Information Package
baskent.edu.tr

Master's of Technology and Knowledge Management without Thesis

TKM522

DATA SCIENCE

Course Code
TKM522
Semester
-
Theory+Practice
3 + 0
ECTS
10
Course TypeElective Course
Course LevelMaster's Degree Without Thesis
Year of Study-
Semester-
ECTS10
Name of Lecturer(s)
Learning Outcomes

At the end of this course, the students;

  1. They will see the general use of R programming language, functions, conditions, loop structures in R, probability calculation in R, and probability distributions.
  2. They will see the use of some functional libraries and packages in R, data visualization and statistical inference.
  3. They will learn theoretically and practically some methods commonly used in Data Science.
  4. They will learn Parametric and Non-Parametric Staitstical Tests and R applications.
Mode of DeliveryFace to face
PrerequisitesNo
Recommended Optional Programme ComponentsData Analytics
Course DescriptionThis course will emphasize practical techniques for working with large-scale date. Specific topics covered will include statistical modeling and machine learning, data pipelines, programming languages and real world topics and case studies. The use of R programming language will be used.
Course Contents
WEEK TOPICS
1st Week Introduction to R Programming Language, Matrix, Factor, List and Data Frames
2nd Week Data Entry, Function, Loop, Condition Structures in R
3rd Week Probability calculation and Probability Distributions and Data Visualization in R
4th Week Descriptive Statistics, Regression and Correlation and R Applications
5th Week Time Series in R
6th Week Cluster Analysis, Discriminant Analysis and R Applications
7th Week Overview of Mid-Term
8th Week Mid-Term Exam
9th Week Parametric and Non-Parameter Statistical Tests and Applications in R - 1
10th Week Parametric and Non-Parameter Statistical Tests and Applications in R - 2
11th Week Parametric and Non-Parameter Statistical Tests and Applications in R - 3
12th Week Parametric and Non-Parameter Statistical Tests and Applications in R - 4
13th Week Presentations
14th Week Projects
Recommended or Required Reading"Univariate, Bivariate, and Multivariate Statistics Using R", Daniel J. Denis, Wiley, 2020.
"İstatistikte R ile Programlama", Necmi Gürsakal, Dora, 2018.
"Introduction to Data Science", Laura Igual, Santi Segui, Springer, 2017.
?Uygulamalı Çok Değişkenli İstatistik Teknikleri?, Ali Sait Albayrak, 2006.
Planned Learning Activities and Teaching Methods
  • Lecture
  • Discussion
  • Case Study
Assessment Methods and Criteria
Quantity Percentage (%)
Mid-term 1 30
Project 1 30
Total(%) 60
Contribution of In-term Studies to Overall Grade(%) 60
Contribution of Final Examination to Overall Grade(%) 40
Total(%) 100
ECTS Allocated Based on Student Workload
Activities Quantity Duration (Hour) Total Workload (Hour)
Midterm exam 1 3 3
Preparation for Quiz
Individual or group work 14 4 56
Preparation for Final exam 1 40 40
Course hours 14 3 42
Preparation for Midterm exam 1 40 40
Laboratory (including preparation)
Final exam 1 3 3
Homework 2 8 16
Project 1 60 60
Quiz 1 35 35
Total Work Load295
Total Work Load / 30 (h)9.83
ECTS Credit of the Course10
Language of InstructionTurkish
Work Placement(s)No

Programme Qualifications / Learning Outcomes

LO1 LO2 LO3 LO4
K1 X
K2 X
K3 X X
K4 X
K5 X
K6 X
K7 X X
K8 X
K9 X
K10 X
K11 X
K12 X X
K13 X