Mersin Üniversitesi
Ders Bilgileri
| GEOLOGICAL DATA MANAGEMENT | ||||||
|---|---|---|---|---|---|---|
| Kodu | Dönemi | Teori | Uygulama | Ulusal Kredisi | AKTS Kredisi | |
| Saat / Hafta | ||||||
| JM481 | Fall | 2 | 0 | 2 | 3 | |
| Ön Koşulu Olan Ders( ler ) | Non |
| Dili | en |
| Türü | Elective |
| Seviyesi | Bachelor's |
| Öğretim Elemanı( ları ) | Asst. Prof. Dr. Hidayet TAĞA |
| Öğretim Sistemi | Face to Face |
| Önerilen Hususlar | Non |
| Staj Durumu | None |
| Amacı | Techniques and strategy of data analysis in geology and geological engineering: basic statistics review, analysis of data sequences, mapping, sampling and sample representativity, univariate and multivariate statistics, geostatistics, and geographic informations systems (GIS). Practical experience with geological applications via supplied software and data sets from case histories. |
| İçeriği | Construction data base, method of data analysis, statistical analysis, performance of statistical analysis result, sampling and sample representation, using computer software programme. |
Dersin Öğrenim Çıktıları
| # | Öğrenim Çıktıları |
|---|---|
| 1 | LO-1. Learn to construct data base. |
| 2 | LO-2. Learn method of data analysis. |
| 3 | LO-3. Learn statistical analysis |
| 4 | LO-4. Learn to interpretation about performance of statistical analysis result. |
| 5 | LO-5. Learn to construct maps using data base. |
| 6 | LO-6. Learn represantative sampling for case. |
| 7 | LO-7. Learn to determine analyse method according to existing data base. |
| 8 | LO-8. Learn to use computer software programme for analyse data base. |
Haftalık Ayrıntılı Ders İçeriği
| # | Konular | Öğretim Yöntem ve Teknikleri |
|---|---|---|
| 1 | Methods of construction data base | |
| 2 | Crisp and fuzzy clusters | Teaching, discussion, guided practice |
| 3 | Methods of data analysis for crisp clusters | Teaching, discussion, guided practice |
| 4 | Basic statistical analysis | Teaching, discussion, guided practice |
| 5 | Simple regression analysis | Teaching, discussion, guided practice |
| 6 | Multiple regression analysis | Teaching, discussion, guided practice |
| 7 | Probability analysis | Teaching, discussion, guided practice |
| 8 | Analysis of sequences of data | Teaching, discussion, guided practice |
| 9 | MID-TERM EXAM | |
| 10 | Sampling and sample represantation | Teaching, discussion, guided practice |
| 11 | T-distribution, F- distribution, x2- distribution, VAF, RMSE | Teaching, discussion, guided practice |
| 12 | Methods of analyse for geometric and linguistic datas | Teaching, discussion, guided practice |
| 13 | Fuzzy logic, fractal geometry | Teaching, discussion, guided practice |
| 14 | Methods used for data base constructig maps. | Teaching, discussion, guided practice |
| 15 | Mid-term project using computer software programmes. | Teaching, discussion, guided practice |
| 16 | Final Exam |
Resources
| # | Malzeme / Kaynak Adı | Kaynak Hakkında Bilgi | Referans / Önerilen Kaynak |
|---|---|---|---|
| 1 | Demuth, H., Beale, M., Hagan, M., 2005. MATLAB Version 7.3.0.267; Neural Network Toolbox for Use with Matlab. The Mathworks. 348p. | Text Book/Material | Book |
| 2 | Demuth, H., Beale, M., Hagan, M., 2005. MATLAB Version 7.3.0.267; Neural Network Toolbox for Use with Matlab. The Mathworks. 348p. | Text Book/Material | Book |
Ölçme ve Değerlendirme Sistemi
| # | Ağırlık | Çalışma Türü | Çalışma Adı |
|---|---|---|---|
| 1 | 0.4 | 1 | 1. Mid-Term Exam |
| 2 | 0.6 | 5 | Final Exam |
Dersin Öğrenim Çıktıları ve Program Yeterlilikleri ile İlişkileri
| # | Öğrenim Çıktıları | Program Çıktıları | Ölçme ve Değerlendirme |
|---|---|---|---|
| 1 | LO-1. Learn to construct data base. | 1͵2͵4͵7͵8 | 1͵2 |
| 2 | LO-2. Learn method of data analysis. | 1͵2͵4͵8 | 1͵2 |
| 3 | LO-3. Learn statistical analysis | 1͵2͵4͵7͵8 | 1͵2 |
| 4 | LO-4. Learn to interpretation about performance of statistical analysis result. | 1͵2͵4͵7͵8 | 1͵2 |
| 5 | LO-5. Learn to construct maps using data base. | 1͵2͵4͵7͵8 | 1͵2 |
| 6 | LO-6. Learn represantative sampling for case. | 1͵2͵4͵7͵8 | 1͵2 |
| 7 | LO-7. Learn to determine analyse method according to existing data base. | 1͵2͵4͵7͵8 | 1͵2 |
| 8 | LO-8. Learn to use computer software programme for analyse data base. | 1͵2͵4͵7͵8 |
Not: Ölçme ve Değerlendirme sütununda belirtilen sayılar, bir üstte bulunan Ölçme ve Değerlerndirme Sistemi başlıklı tabloda belirtilen çalışmaları işaret etmektedir.
İş Yükü Detayları
| # | Etkinlik | Adet | Süre (Saat) | İş Yükü |
|---|---|---|---|---|
| 0 | Course Duration | 14 | 2 | 28 |
| 1 | Course Duration Except Class (Preliminary Study, Enhancement) | 14 | 2 | 28 |
| 2 | Presentation and Seminar Preparation | 0 | 0 | 0 |
| 3 | Web Research, Library and Archival Work | 0 | 0 | 0 |
| 4 | Document/Information Listing | 0 | 0 | 0 |
| 5 | Workshop | 0 | 0 | 0 |
| 6 | Preparation for Midterm Exam | 1 | 3 | 3 |
| 7 | Midterm Exam | 1 | 1 | 1 |
| 8 | Quiz | 0 | 0 | 0 |
| 9 | Homework | 3 | 1 | 3 |
| 10 | Midterm Project | 0 | 0 | 0 |
| 11 | Midterm Exercise | 0 | 0 | 0 |
| 12 | Final Project | 1 | 6 | 6 |
| 13 | Final Exercise | 0 | 0 | 0 |
| 14 | Preparation for Final Exam | 1 | 5 | 5 |
| 15 | Final Exam | 1 | 1 | 1 |
| 75 | ||||