Introduction to Data Analysis(INF356)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF356 | Introduction to Data Analysis | 5 | 3 | 0 | 0 | 3 | 4 |
| Prerequisites | IND211 VEYA INF211 |
| Admission Requirements | IND211 VEYA INF211 |
| Language of Instruction | French |
| Course Type | Compulsory |
| Course Level | Bachelor Degree |
| Course Instructor(s) | Günce Keziban ORMAN korman@gsu.edu.tr (Email) |
| Assistant | |
| Objective | This course aims that the students who already have basic knowledge about statistics might combine different statistical concepts, make statistical inference from data, develop models for their data and easily create the codes that implements their models when they come across real-world engineering problems. Hence, those students can approach at first sight theoretically, then develop theoretical solutions and finally create practical structures to the engineering problems related to data. |
| Content |
1. Week Data-Information-Knowledge, General View to Data Analysis 2. Week Basic Statistical Concepts, variable types, Data description, Introduction to R 3. Week Numerical Data Description - Application in R and R visualization functions 4. Week Parametric Statistic, Statistical Inference, creating toy data in R and inference from it 5. Week Comparing two samples, t-test, Interpreting the results, R application 6. Week Analysis of variance, AOV and ANOVA in R 7. Week Linear and Multiple regression, lm function in R 8. Week Midterm 9. Week Covariance analysis, R application 10. Week Variations of Linear Regression: Logic Regression,General Linear Model, Hierarchical linear Model 11. Week Time Series Analysis, Declaration of Term Project 12. Week Non-parametric Statistic, Significance test 13. Week Non-parametric Statistic, Measures of Association 14. Week Advanced non-parametric methods and project presentations |
| Course Learning Outcomes |
The students who succeeded in this course will have following qualifications: 1. Able to Use statistical Methods in Data Analysis 2. Able to Code Statistical Programming 3. Able to Design Statistical Models for Real-world Data analysis 4. Create Theoretical and Practical Background for Understanding Numerical Data 5. Able to Analyze Time Series and Find the trends |
| Teaching and Learning Methods |
1. Lecture 2. Discussion 3. Demonstration 4. Case Study 5. Problem Solving 6. Cooperative Learning 7. Question-Answer 8. Concept Mapping 9. Project 10. Brain Storming |
| References |
1. PDQ Statistics, Geoffrey R. Norman, David L. Streiner, 2003 2. The Art of R Programming, A tour of Statistical Software Design, Norman Matloff, 2011 3. Data Mining Concepts and Techniques, Jiawei Han, Micheline Kamber, 2006 4. An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, 2013 5. Software for Data Analysis: Programming with R (Statistics and Computing), John M. Chambers, 2008 6. Modern Applied Statistics with S (Statistics and Computing), W.N. Venables, B.D. Ripley, 2002 |
Theory Topics
| Week | Weekly Contents |
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Practice Topics
| Week | Weekly Contents |
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Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Toplam | 0 | 0 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Toplam | 0 | 0 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Total Workload | 0 | ||
| Total Workload / 25 | 0,00 | ||
| Credits ECTS | 0 | ||