SQL + Tableau + Python
Master the integration of SQL, Python, and Tableau for effective data analysis
$99.00
Lifetime access
What you get:
- 5 hours of content
- 10 Interactive exercises
- 33 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
SQL + Tableau + Python
$99.00
Lifetime access
What you get:
- 5 hours of content
- 10 Interactive exercises
- 33 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
$99.00
Lifetime access
$99.00
Lifetime access
What you get:
- 5 hours of content
- 10 Interactive exercises
- 33 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Master the integration of SQL, Python, and Tableau for effective data analysis
- Use SQL, Tableau, and Python to respond to real-life business questions
- Learn how to seamlessly transfer data from Python to SQL, creating the structure necessary for connecting two compatible software tools
- Gain hands-on experience with Tableau to create compelling data visualizations and impactful analysis
- Apply machine learning techniques to solve specific business problems
- Enhance your resume with three essential tech stack components for aspiring data analysts
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
While Python is the leading programming language for data science, SQL is unmatched when it comes to relational database management. Tableau, on the other hand, is a leading business intelligence software, providing tools for quick computations and rich visualizations. This course will show you how to combine these software products to solve real-life business problems.
Learn for Free
1.1 Course Introduction
3 min
1.2 Properties and Definitions: Data, Servers, Clients, Requests, and Responses
5 min
1.4 Properties and Definitions: Data Connectivity, APIs, and Endpoint
7 min
1.6 Further Details on APIs
8 min
1.8 Text Files as Means of Communication
4 min
1.10 Definitions and Applications
5 min
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 2. What's Next in the Course?3 Lessons 10 Min
In this short section, we introduce the business problem to be solved and outline the task we’ll need to solve in the lessons to come: predict the probability that an individual will be absent from work on a specific day.
Up Ahead4 minReal-Life Example: Absenteeism at Work3 minReal-Life Example: The Dataset3 min - 3. Preprocessing the 'Absenteeism_data'23 Lessons 94 Min
In this section, we will focus on cleaning and preprocessing an entire dataset. If you already are a Python guru and cleaning data sets comes as a second nature, you may wish to skip this section. But if it is possible that you have gaps in your Python mastery, even if it’s here and there, it is essential that you go through every lecture. We are actually coding all the time in this section, so you’ll quite likely end up having a lot of fun.
What to Expect from the Next Couple of Sections Read now3 minimporting the Dataset in Python3 minEyeballing the Data6 minIntroduction to Terms with Multiple Meanings3 minA Refresher on Regression Analysis Read now3 minAn Analytical Approach to Solving the Task2 minDropping the "ID" Column6 minAnalysis of the "Reason for Absence" Column5 minConverting a Feature into Multiple Dummy Variables9 minDropping a Dummy Variable Read now3 minWorking with Dummy Variables from a Statistical Perspective1 minGrouping the Various Reasons for Absence9 minConcatenating Column Values5 minReordering Columns2 minCreating Checkpoints in Jupyter3 minWorking on the "Date" Column8 minExtracting the Month Value7 minCreating the "Day of the Week" Column4 minDropping the "Date" Column Read now1 minModifying "Education" and Discussing "Children" and "Pets"5 minAnalyzing the Next 5 Columns in our DataFrame3 minFinal Remarks on the Data Preprocessing Part of the Exercise2 minA Note on Exporting Your Data as a *.csv File Read now1 min - 4. Machine Learning15 Lessons 67 Min
This section is at the core of this Absenteeism Exercise. Here, we discuss modern machine learning tools that can be used to solve problems like the one we’re looking at. Every step requires you to use Python, so stretch your coding fingers and let’s get to it!
Exploring the Problem from a Machine Learning Point of View3 minCreating the Targets for the Logistic Regression7 minSelecting the Inputs for the Regression3 minStandardizing the Dataset for Better Results3 minTrain-test Split of the Data6 minTraining and Evaluating the Model6 minExtracting the Intercept and Coefficients5 minInterpreting the Coefficients6 minCreating a Custom Scaler to Standardize Only Numerical Features4 minInterpreting the Important Predictors5 minSimplifying the Model (Backward Elimination)4 minTesting the Machine Learning Model5 minSaving theLogistic Regression Model4 minMore about 'pickling' Read now2 minCreating a Module for Later Use of the Model4 min - 5. Connecting Python and SQL11 Lessons 47 Min
In this section, you see software integration applied in practice. You will not only be given the chance to experience how data can be transferred from Python to SQL firsthand, but you will also learn about the structure necessary for connecting two compatible software tools. Finally, we will export the dataset in the form of a *.csv file that’s ready to be used in Tableau.
Downloading the Section Resources Read now1 minLoading the "abssenteeism_module"4 minWorking with the "absenteeism_module"6 minCreating a Database Structure in MySQL6 minInstalling and Importing 'pymysql'3 minSetting up a Connection and Creating a Cursor3 minCreating the 'predicted_outputs' table in MySQL5 minExecuting and SQL Query from Python3 minMoving Data from Python to SQL - Part I6 minMoving Data from Python to SQL - Part II7 minMoving Data from Python to SQL - Part III3 min - 6. Analyzing the Obtained Data in Tableau4 Lessons 24 Min
In the last section of this course, we focus on the analytical part of the absenteeism task. We will load, analyze, and visualize in Tableau the data obtained in the previous sections.
Tableau Analysis: Age vs Probability - Homework and Dataset Read now1 minTableau Analysis: Age vs Probability9 minTableau Analysis: Reasons vs Probability8 minTableau Analysis: Transportation Expense vs Probability6 min
Topics
Course Requirements
- A solid understanding of relational database theory and basic SQL is required. It is highly recommended to start with the SQL course first
- An introduction to Tableau is strongly recommended
- An introduction to Machine Learning in Python is strongly recommended
- You will need to install MySQL Workbench
- You will need to install Tableau Public
Who Should Take This Course?
Level of difficulty: Advanced
- Aspiring data analysts, data scientists, data engineers who want to improve their job prospects
- Existing data analysts, data scientists, data engineers who want to become proficient in SQL, Tableau, and Python
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
Meet Your Instructor
Martin began working with 365 in 2016 as the company’s second employee. Martin’s resilience, hard-working attitude, attention to detail, and excellent teaching style played an instrumental role in 365’s early days. He authored some of the firm’s most successful courses. And besides teaching, Martin dreams about becoming an actor. In September 2021, he enrolled in an acting school in Paris, France.
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