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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 499 ( |
| Duration | 05 Hours |
| Rating | 4.4/5 |
| Student Enrollment | 37,972 students |
| Instructor | 365 Careers |
| Topics Covered | MySQL, Software Integration, Data Preprocessing, Machine Learning |
| Course Level | Intermediate |
| Total Student Reviews | 4,472 |
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
Pros
- 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.
Cons
- 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
| Parameters | Python + SQL + Tableau: Integrating Python, SQL, and Tableau | The Art of Doing: Code 40 Challenging Python Programs Today! | Build Full Download Manager | Python & PyQt5 |
|---|---|---|---|
| Offers | INR 499 ( | INR 455 ( | INR 455 ( |
| Duration | 5.5 hours | 28.5 hours | 4 hours |
| Rating | 4.4/5 | 4.8/5 | 4.1/5 |
| Student Enrollments | 37,972 | 74,981 | 27,786 |
| Instructors | 365 Careers | Michael Eramo | Mahmoud Ahmed |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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