Shruti P.
After 365:
Sr. Business Intelligence Engineer | Child Care Resource Center (USA)
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Online Course
free
SQL + Tableau + Python
with
Martin Ganchev
Master the integration of SQL, Python, and Tableau for effective data analysis
8356 students already have enrolled
Skill level:
Advanced
Duration:
5 hours
-
Lessons (5 hours)
-
Practice exams (40 minutes)
- Lessons (5 hours)
- Practice exams (40 minutes)
CPE credits:
6.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master integrating SQL, Python, and Tableau for data analysis.
- Use these tools to answer real-life business questions.
- Transfer data from Python to SQL for seamless integration.
- Create compelling Tableau visualizations and analyses.
- Apply machine learning to solve business problems.
Topics & tools
pythondata analysistableauprogrammingdata processingmachine learningtheorydata visualizationSQLmachine and deep learningdata preprocessing
Your instructor
Content Creator & Content Manager at 365 Data Science | Data Science Instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Economic and Social Sciences
Course OVERVIEW
Description
CPE Credits: 6.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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.
Prerequisites
- Any SQL environment (such as SQLite, MySQL Workbench, or an online SQL editor).
- Tableau Desktop or Tableau Public
- Python (any recent version, such as Python 3.8 or later) and Spyder IDE (included in the free Anaconda distribution)
Advanced preparation
Curriculum
62 lessons
10 exercises
3 exams
- 2. What's Next in the Course?10 MinIn 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.
- 3. Preprocessing the 'Absenteeism_data'94 MinIn 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 Freeimporting the Dataset in Python FreeEyeballing the Data FreeIntroduction to Terms with Multiple Meanings FreeA Refresher on Regression Analysis FreeAn Analytical Approach to Solving the Task FreeDropping the "ID" Column FreeAnalysis of the "Reason for Absence" Column FreeConverting a Feature into Multiple Dummy Variables FreeDropping a Dummy Variable FreeWorking with Dummy Variables from a Statistical Perspective FreeGrouping the Various Reasons for Absence FreeConcatenating Column Values FreeReordering Columns FreeCreating Checkpoints in Jupyter FreeWorking on the "Date" Column FreeExtracting the Month Value FreeCreating the "Day of the Week" Column FreeDropping the "Date" Column FreeModifying "Education" and Discussing "Children" and "Pets" FreeAnalyzing the Next 5 Columns in our DataFrame FreeFinal Remarks on the Data Preprocessing Part of the Exercise FreeA Note on Exporting Your Data as a *.csv File Free
- 4. Machine Learning67 MinThis 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 View FreeCreating the Targets for the Logistic Regression FreeSelecting the Inputs for the Regression FreeStandardizing the Dataset for Better Results FreeTrain-test Split of the Data FreeTraining and Evaluating the Model FreeExtracting the Intercept and Coefficients FreeInterpreting the Coefficients FreeCreating a Custom Scaler to Standardize Only Numerical Features FreeInterpreting the Important Predictors FreeSimplifying the Model (Backward Elimination) FreeTesting the Machine Learning Model FreeSaving theLogistic Regression Model FreeMore about 'pickling' FreeCreating a Module for Later Use of the Model FreePractice exam Free
- 5. Connecting Python and SQL47 MinIn 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 FreeLoading the "abssenteeism_module" FreeWorking with the "absenteeism_module" FreeCreating a Database Structure in MySQL FreeInstalling and Importing 'pymysql' FreeSetting up a Connection and Creating a Cursor FreeCreating the 'predicted_outputs' table in MySQL FreeExecuting and SQL Query from Python FreeMoving Data from Python to SQL - Part I FreeMoving Data from Python to SQL - Part II FreeMoving Data from Python to SQL - Part III Free
- 6. Analyzing the Obtained Data in Tableau24 MinIn 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.
- 7. Course exam60 Min
Free preview
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
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