Dr Kasım Zor

EEE356 - Data Analytics [R] (2025-2026 Spring)

​

Main Course

• Venue: D203, M2 Building

• Date&Time: 09:45-12:00 on Thursdays

• 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)

• 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.​

• Course Documents

  • Course Introduction and Scope

  • Introduction to Data Analytics & Quarto Markdown & The Joy of Stats ​

  • Hands-on Exercise: Case Study for Students (Deadline: February 25 and March 4, 2025)

  • Hands-on Exercise: Q2 (a, b, c, and d) (Deadline: March 11 and 18, 2025)

  • Hands-on Exercise: Q2 (e, f, g, and h) (Deadline: )

  • Hands-on Exercise: Q3 (Deadline: )

  • Hands-on Exercise: Q2 (a, b, c, d, and e) (Deadline: )

  • Hands-on Exercise: Q3 and Q4 (Deadline: )

​​

Laboratory

• Venue: Computer and Networks Lab, M3 Building

• Date&Time: 15:15-17:00 on Wednesdays

• Lab Documents

  • ​RStudio Installation

  • Introduction to RStudio (Reference: Prof. Trevor Hastie, Statistical Learning)

  • Joining EEE356 Team in DataCamp Classrooms

  • ​DataCamp Classrooms: Introduction to R​​​​

  • DataCamp Classrooms: Introduction to the Tidyverse​​

  • DataCamp Classrooms: Introduction to Data Visualisation with ggplot2

  • DataCamp Classrooms: Exploratory Data Analysis with R

 

Exams

• Week 9 - Midterm Examination: (2025) (2024) (2021)

• Week 16 - Final Examination: (2025) (2024) (2021)​​

​

Announcements

​The students are responsible to prepare a project presentation regarding data analytics by paying attention to the followings:

1. Deadline: 20 May 2026 Wednesday before 23:59 (The presentations must be delivered till the deadline by e-mail [kasimzor@yahoo.com])

2. Data set category can be freely chosen by the students according to their interests. But the data set must at least conform the following properties:

   • Resolution (Temporal Granularity) <= 1-h

   • Duration <= 1-y

   • Number of Features <= 5

3. Aim: One-step ahead forecasting by using Multiple Linear Regression (MLR) [Source] [Video]

4. Performance and Error Metrics: R-squared (%), MAE (Unit), MAPE (%), and RMSE (Unit).