Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
5MIS 321Introduction to Data Science (Programming with R)3+0+03530.11.2023

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Management Information Systems (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course To teach data analysis techniques and applications with R
Course Content The role of data analyst and data scientist, vertical use cases, and business applications of data science.
Data acquisition, methods for evaluating source data, and data transformation and preparation.
Statistical models and methods; prediction vs. description; exploratory data analysis; communication; visualization; data processing, munging and engineering; big data; coding; ethics; asking good questions.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Çiğdem TOPÇU GÜLÖKSÜZ
Asist Prof.Dr. Ali Ulvi ÖZGÜL
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources N. Zumel and J. Mount. Practical Data Science with R, Manning Publications, 2014.
Irizarry R.A. An Introduction to Data Science,CRC Press, 2020.
An Introduction to R, Alex Douglas et al., https://intro2r.com/
Data Science with R, J.S. Saltz & J.M. Stanton (2022), Sage.

Course Category
Mathematics and Basic Sciences %100

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 5 70
Assignments 14 2 28
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 5 144

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Understands basic concepts of data science.
2 Understands the importance and means of data preprocessing.
3 Uses R as a data science tool.
4 Understands the importance of data, data processing and getting information out of it.
5 Obtains, prepares, processes and visualizes data using R.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Examples, data science articulated, history and context, technology landscape
2 Data Science Tools : Introduction to R basics, installing packages
3 R Data Types and reading data in and writing out, vectors, matrices, lists, data frames
4 Control structures and Loops in R Functions and Libraries
5 Control structures and Loops in R Functions and Libraries (Con’t)
6 Data Sources: How to obtain data, transform and manage
7 Midterm Exam
8 Data Preparation with R,Data visualization
9 Statistics with R, random variables
10 Analytics: Topics in statistical modeling: basic concepts, experiment design, pitfalls
11 Linear Models
12 Topics in statistical modeling (Con’t), Regression
13 Exercises with Data Sets
14 Exercises with Data Sets
15 Discussion
16 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
C1 2 1 1 2 1 2 3 3 2 2 5
C2 4 1 1 2 1 2 3 3 2 2 5
C3 5 1 1 4 2 5 3 4 5 3 5
C4 5 1 1 4 2 5 3 4 5 3 5
C5 5 1 1 4 2 5 3 4 5 3 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://obs.ankarabilim.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=50621&curProgID=5813&lang=en