COURSE UNIT TITLE

: COMPUTATIONAL TOOLS FOR STATISTICS I

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 1051 COMPUTATIONAL TOOLS FOR STATISTICS I COMPULSORY 2 0 0 4

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
Statistics(Evening)

Course Objective

To make students to learn fundamental concepts of statistical packages and R, to perform fundamental statistical analysis using statistical packages and R.

Learning Outcomes of the Course Unit

1   Able to use some statistical computation tools
2   Able to enter and manipulate data
3   Calculating descriptive statistics
4   Making summary tables and graphics
5   Using basic R functions

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to statistical computation tools
2 Basic concepts of statistical packages, data entry
3 Data structures in R
4 Data manipulation
5 Computing with R and statistical packages
6 Combination, permutation
7 Constructing frequency tables, descriptive statistics
8 Numerical Measures of Central Tendency
9 Numerical Measures of Variability
10 Graphs for Qualitative Data
11 Graphs for Quantitative Data
12 Graphs for Quantitative Data
13 Random data generation, Random sample
14 Random data generation, Random sample

Recomended or Required Reading

Textbook(s):
1.Braun, J., & Murdoch, D. J. (2016). A first course in statistical programming with R. 2nd Ed., Cambridge University Press.
2.Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Performing data analysis using IBM SPSS. John Wiley & Sons.
Supplementary Book(s):
1.Dalgaard, P. (2008). Introductory statistics with R. Springer Science & Business Media.
2.Kabacoff, R. I. (2015). R in action: data analysis and graphics with R. Simon and Schuster.
3.Long, J. D., & Teetor, P. (2019). R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. O'Reilly Media.
4.Cebeci, Zeynel. (2020). Veri Biliminde R ile Veri Önişleme. Nobel Akademik Yayıncılık.

Planned Learning Activities and Teaching Methods

Lecture, presentation, computer exercises, problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams.

Language of Instruction

English

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at https://fen.deu.edu.tr/

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
Tel: 0232 301 86 04
e-mail: sedat.capar@deu.edu.tr
Tel: 0232 301 86 01

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 23 23
Preparation for final exam 1 27 27
Preparing assignments 1 4 4
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 100

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.155
LO.255
LO.355
LO.455
LO.555