EEE356 - Data Analytics [R] (2023-2024 Spring)
Main Course
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Venue: D204, M2 Building
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Date&Time: 09:45-12:00 on Mondays
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Objectives: This course aims to gain students insight and required skills related to data analytics containing R Programming, data wrangling, data visualisation, exploratory data analysis, and approaches to missing data.
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Textbook:
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Contents: Introduction to Data Analytics, Introduction to R Programming Language, Data Structures, Control Structures, Functions, Introduction to Data Visualisation, Data Transformation, Data Wrangling, Data Visualisation (ggplot2, Layers, Scales, and The Grammar), Exploratory Data Analysis, and Approaches to Missing Data.
Laboratory
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Venue: Computer Lab, M1 Building
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Date&Time: 12:30-15:15 on Wednesdays (Two Groups)
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Group 1 - 12:30-13:45
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Group 2 - 14:00-15:15
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Platform: DataCamp Classrooms
Exams
Lecture Documents
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Hands-on Exercise: RStudio
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Hands-on Exercise: Tidyverse
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Introduction to
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Data
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Advanced Data Visualisation
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Advanced Data Transformation
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Advanced Data Transformation
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Advanced Data Import
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Hands-on Exercise: Correlation Map with ggstatsplot
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Hands-on Exercise: Single Imputation with imputeTS
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Homework: Shiny (Recommended Book)
Laboratory Documents
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Introduction to RStudio (Reference: Prof. Trevor Hastie, Statistical Learning)
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DataCamp Classrooms: Introduction to R
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DataCamp Classrooms: Introduction to the Tidyverse
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DataCamp Classrooms: Introduction to Data Visualisation with ggplot2
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DataCamp Classrooms: Exploratory Data Analysis with R
Announcements