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Course Outline
Introduction
- Overview of Tableau.
- Fundamentals of Python, R, and SQL.
Getting Started
- Setting up the development environment.
- Understanding software integration.
Data Analysis with Python
- Python fundamentals and programming.
- Importing libraries and datasets.
- Data wrangling.
- Data normalization and formatting.
- Exploratory data analysis.
- Performing regression analysis.
- Model development and evaluation.
- Visualizing Data.
Data Analysis with R
- R fundamentals and programming.
- Preparing data.
- Classifying and working with data in R.
- Using functions.
- Visualizing Data.
Data Analysis with SQL
- Setting up the database.
- Connecting Python and SQL.
- Connecting R and SQL.
- SQL aggregations and joins.
- Querying the database.
- Data manipulation.
Data Visualization Using Tableau
- Tableau design principles for visualization.
- Creating dashboards, charts, and tables.
- Mapping techniques.
- Regressions in R and Tableau.
- Advanced analytics with R and Tableau.
- Practical examples and use cases.
Troubleshooting
Summary and Next Steps
Requirements
- Practical experience with data analysis (e.g., using Excel).
- General understanding of database concepts.
- No prior programming experience is required.
Audience
- Data analysts.
35 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
