Shruti P.
After 365:
Sr. Business Intelligence Engineer | Child Care Resource Center (USA)
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Online Course
SQL + Tableau + Python
with
Martin Ganchev
Master the integration of SQL, Python, and Tableau for effective data analysis
8,743 students already 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 PreprocessingSql
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.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.Up Ahead FreeReal-Life Example: Absenteeism at Work FreeReal-Life Example: The Dataset FreeExercise Free
- 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.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 Sectionsimporting the Dataset in PythonEyeballing the DataIntroduction to Terms with Multiple MeaningsA Refresher on Regression AnalysisAn Analytical Approach to Solving the TaskDropping the "ID" ColumnAnalysis of the "Reason for Absence" ColumnConverting a Feature into Multiple Dummy VariablesDropping a Dummy VariableWorking with Dummy Variables from a Statistical PerspectiveGrouping the Various Reasons for AbsenceConcatenating Column ValuesReordering ColumnsCreating Checkpoints in JupyterWorking on the "Date" ColumnExtracting the Month ValueCreating the "Day of the Week" ColumnDropping the "Date" ColumnModifying "Education" and Discussing "Children" and "Pets"Analyzing the Next 5 Columns in our DataFrameFinal Remarks on the Data Preprocessing Part of the ExerciseA Note on Exporting Your Data as a *.csv File
- 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!67 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 ViewCreating the Targets for the Logistic RegressionSelecting the Inputs for the RegressionStandardizing the Dataset for Better ResultsTrain-test Split of the DataTraining and Evaluating the ModelExtracting the Intercept and CoefficientsInterpreting the CoefficientsCreating a Custom Scaler to Standardize Only Numerical FeaturesInterpreting the Important PredictorsSimplifying the Model (Backward Elimination)Testing the Machine Learning ModelSaving theLogistic Regression ModelMore about 'pickling'Creating a Module for Later Use of the ModelPractice exam
- 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.47 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 ResourcesLoading the "abssenteeism_module"Working with the "absenteeism_module"Creating a Database Structure in MySQLInstalling and Importing 'pymysql'Setting up a Connection and Creating a CursorCreating the 'predicted_outputs' table in MySQLExecuting and SQL Query from PythonMoving Data from Python to SQL - Part IMoving Data from Python to SQL - Part IIMoving Data from Python to SQL - Part III
- 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.24 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.Tableau Analysis: Age vs Probability - Homework and DatasetTableau Analysis: Age vs ProbabilityTableau Analysis: Reasons vs ProbabilityTableau Analysis: Transportation Expense vs Probability
- 7. Course exam60 min60 minCourse exam
Free lessons
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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96%
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365 Data Science.
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ACCREDITED certificates
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