COURSE UNIT TITLE

: INTRODUCTION TO STATISTICS AND DATA SCIENCE

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MTH 0006 INTRODUCTION TO STATISTICS AND DATA SCIENCE ELECTIVE 2 0 0 2

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ÖZLEM EGE ORUÇ

Offered to

Biology
Chemistry
Computer Science
Statistics
Mathematics
Physics

Course Objective

The aim of this course is to provide students with competencies in statistical methods and basic data science principles.

Learning Outcomes of the Course Unit

1   1. Understand what data science is
2   2. Comprehend data preparation in machine learning and model development,
3   3. Learn supervised machine learning methods, their purposes, and selecting the appropriate model based on the data type,
4   4. Learn unsupervised machine learning methods, their purposes, and selecting the appropriate model based on the data type.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to data science, data preparation in machine learning and model development process.
2 Supervised and unsupervised machine learning methods, their purposes, and selecting the appropriate model based on the type of data.
3 Evaluating models and determining metrics for evaluation criteria.
4 Introduction to basic statistical concepts: parameters, variables, measurements, and statistical methods.
5 Descriptive statistics: describing data, percentiles, and variations.
6 Data visualization: histograms, normal distribution, and the use of graphs.
7 Creating confidence intervals and performing basic hypothesis tests.
8 Exploring and sharing objects (assets) in the SAS platform using the 'SAS Drive' interface.
9 Transferring data to the SAS platform using the 'SAS Manage Data' interface.
10 Developing code using the 'SAS Studio' interface and executing the developed code on the CAS analytics engine.
11 Drag-and-drop data preparation processes using the 'SAS Data Studio' interface.
12 Data visualization, report creation, and viewing reports using the 'SAS Visual Analytics' interface.
13 Creating statistical models and comparing models using the 'SAS Studio' interface.
14 Creating machine learning models using the 'SAS Build Models' interface.
15 Final Exam

Recomended or Required Reading

1. Gareth James , Daniela Witten , Trevor Hastie, Robert Tibshirani . An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st ed. 2013, Corr. 7th printing 2017 Edition

2. Peter Bruce Andrew Bruce Peter Gedeck, Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition, O'Reilly Media; 2nd edition (June 16, 2020)


https://video.sas.com/category/videos/advanced-analytics_

Planned Learning Activities and Teaching Methods

Lecture and problem solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Vize
2 FN Final
3 BNS BNS VZ * 0.40 + FN * 0.60
4 BUT Bütünleme Notu
5 BBN Bütünleme Sonu Başarı Notu VZ * 0.40 + BUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

Exams

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 undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

To be announced.

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 10 1 10
Preparation for midterm exam 1 5 5
Preparation for final exam 1 10 10
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 57

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1444
LO.2444
LO.3444
LO.4

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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