School/Faculty/Institute |
Faculty of Engineering |
Course Code |
IE 332 |
Course Title in English |
Exploratory Data Analytics |
Course Title in Turkish |
Keşifsel Veri Analizi |
Language of Instruction |
EN |
Type of Course |
Flipped Classroom,Lecture |
Level of Course |
Advanced |
Semester |
Spring |
Contact Hours per Week |
Lecture: 3 |
Recitation: none |
Lab: none |
Other: none |
|
Estimated Student Workload |
152 hours per semester |
Number of Credits |
6 ECTS |
Grading Mode |
Standard Letter Grade
|
Pre-requisites |
None |
Expected Prior Knowledge |
Basic principles of computational methods and introductory level probability/statistics |
Co-requisites |
None |
Registration Restrictions |
none |
Overall Educational Objective |
To learn the basics of data analytics process with on hands applications using modern tools to explore data by summarizing, slicing/dicing and analyzing data via graphical and quantitative tools. |
Course Description |
The aim of the course is to give the fundamentals of exploratory data analytics. Exploratory data analytics focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, to consider how that data set came into existence, and to decide how it can be investigated with more formal statistical methods. |
Course Description in Turkish |
Bu ders veri analitiğinin temellerini inceleyen bir ders olarak tasarlanmıştır. Araştırma amaçlı veri analitiği verinin altında yatan yapılanmayı anlamaya, veri seti hakkında sezgi geliştirmeye, verinin nasıl ortaya çıkıp, nasıl hazırlandığını düşünmeye ve istatistiki metotlarla nasıl derinlemesine incelenebileceğine odaklanır.
|
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) summarize data using statistical methods;
2) draw conclusions and/or make predictions using data analytics;
3) apply linear regression models;
4) understand recent analytical trends such as text mining, recommendation systems;
5) communicate data analysis results in a clear and concise manner in written and oral form.
|
Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
5 |
1) Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
|
|
|
|
|
2) Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
|
|
|
|
|
3) Displays a sound comprehension of microeconomic and macroeconomic theory.
|
|
|
|
|
|
4) Applies economic concepts to solve complex problems and enhance decision-making capability. |
|
|
|
|
|
5) Uses quantitative techniques to analyze different economic systems.
|
|
|
|
|
|
6) Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
|
|
|
|
|
7) Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
|
|
|
|
|
8) Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
|
|
|
|
|
|
9) Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
|
|
|
|
|
10) Exhibits individual and professional ethical behavior and social responsibility. |
|
|
|
|
|
11) Displays learning skills necessary for further study with a high degree of autonomy |
|
|
|
|
|
Relation to Program Outcomes and Competences
N None |
S Supportive |
H Highly Related |
|
|
|
|
Program Outcomes and Competences |
Level |
Assessed by |
1) |
Has a broad understanding of economics with a deep exposure to other social sciences and mathematics. |
N |
|
2) |
Demonstrates knowledge and skills in understanding the interactions of different areas of economics. |
N |
|
3) |
Displays a sound comprehension of microeconomic and macroeconomic theory.
|
N |
|
4) |
Applies economic concepts to solve complex problems and enhance decision-making capability. |
N |
|
5) |
Uses quantitative techniques to analyze different economic systems.
|
N |
|
6) |
Applies theoretical knowledge to analyze issues regarding Turkish and global economies. |
N |
|
7) |
Demonstrates proficiency in statistical tools and mainstream software programs to process and evaluate economic data. |
N |
|
8) |
Behaves according to scientific and ethical values at all stages of economic analysis: data collection, interpretation and dissemination of findings.
|
N |
|
9) |
Uses written and spoken English effectively (at least CEFR B2 level) to exchange scientific information. |
N |
|
10) |
Exhibits individual and professional ethical behavior and social responsibility. |
N |
|
11) |
Displays learning skills necessary for further study with a high degree of autonomy |
N |
|
Prepared by and Date |
ÖZGÜR ÖZLÜK , September 2019 |
Course Coordinator |
SEMRA AĞRALI |
Semester |
Spring |
Name of Instructor |
Öğr. Gör. HANDE OZOGUZ |
Course Contents
Week |
Subject |
1) |
Introduction to Data Science |
2) |
Basics of R |
3) |
Basics of R |
4) |
Data Analysis (one variable) |
5) |
Data Analysis (two variable) |
6) |
Data Analysis (multi variable) |
7) |
Linear Regression |
8) |
Linear Regression |
9) |
Linear Regression |
10) |
Linear Regression |
11) |
Image Processing Basics |
12) |
Text Mining Basics |
13) |
Search Engines Basics |
14) |
Recommendation Engines Basics |
15) |
Final Exam/Project/Presentation period |
16) |
Final Exam/Project/Presentation period |
Required/Recommended Readings | none |
Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique |
Homework and Projects | Students are required to complete a portfolio to be able to enter the final exam |
Laboratory Work | Students will apply the methods they learned using R at the laboratory hours |
Computer Use | Students will apply the methods they learned using R at the laboratory hours |
Other Activities | none |
Assessment Methods |
Assessment Tools |
Count |
Weight |
Quiz(zes) |
3 |
% 36 |
Homework Assignments |
12 |
% 20 |
Project |
3 |
% 44 |
TOTAL |
% 100 |
|
Course Administration |
ozluko@mef.edu.tr
none
Instructor’s office and phone number: 5th Floor
office hours: Thursdays 16:20-17:20
email address: ozluko@mef.edu.tr
Rules for attendance: none
Rules for late submission of assignments: It will be discounted 20/100 by each delayed day.
Rules for missing a midterm: Provided that proper justification evidence is presented, each missed midterm exam will be given the grade of the final exam. There will be no make-up exams
Missing a final: Faculty regulations
A reminder of proper classroom behavior, code of student conduct: YÖK Regulations
Statement on plagiarism: YÖK Regulations
|