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CP1045 - Visualization, ETL & Modeling
In this course, students will immerse themselves in the pivotal components of data analytics, with a key focus on Extract, Transform, and Load (ETL) operations, utilizing a programming language such as Python or R and popular libraries such as pandas, seaborn, matplotlib and sci-kit-learn.
The course also provides an in-depth exploration of data query optimization and analysis techniques. Real-world examples integrated throughout the course will reinforce their understanding and application of data modeling and optimization concepts. Moreover, students will learn to craft compelling data visualizations through programming. They will grasp basic plotting techniques and how to represent data in various forms such as histograms, bar charts, scatterplots, and more. The course also covers the customization of these plots, fostering a more engaging and informative data presentation. Emphasis will be placed on data cleaning techniques to ensure high accuracy in analysis. This includes handling missing data, data transformation, and data aggregation, all performed via programming. Students will also delve into specific analysis techniques such as time-series analysis, linear regression modeling, and making predictions with multiple regression models.
Upon completion of this course, students will have developed a comprehensive set of skills in programming for data modeling. This will significantly enhance their capabilities in data analysis and decision-making, enabling them to create robust data models.
This course is offered in the following programs:
Data Analytics
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