Data Science for Industrial Engineers

Course Code:

ISE 412

Course Period:

Autumn

Course Type:

Core

Credits:

3

Theoric:

3

Practice:

0

Laboratory Hour:

0

ECTS:

5

Prerequisite Courses:

Statistical Applications for Industrial Engineers

Computational Methods for Industrial Engineers

Course Language:

English

Courses given by:

Emre Çelebi

Course Objectives:

The aim of this course is to examine the interaction of statistics, data analysis and machine learning with modern data science tools and to include the process of data science from data collection to the development of learning algorithms.

Course Content:

Topics covered include: Introduction to programming with R; data manipulation and missing data analysis; exploratory data analysis and data visualization; supervised and unsupervised machine learning.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Lab, 4: Case study

Course Evaluation Methods:

A: Exam, B: Experiment, C: Homework, D: Project

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Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
1) Students will be able to program the modern R scripting language.	1a, 4b, 6c, 8a	1,3	A,D
2) Specialize in data cleaning and data editing.	4a,4b	1,3	A,D
3) Using explorer graphics, can access sensitive information about data sets and interpret the results.	4b,5b	1,3	A,D
4) Reach a wide range of information about statistical analysis.	5b	1,3	A,D
5) Students will be able to use the algorithms and methods that are studied in theoretical details in modern data processing systems in accordance with industrial standards.	1b, 2a	1,3	A,D
6) In the Science of Data, it can dominate the process from data collection to the development of a learning algorithm.	1b, 2b, 5a	1,3	A,D
7) Students will be able to apply data analysis and machine learning methods to real life problems during the course.	4a	1,3	A,D

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	INTRODUCTION TO DATA SCIENCE WITH R	R Programming for Data Science, Chapter 1- Chapter 2- Chapter 3, R for Data Science, Grolemund and Wickham, Chapter 2 Lecture Notes
2	R PROGRAMMING - DATA IMPORTING - DATA TYPES	R Programming for Data Science, Chapter 4- Chapter 5 - Chapter 9 - Chapter 10 R for Data Science, Grolemund and Wickham, Chapter 11 Lecture Notes
3	R PROGRAMMING - DATA IMPORTING - DATA TYPES	R Programming for Data Science, Chapter 4- Chapter 5 - Chapter 9 - Chapter 10 R for Data Science, Grolemund and Wickham, Chapter 11 Lecture Notes
4	DATA MANIPULATION	R Programming for Data Science, Chapter 13 - Chapter 16 Case Study, Lecture Notes
5	DATA CLEANING AND MISSING DATA ENTRY	R Programming for Data Science, Chapter 4 - Chapter 9 Case Study, Ders Notları
6	EXPLORATORY DATA ANALYSIS AND DATA VISUALIZATION	Exploratory Data Analysıs Wıth R, Chapter 4 - Chapter 6 - Chapter 14 - Chapter 15 R for Data Science, Grolemund and Wickham, Chapter 7 - Chapter 3 Case Study, Lecture Notes
7	INFERENCIAL STATISTICS	Coursebooks, Lecture Notes, Case Study
8	MIDTERM EXAM 1	Coursebooks Lecture Notes
9	INFERENCIAL STATISTICS	Coursebooks, Lecture Notes, Case Study
10	MACHINE LEARNING	An Introduction To Statistical Learning With Applications In R, Chapter 1 - Chapter 2 Lecture Notes
11	SUPERVISED LEARNING	An Introduction To Statistical Learning With Applications In R, Chapter 3 - Chapter 4 Lecture Notes, Case Study
12	SUPERVISED LEARNING	An Introduction To Statistical Learning With Applications In R, Chapter 8 - Chapter 9 Lecture Notes, Case Study
13	UNSUPERVISED LEARNING	An Introduction To Statistical Learning With Applications In R, Chapter 10 Exploratory Data Analysıs Wıth R, Chapter 4 - Chapter 11 - Chapter 12 Lecture Notes, Case Study
14	UNSUPERVISED LEARNING	An Introduction To Statistical Learning With Applications In R, Chapter 10 Exploratory Data Analysıs Wıth R, Chapter 4 - Chapter 11 - Chapter 12 Lecture Notes, Case Study

Recommended Sources

RECOMMENDED SOURCES

Textbook

R PROGRAMMING FOR DATA SCIENCE, ROGER D. PENG

Additional Resources

R FOR DATA SCIENCE, GARRET GROLEMUND, HADLEY WICKHAM – O’REILLY JANUARY 2017

EXPLORATORY DATA ANALYSIS WITH R, ROGER D. PENG – LEANPUB OCTOBER 2015

PRACTICAL DATA SCIENCE WITH R, NINA ZUMEL

DATA MANIPULATION WITH R, PHIL SPECTOR EFFICIENT R PROGRAMMING, COLIN GILLESPIE

AN INTRODUCTION TO STATISTICAL LEARNING WITH APPLICATIONS IN R, GARETH JAMES, DANIELA WITTEN, TREVOR HASTIE AND ROBERT TIBSHIRANI – SPRINGER JUNE 2014

Material Sharing

MATERIAL SHARING
Documents	Case Study, Laboratory notes, lecture notes
Project	Project proposal and report
Exams	Midterm exam (1) , Final exam

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Midterm	1	50
Project	1	50
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check X
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	X
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	X
2a	Ability to identify, formulate, and solve complex engineering problems,	X
2b	Ability to select and apply proper analysis and modeling methods for this purpose.	X
3a	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.	X
4b	Ability to employ information technologies effectively.	X
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,	X
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	X
6a	Ability to work efficiently in intra-disciplinary teams,
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.	X
7a	Ability to communicate effectively both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,
7d	Ability to make effective presentations,
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,	X
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	Awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (except midterm week, 12x lecture and lab hours)	12	3	36
Hours for off-the-classroom study (Pre-study, practice)	14	4	56
Midterm	1	2	2
Project	1	30	30
Final	1	2	2
Total Work Load			126
Total Work Load / 25 (h)			5.0
ECTS Credit of the Course			5

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