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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS EMT 2014 STATISTICAL ROGRAMMING COMPULSORY 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR IPEK DEVECI KOCAKOÇ

Offered to

Econometrics (Evening) Econometrics

Course Objective

The aim of this course is to make the student write code by using Python programming language for statistical analysis.

Learning Outcomes of the Course Unit

1   To be able to write code for data analysis by using Python 2   To be able to connect to databases by using Python 3   To be able to use loops and functions in codes. 4   To be able to visualize data by using Python 5   To be able to create and use packages in Python

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 1 Introduction 2 2 Python libraries for statistical analysis 3 3 numpy 4 4 numpy 5 5 quiz 6 6 pandas 7 7 pandas 8 8 quiz 9 9 Mid-Term 10 10 pandas 11 11 visualization 12 12 visualization 13 13 basic statistical methods 14 14 basic statistical methods 15 15 applications

Recomended or Required Reading

Herkes için Python - bülent çobanoğlu

Planned Learning Activities and Teaching Methods

This course will be presented using class lectures, class discussions, overhead projections, and demonstrations.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 STT TERM WORK (SEMESTER) 2 MTE MIDTERM EXAM 3 MTEG MIDTERM GRADE STT * 0.40 + MTE * 0.60 4 FIN FINAL EXAM 5 FCG FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 6 RST RESIT 7 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof.Dr.Ipek Deveci Kocakoç ipek.deveci@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Make a plan 12 3 36 Preparation for midterm exam 1 17 17 Preparation for final exam 1 24 24 Midterm 1 1 1 Final 1 1 1 TOTAL WORKLOAD (hours) 121

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 1 LO.2 1 LO.3 1 LO.4 1 LO.5 1