COURSE UNIT TITLE

: PYTHON FOR DATA SCIENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
DSM 5004 PYTHON FOR DATA SCIENCE ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR SEDAT ÇAPAR

Offered to

Data Science
Data Science (Non-Thesis-Evening)

Course Objective

The objective of this course is to teach the fundamentals of the Python programming language and the necessary libraries for data science.

Learning Outcomes of the Course Unit

1   Design, code and test Python programs.
2   Develop programs with modular structure based on functions.
3   Do fundamental statistical analysis using Python functions.
4   Use Python packages for data science applications.
5   Use functions for data visualization and graphics.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Installing Python, Python tutorial, Python Editors, Jupyter notebook
2 Software development process Python syntax: names, expressions, assignment, statements, input, output, definite loops
3 Data types, Math library, Conditional execution
4 String processing, string methods, Lists, list methods. Input/output as string manipulation. Files and file processing.
5 Boolean expressions. Decision structures, conditions, multi-way decisions. Exceptions and exception handling.
6 Functions, function calls. Parameter passing.
7 Functions, Local variables, scope of variables.
8 Loops, Definite and indefinite loops
9 Multidimensional arrays for vectorized operations.
10 Graphics, graphics library
11 Mathematical and statistical functions
12 Operations and function applications on pandas objects
13 Python packages for data science applications.
14 Student presentations

Recomended or Required Reading

1. Downey, A. (2016). Think Python. O'Reilly.
2. Wes McKinney. (2013). Python for Data Analysis. O'Reilly.
3. VanderPlas, J. (2016). Python data science handbook: essential tools for working with data. O'Reilly.
Materials: Lecture Slides

Planned Learning Activities and Teaching Methods

The course consists of lecture and projects.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.40 + FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homework/presentation.

Language of Instruction

Turkish

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 graduate policy at http://www.fbe.deu.edu.tr

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
phone: +90 232 301 86 04

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 40 40
Preparation for final exam 1 45 45
Preparing assignments 1 25 25
Preparing presentations 1 25 25
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 195

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.15455
LO.2555
LO.35555
LO.455555
LO.5555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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