CEN429 Introduction to Data Science

6 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Code CEN429
Name Introduction to Data Science
Semester 7. Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Doç. Dr. İLKER ÜNAL


Course Goal

By the end of the course the students will learn the basic tools that they need for data analysis. At the end of the course the students apply these tools and techniques to analyze a real-world problem by using R.

Course Content

This course will cover the topics needed to solve data-science problems, which include data preparation (collection and integration), data characterization and presentation, data analysis (experimentation and observational studies), and data products using R.

Course Precondition

None

Resources

Grolemund, Garrett, and Wickham, Hadley (2017), R for Data Science, O’Reilly.

Notes

Cathy O’Neil and Rachel Schutt. Doing Data Science, Straight Talk From The Frontline, O’Reilly. 2014


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Conduct basic statistical analysis by using R
LO02 Access the data from various sources and formats
LO03 Clean and organize the data for reporting and further analysis
LO04 Explore and visualize the data


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Has capability in the fields of mathematics, science and computer that form the foundations of engineering 3
PLO02 Bilgi - Kuramsal, Olgusal Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, 3
PLO03 Bilgi - Kuramsal, Olgusal Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. 3
PLO04 Bilgi - Kuramsal, Olgusal Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. 2
PLO05 Bilgi - Kuramsal, Olgusal Ability to design and to conduct experiments, to collect data, to analyze and to interpret results 5
PLO06 Bilgi - Kuramsal, Olgusal Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence 2
PLO07 Beceriler - Bilişsel, Uygulamalı Can access information,gains the ability to do resource research and uses information resources 2
PLO08 Beceriler - Bilişsel, Uygulamalı Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability 2
PLO09 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language 2
PLO10 Yetkinlikler - Öğrenme Yetkinliği Professional and ethical responsibility,
PLO11 Yetkinlikler - Öğrenme Yetkinliği Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications,
PLO12 Yetkinlikler - Öğrenme Yetkinliği Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues


Week Plan

Week Topic Preparation Methods
1 Introduction to Data Science Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
2 Statistical Inference and Introduction to R Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
3 Data Visualization Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
4 Data Structures Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
5 Generic Functions in R Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
6 Data handling Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
7 Midterm Overview Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
8 Mid-Term Exam Preparation to exam Ölçme Yöntemleri:
Yazılı Sınav
9 Factors and lists Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
10 Reading and collecting data Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
11 Writing functions Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
12 Descriptive statistics Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
13 Rmarkdown Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
14 Maps and animations Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
15 Final Exam Overview Reading the related chapter in lecture note Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
16 Term Exams Preparation to exam Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Preparation to exam Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS