1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF AGRICULTURE
  3. GARDEN PLANTS DEPARTMENT
  4. 523 HORTICULTURAL CROPS
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
Print

COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
BBT 254STATISTICAL MODELING TECHNIQUES AND APPLICATIONS IN NATURAL SCIENCES2 + 14th Semester3

COURSE DESCRIPTION
Course Level Bachelor's Degree
Course Type Elective
Course Objective To give information about the development processes of natural sciences from past to present, to gain experience by introducing general concepts, theories and objectives. Modeling spatial, temporal and magnitude/severity relationships in natural sciences using statistical methods, increasing the motivation and experience of the participants by interpreting the modeling results both in their own and other fields. To provide participants with the ability to think interdisciplinary and collaborate with researchers in other disciplines, to provide analytical thinking, critical analysis and interpretation skills. To provide participants with predictions about the future of natural sciences, to provide information about statistical methods that are frequently used in international studies but not yet widely used at the national level.
Course Content To emphasize the importance of sampling and model design, to obtain statistical model parameters and to use these parameters to predict unknown properties of the problem under study. To interpret the statistical modeling methods used in natural sciences together with expert instructors, to show examples of their application throughout the country and to popularize their use. To introduce discrete distributions (binomial, Poisson, geometric and negative binomial), continuous distributions (normal, exponential, gamma, inverse gamma) and extreme value distributions (Weibull, Gumbel, Pareto, Frechet, generalized Pareto, power law, Frechet, Burr) used in natural sciences and to give detailed information about the applications of these distributions. To explain the analytical processes for the use of simple and multiple linear regression models; logistic and conditional logistic regression models; Poisson, negative binomial, zero-inflated regression models; classification and regression tree from rule-based methods; artificial neural networks from empirical methods for modeling the factors affecting the distribution and productivity of living species. To use Markov chains, Poisson and combined Poisson processes, which are stochastic processes applied to time-dependent data, in modeling the magnitude, number and recurrence periods of earthquakes, forest fires, floods and landslides. To demonstrate the application of all the methods to be used in the fields of natural sciences to be explained during the training on real data and to provide the ability to interpret the results obtained. To provide the participants with the ability to use these software by solving statistical models to be used in the fields of natural sciences with open source and limited time software. To show the use of statistical modeling techniques in different disciplines and to provide the participants with a broader perspective of the related topics.
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES
1Can develop software skills.
2Implement projects in the field of Data Science.
3Can analyze data.
4Have basic knowledge of statistics.
5Gain Data Literacy.
6Discover how they can create a career map in the field of Data Science.

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12PO 13PO 14
LO 001              
LO 002              
LO 003              
LO 004              
LO 005              
LO 006              
Sub Total              
Contribution00000000000000

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)10330
Mid-terms133
Final examination133
Total Work Load

ECTS Credit of the Course





78

3
COURSE DETAILS
 Select Year   


This course is not available in selected semester.


Print

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes