| Course Objectives: |
To teach the basic subjects of statistics, which is the first and an important step of data science, to be applied with the help of a mathematical background and a data analysis language. |
| Course Content: |
The relationship between data, information and knowledge, structural, semi-structural and nonstructured data, data sources, things to be considered in data collection, universe, sample, distribution concepts and its effects on the data set, R language and its detailed use, basic statistical concepts, confidence intervals, correlations and types, Chi-square, t and F distributions, hypothesis tests, Chi-square based significance tests, simple and multiple regression, mathematical backgrounds and applications in R language |
| Week |
Subject |
Related Preparation |
| 1) |
Differences, similarities and methods in intersection sets between the concepts of data science, data mining, machine learning and artificial intelligence. Data, information, knowledge concepts. |
|
| 2) |
Scope of data analysis, steps, tools used in the process. Introduction to R language (setting up R, community and Cran structure, advantages and disadvantages of RStudio, package and mirror selection, basic menus) |
|
| 3) |
Basic operations with R language (calling default datasets in R, understanding the summary and data structure of a called data set, viewing certain rows and columns from the data) |
|
| 4) |
R language and basic concepts (variables, operators, vectors, matrices, arrays, tables, basic loop and condition structures) |
|
| 5) |
Basic operations with R language (creating own data set, printing / saving, recall, finding and recalling data set from internet, simple level graphics) |
|
| 6) |
Data manipulation (data manipulation with default functions in R) |
|
| 7) |
Data manipulation (data manipulation with commands in R packets - Tidyverse / Dplyr) |
|
| 8) |
Basic descriptive statistics, hypothesis creation and confidence interval concepts, their application with R language |
|
| 9) |
Correlation concept, its types and application with R language |
|
| 10) |
Chi-square, t and F distributions, hypothesis tests, Chi-square based significance tests, their application with R language |
|
| 11) |
Chi-square, t and F distributions, hypothesis tests, Chi-square based significance tests, their application with R language |
|
| 12) |
The Concept of Regression, simple regression and multiple regression, their application with the R language |
|
| 13) |
The Concept of Regression, simple regression and multiple regression, their application with the R language |
|
| 14) |
LDA and EDA, R language applications |
|
| 15) |
LDA and EDA, R language applications |
|
| 16) |
Final Exam Week |
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| Course Notes / Textbooks: |
Illowsky, B.; Dean, S. (2018). Introductory Statistics, Rice University OpenStax, ISBN: 978-1-947172-05-0. https://d3bxy9euw4e147.cloudfront.net/oscms-prodcms/media/documents/IntroductoryStatistics-OP_i6tAI7e.pdf
Büyüköztürk, Ş.; Çokluk, Ö.; Köklü, N. (2018). Sosyal Bilimler İçin İstatistik, Pegem Akademi, ISBN: 9789756802335.
|
| References: |
Özdemir, Ş. (2018). R Dili İle Veri Ön İşlemeden Model Seçimine Kadar Makine Öğrenmesi Süreci. BTK Akademi Eğitim Videoları. https://www.btkakademi.gov.tr/portal/course/r-dili-ile-veri-on-islemeden-model-secimine-kadar-makine-ogrenmesi-sureci-2305#!/about
Introduction to R, Datacamp.
R Tutorial for Beginners: Learn R Programming Language, Guru99. https://www.guru99.com/r-tutorial.html
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| |
Program Outcomes |
Level of Contribution |
| 1) |
It has a wide range of interdisciplinary approaches to management information systems, primarily business and computer engineering. |
3 |
| 2) |
Comprehends the management information systems in terms of technical, organizational and managerial aspects and uses the current programming language by knowing the logic of programming. |
3 |
| 3) |
Uses different information technologies and systems for understanding and solving various business problems. |
2 |
| 4) |
Interpret the data, concepts and ideas in the field of management information systems with scientific and technological methods. |
1 |
| 5) |
Analyze the needs for an information system and analyze the processes of analysis, design and implementation of the database. |
1 |
| 6) |
Gains technical and managerial contributions to IT projects and takes responsibility. |
2 |
| 7) |
Solve complex business and informatics problems by using various statistical techniques and numerical methods and make analyzes using statistical programs effectively. |
2 |
| 8) |
Uses a foreign language at the B1 General Level in terms of European Language Portfolio criteria according to the level of education. |
1 |
| 9) |
Develops teamwork, negotiation, leadership and entrepreneurship skills. |
1 |
| 10) |
Has universal ethical values, social responsibility awareness and sufficient legal knowledge. |
1 |
| 11) |
Develops positive attitudes related to lifelong learning and identifies individual learning needs and carries out studies to correct them. |
1 |
| 12) |
Students will be able to communicate their ideas and solutions both written and orally, and present and publish them on both national and international platforms. |
1 |
| 13) |
It uses information and communication technologies together with computer software at the advanced level of European Computer Driving License required by the field. |
1 |
ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
