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Business Business Analytics & Intelligence Database Programming

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
Bestseller
Rating: 4.5 out of 54.5 (2,543 ratings)
24,373 students
Created by 365 Careers
Last updated 1/2021
English
English [Auto], French [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

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!

Who this course is for:

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

Featured review

Nikhil Pandya
Nikhil Pandya
65 courses
43 reviews
Rating: 5.0 out of 5a year ago
I have done some basic courses in python, SQL and Tableau. In this course, the neat way of explaining along with the appropriate slides makes learning look very easy. I enjoyed learning the new techniques and the integration of mySQL, Python and Tableau. I will repeat this course to refresh my memory.

Course content

10 sections • 91 lectures • 5h 14m total length

  • Preview03:55

  • Preview04:43
  • Properties and Definitions: Data, Servers, Clients, Requests and Responses
    2 questions
  • Preview07:05
  • Properties and Definitions: Data Connectivity, APIs, and Endpoints
    2 questions
  • Further Details on APIs
    08:05
  • Further Details on APIs
    2 questions
  • Preview04:20
  • Text Files as Means of Communication
    1 question
  • Definitions and Applications
    05:25
  • Definitions and Applications
    2 questions

  • Setting Up the Environment - An Introduction (Do Not Skip, Please)!
    00:51
  • Why Python and why Jupyter?
    04:59
  • Why Python and why Jupyter?
    2 questions
  • Installing Anaconda
    06:49
  • The Jupyter Dashboard - Part 1
    03:15
  • The Jupyter Dashboard - Part 2
    06:15
  • Jupyter Shortcuts
    00:09
  • The Jupyter Dashboard
    3 questions
  • Installing sklearn
    01:16
  • Installing Packages - Exercise
    00:09
  • Installing Packages - Solution
    00:12

  • Up Ahead
    04:08
  • Real-Life Example: Absenteeism at Work
    02:48
  • Real-Life Example: The Dataset
    03:18
  • Real-Life Example: The Dataset
    1 question
  • Important Notice Regarding Datasets
    00:37

  • What to Expect from the Next Couple of Sections
    01:39
  • Data Sets in Python
    03:23
  • Data at a Glance
    05:53
  • A Note on Our Usage of Terms with Multiple Meanings
    03:27
  • ARTICLE - A Brief Overview of Regression Analysis
    01:50
  • Picking the Appropriate Approach for the Task at Hand
    02:17
  • Removing Irrelevant Data
    06:27
  • EXERCISE - Removing Irrelevant Data
    00:25
  • SOLUTION - Removing Irrelevant Data
    00:01
  • Examining the Reasons for Absence
    05:04
  • Splitting a Column into Multiple Dummies
    08:37
  • EXERCISE - Splitting a Column into Multiple Dummies
    00:04
  • SOLUTION - Splitting a Column into Multiple Dummies
    00:00
  • ARTICLE - Dummy Variables: Reasoning
    01:32
  • Dummy Variables and Their Statistical Importance
    01:28
  • Grouping - Transforming Dummy Variables into Categorical Variables
    08:35
  • Concatenating Columns in Python
    04:35
  • EXERCISE - Concatenating Columns in Python
    00:04
  • SOLUTION - Concatenating Columns in Python
    00:01
  • Changing Column Order in Pandas DataFrame
    01:43
  • EXERCISE - Changing Column Order in Pandas DataFrame
    00:06
  • SOLUTION - Changing Column Order in Pandas DataFrame
    00:12
  • Implementing Checkpoints in Coding
    02:52
  • EXERCISE - Implementing Checkpoints in Coding
    00:04
  • SOLUTION - Implementing Checkpoint in Coding
    00:00
  • Exploring the Initial "Date" Column
    07:48
  • Using the "Date" Column to Extract the Appropriate Month Value
    07:00
  • Introducing "Day of the Week"
    03:36
  • EXERCISE - Removing Columns
    00:37
  • Further Analysis of the DataFrame: Next 5 Columns
    03:17
  • Further Analysis of the DaraFrame: "Education", "Children", "Pets"
    04:38
  • A Final Note on Preprocessing
    01:59
  • A Note on Exporting Your Data as a *.csv File
    00:26

  • Preview03:20
  • Creating the Targets for the Logistic Regression
    06:32
  • Selecting the Inputs
    02:41
  • A Bit of Statistical Preprocessing
    03:26
  • Train-test Split of the Data
    06:12
  • Training the Model and Assessing its Accuracy
    05:39
  • Extracting the Intercept and Coefficients from a Logistic Regression
    05:16
  • Interpreting the Logistic Regression Coefficients
    06:14
  • Omitting the dummy variables from the Standardization
    04:12
  • Interpreting the Important Predictors
    05:10
  • Simplifying the Model (Backward Elimination)
    04:02
  • Testing the Machine Learning Model
    04:43
  • How to Save the Machine Learning Model and Prepare it for Future Deployment
    04:06
  • ARTICLE - More about 'pickling'
    01:13
  • EXERCISE - Saving the Model (and Scaler)
    00:13
  • Creating a Module for Later Use of the Model
    04:04

  • Installing MySQL
    09:56
  • Installing MySQL on macOS and Unix systems
    01:24
  • Setting Up a Connection
    02:34
  • Introduction to the MySQL Interface
    05:09

  • Are you sure you're all set?
    00:13
  • Implementing the 'absenteeism_module' - Part I
    03:50
  • Implementing the 'absenteeism_module' - Part II
    06:23
  • Creating a Database in MySQL
    06:37
  • Importing and Installing 'pymysql'
    02:44
  • Creating a Connection and Cursor
    02:54
  • EXERCISE - Create 'df_new_obs'
    00:10
  • Creating the 'predicted_outputs' table in MySQL
    04:52
  • Running an SQL SELECT Statement from Python
    03:04
  • Transferring Data from Jupyter to Workbench - Part I
    06:15
  • Transferring Data from Jupyter to Workbench - Part II
    06:35
  • Transferring Data from Jupyter to Workbench - Part III
    02:45

  • EXERCISE - Age vs Probability
    00:14
  • Analysis in Tableau: Age vs Probability
    08:49
  • EXERCISE - Reasons vs Probability
    00:14
  • Analysis in Tableau: Reasons vs Probability
    07:49
  • EXERCISE - Transportation Expense vs Probability
    00:22
  • Analysis in Tableau: Transportation Expense vs Probability
    06:00

  • Bonus Lecture: Next Steps
    00:39

Instructor

365 Careers
Creating opportunities for Business & Finance students
365 Careers
  • 4.5 Instructor Rating
  • 393,356 Reviews
  • 1,345,897 Students
  • 70 Courses

365 Careers is the #1 best-selling provider of finance courses on Udemy. The company’s courses have been taken by more than 1,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings.  

Currently, the firm focuses on the following topics on Udemy:  

1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA

2) Data science – 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, Power BI, Credit Risk Modeling, and Credit Analytics

3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing

4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook

5) Blockchain for Business

All of the company’s courses are:  

Pre-scripted  

Hands-on  

Laser-focused  

Engaging  

Real-life tested  

By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.  

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start. 

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