top of page
atu_logo.png

EEE356 - Data Analytics [R] (2023-2024 Spring)

​

Main Course

  • Venue: D204, M2 Building

  • Date&Time: 09:45-12:00 on Mondays

  • 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.

  • Textbook:

    • Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, R for Data Science, 2nd Ed., O'Reilly​, 2023. (English, Turkish)

  • Reference Books:

    • Rafael A. Irizarry, Introduction to Data Science, 1st Ed., CRC Press, 2023. (English)

    • Zeynel Cebeci, Veri Biliminde R ile Veri ÖniÅŸleme, 1st Ed., Nobel, 2020.

    • Özgür Ergül, Guide to Programming and Algorithms Using R, 1st Ed., Springer, 2013.

  • 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

  • Venue: Computer Lab, M1 Building

  • Date&Time: 12:30-15:15 on Wednesdays (Two Groups)

    • Group 1 - 12:30-13:45​

    • Group 2 - 14:00-15:15

  • Platform: DataCamp Classrooms

 

Exams

  • Week 9 - Midterm Examination: 2021

  • Week 16 - Final Examination: 2021

​

Lecture Documents

  1. Course Introduction and Scope

  2. Introduction to Data Analytics

  3. Hands-on Exercise: RStudio

  4. Hands-on Exercise: Tidyverse

  5. Introduction to

    1. Quarto Markdown

    2. Data Visualisation (The Joy of Stats)

  6. Data Transformation

  7. Data

    1. Tidying​

    2. Import

​​

Laboratory Documents​

Lab 7 - 03.04.2024

​

Announcements

​

----------------------------------------------------------------------------------------------------------------------------------

2020-2021 Spring Documents

​

Lecture Slides or Notes

  1. Course Introduction and Scope

  2. Introduction to Data Analytics

  3. Introduction to R Programming Language and Data Structures - Lab Files

  4. Control Structures and Functions - Lab Slides - Lab Files

  5. Introduction to Data Visualisation - The Joy of Stats - Lab Files

  6. Data Transformation - Lab Files

  7. Data Wrangling (Introduction, Tibbles, Data Import, and Tidy Data) - Lab Files

  8. Data Wrangling (Relational Data, Strings, Factors, and Dates&Times) - Lab Files

  9. Midterm Exam

  10. Data Visualisation: ggplot2 and Layers - Lab Files

  11. Data Visualisation: Scales - Lab Files

  12. Data Visualisation: The Grammar - Lab Files - R Markdown for Presentations

  13. Exploratory Data Analysis - Lab Files

  14. Approaches to Missing Data:

    1. imputeTS: Article and Cheat Sheet​

    2. naniar

    3. VIM

  15. Paper Presentations

    • Presentations shall be sent to kzor@atu.edu.tr till 17:00 on 28 May 2021.

    • Presentation duration is determined as at least 10 minutes and at most 20 minutes per student.

    • Presentations shall be prepared as beamer presentations via using R Markdown.

  16. Final Exam

​

Assignment (2020-2021 Spring) [Weight: 50% of Final Exam]

bottom of page