Data Science Applications (MAT425)

Course Code Course Name Semester Theory Practice Lab Credit ECTS
MAT425 Data Science Applications 8 2 0 0 2 4
Admission Requirements
Language of Instruction French
Course Type Elective
Course Level Bachelor Degree
Course Instructor(s) Ayşegül ULUS (Email)
Objective The aim of this course is to introduce mathematical tools and applications that can be used to generate knowledge from data. The aim of this course is to examine the basic statistical concepts that will be used to define the data through case studies.
Content Data Science: Technologies, mathematical tools and technologies. Basic statistical concepts to define the data. Sampling and measurement. Calculations for the sample based on the sample. Inferential statistics. Supervised learning. Regression analysis. Applications from business life.
Course Learning Outcomes To understand the basic concepts and methods of data science and applications to gain the ability to see the solution. To gain the ability to understand the basic methods and applications of data science.
Teaching and Learning Methods Lesson, case studies, data applications and exercises
References Foundations of Data Science: Avrim Blum, John Hopcroft, and Ravindran Kannan

An Introduction to Statical Learning with Applications: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Print the course contents
Theory Topics
Week Weekly Contents
1 Introduction to Data Science Define data with tables and graphs
2 Introduction to Statistical Methodology
3 Sampling and Measurement.
4 Artificial Intelligence and Machine Learning Applications, Case analysis I-Betting Sites
5 Data center, variability, position measurement
6 Statistical Inference: Estimation and Correlation Analysis
7 Midterm exam
8 Introduction to Data Science Computer Technologies, Case study II-Medicine and Biology
9 Case study III-Artificial Intelligence Solutions in Banking
10 Regression Methods
11 Case study IV-Database Formation Process in Banking
12 Case study V: Evaluation of banking sector based data models
13 Case study VI: Data analysis in the field of insurance: how to prepare a motor insurance / house insurance tariff and SAS applications
14 Case study VII: Artificial Intelligence in 50 questions
Practice Topics
Week Weekly Contents
Contribution to Overall Grade
  Number Contribution
Contribution of in-term studies to overall grade 2 60
Contribution of final exam to overall grade 1 40
Toplam 3 100
In-Term Studies
  Number Contribution
Presentation 1 20
Midterm Examinations (including preparation) 1 40
Toplam 2 60
No Program Learning Outcomes Contribution
1 2 3 4 5
1 understands principles of deductive reasoning; has experience to verify well-foundedness and exactness of mathematical statements in systematic ways; X
2 can properly state and use concepts and results of major mathematical interest; X
3 masters current computational techniques and algorithms; has a good ability in their use; can identify relevant tools, among those one has learned, suitable to solve a problem and is able to judge whether or not one is in possession of these tools; X
4 is able to express one’s mathematical ideas in an organised way both in written and oral forms; X
5 understands relations connecting substantial concepts and results; can switch from one viewpoint to another on mathematical objects (pictures, formulae, precise statements, heuristic trials, list of examples,...); X
6 has followed individually a guided learning strategy; has pursued steps toward the resolution of unfamiliar problems; X
7 has a theoretical and practical knowledge in computer science well adapted for learning a programming language; X
8 has investigated the relevance of modeling and using mathematical tools in natural sciences and in the professional life; is conscious about historical development of mathematical notions; X
9 has followed introduction to some mathematical or non-mathematical disciplines after one’s proper choice; had experience to learn selected subjects according to one’s proper arrangement; X
10 masters French language as well as other foreign languages, to a level sufficient to study or work abroad. X
Activities Number Period Total Workload
Class Hours 2 28 56
Working Hours out of Class 1 5 5
Presentation 1 2 2
Midterm Examinations (including preparation) 1 4 4
Final Examinations (including preparation) 1 7 7
Seminar 7 2 14
Total Workload 88
Total Workload / 25 3,52
Credits ECTS 4
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