
Text Files as Means of Communication
Text Files as Means of Communication
Definitions and Applications
Definitions and Applications
3.Setting up the working environment (23 minutes)Setting Up the Environment – An Introduction (Do Not Skip, Please)!
Why Python and why Jupyter?
Why Python and why Jupyter?
Installing Anaconda
The Jupyter Dashboard – Part 1
The Jupyter Dashboard – Part 2
Jupyter Shortcuts
The Jupyter Dashboard
Installing sklearn
Installing Packages – Exercise
Installing Packages – Solution
4.What’s next in the course? (10 minutes)Up Ahead
Real-Life Example: Absenteeism at Work
Real-Life Example: The Dataset
Real-Life Example: The Dataset
Important Notice Regarding Datasets
5.Preprocessing (01 hour 29 minutes)What to Expect from the Next Couple of Sections
Data Sets in Python
Data at a Glance
A Note on Our Usage of Terms with Multiple Meanings
ARTICLE – A Brief Overview of Regression Analysis
Picking the Appropriate Approach for the Task at Hand
Removing Irrelevant Data
EXERCISE – Removing Irrelevant Data
SOLUTION – Removing Irrelevant Data
Examining the Reasons for Absence
Splitting a Column into Multiple Dummies
EXERCISE – Splitting a Column into Multiple Dummies
SOLUTION – Splitting a Column into Multiple Dummies
ARTICLE – Dummy Variables: Reasoning
Dummy Variables and Their Statistical Importance
Grouping – Transforming Dummy Variables into Categorical Variables
Concatenating Columns in Python
EXERCISE – Concatenating Columns in Python
SOLUTION – Concatenating Columns in Python
Changing Column Order in Pandas DataFrame
EXERCISE – Changing Column Order in Pandas DataFrame
SOLUTION – Changing Column Order in Pandas DataFrame
Implementing Checkpoints in Coding
EXERCISE – Implementing Checkpoints in Coding
SOLUTION – Implementing Checkpoint in Coding
Exploring the Initial “Date” Column
Using the “Date” Column to Extract the Appropriate Month Value
Introducing “Day of the Week”
EXERCISE – Removing Columns
Further Analysis of the DataFrame: Next 5 Columns
Further Analysis of the DaraFrame: “Education”, “Children”, “Pets”
A Final Note on Preprocessing
A Note on Exporting Your Data as a *.csv File
6.Machine Learning (01 hour 07 minutes)Exploring the Problem from a Machine Learning Point of View
Creating the Targets for the Logistic Regression
Selecting the Inputs
A Bit of Statistical Preprocessing
Train-test Split of the Data
Training the Model and Assessing its Accuracy
Extracting the Intercept and Coefficients from a Logistic Regression
Interpreting the Logistic Regression Coefficients
Omitting the dummy variables from the Standardization
Interpreting the Important Predictors
Simplifying the Model (Backward Elimination)
Testing the Machine Learning Model
How to Save the Machine Learning Model and Prepare it for Future Deployment
ARTICLE – More about ‘pickling’
EXERCISE – Saving the Model (and Scaler)
Creating a Module for Later Use of the Model
7.Installing MySQL and Getting Acquainted with the Interface (19 minutes)Installing MySQL
Installing MySQL on macOS and Unix systems
Setting Up a Connection
Introduction to the MySQL Interface
8.Connecting Python and SQL (46 minutes)Are you sure you’re all set?
Implementing the ‘absenteeism_module’ – Part I
Implementing the ‘absenteeism_module’ – Part II
Creating a Database in MySQL
Importing and Installing ‘pymysql’
Creating a Connection and Cursor
EXERCISE – Create ‘df_new_obs’
Creating the ‘predicted_outputs’ table in MySQL
Running an SQL SELECT Statement from Python
Transferring Data from Jupyter to Workbench – Part I
Transferring Data from Jupyter to Workbench – Part II
Transferring Data from Jupyter to Workbench – Part III
9.Analyzing the Obtained data in Tableau (23 minutes)EXERCISE – Age vs Probability
Analysis in Tableau: Age vs Probability
EXERCISE – Reasons vs Probability
Analysis in Tableau: Reasons vs Probability
EXERCISE – Transportation Expense vs Probability
Analysis in Tableau: Transportation Expense vs Probability
10.Bonus lecture (46 seconds)Bonus Lecture: Next Steps

Resources Required

  • Python coding basics are required.
  • Basic understanding of SQL.
  • Basic knowledge of Tableau’s data visualisation capabilities.

Featured Review

Manojkumar Rajkumar (5/5) : The way they have explained from the scratch is very good. Easily understandable with perfect examples


  • Gulfaraz Tariq (5/5) : I’m a beginner in data science and my experience with learning the terms has been really good!
  • Ninja Turtle (4/5) : Some of the concepts’ in-depth study is considered beyond the scope of the course, however, it would be great if optional lessons on those are still provided.
  • Todd Grassi (5/5) : Great refresher for someone who has not taken a class in almost 10 years.
  • Gayathri Nandan Malligari (4/5) : Also another ML algorithm and Confusion matrix & metrics and would have been great.


  • M. Bugra Kanmaz (2/5) : ML part and the parts after that are not handy for practice.
  • Brenda Fosmire (1/5) : The cadence of the speach is overly acted making it hard to relate to.
  • Brenda Fosmire (1/5) : But the presentation is robot-ish and hard to get engaged with.
  • Mark Lynn (1/5) : In the first 50% of the course, I did not learn anything useful that I would apply in practice, there are too many numbers and nothing is understandable…

About the Author

The instructor of this course is 365 Careers which is creating opportunities for Data Science and Finance students. With 4.6 Academy Rating and 662,366 Reviews on Udemy, 365 Careers offers 91 Courses and has taught 2,298,099 Students so far.

  • On Udemy, 365 Careers is the top-selling provider of courses in business, finance, and data science.
  • In 210 different countries, more than 2,000,000 students have taken the company’s courses.
  • Individuals who have finished 365 Careers trainings now work at renowned companies like Apple, PayPal, and Citibank.
  • In Udemy right now, 365 focuses on the following subjects: 1) Finance – Financial modelling in Excel, valuation, capital budgeting, financial statement analysis (FSA), investment banking (IB), leveraged buyout (LBO), corporate budgeting, using Python for finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Credit Risk Modeling and Credit Analytics, Data Literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy, Statistics, Mathematics, Probability, SQL, Python Programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the Integration of SQL and Tableau, the Integration of SQL, Python, Tableau, and Power BI Entrepreneurship: Business Strategy, HR Management, Marketing, Decision-Making, Negotiation, and Persuasion, as well as Tesla’s Business Strategy and Marketing 4) Office productivity, including Microsoft Word, Excel, PowerPoint, and Outlook 5) Business Blockchain.
  • The courses offered by 365 Careers are the ideal place to start whether you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur.

Comparison Table

ParametersPython + SQL + Tableau: Integrating Python, SQL, and TableauThe Art of Doing: Code 40 Challenging Python Programs Today!Build Full Download Manager | Python & PyQt5
OffersINR 499 (INR 2,799) 80% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration5.5 hours28.5 hours4 hours
Student Enrollments37,97274,98127,786
Instructors365 CareersMichael EramoMahmoud Ahmed
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