| School/Faculty/Institute |
Gradutate School of Science and Engineering |
| Course Code |
BDA 501 |
| Course Title in English |
Applied Statistics |
| Course Title in Turkish |
Uygulamalı İstatistik |
| Language of Instruction |
EN |
| Type of Course |
Exercise,Flipped Classroom,Lecture |
| Level of Course |
Intermediate |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
174 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
Basic probability knowledge |
| Co-requisites |
None |
| Registration Restrictions |
Only Graduate Students |
| Overall Educational Objective |
To learn the basic statistical concepts with on hands applications using modern tools to summarize and analyze data via graphical and quantitative tools, to drive conclusions using statistical analysis and modern tools. |
| Course Description |
The aim of the course is to give the fundamentals of statistical analysis. This course introduces the basics of statistics for engineers to summarize numerical and categorical data obtained from surveys, experiments, etc. The topics include different data types, measures of location, variability, shape, and association between variables. The students are expected to learn the fundamental concepts of estimation, confidence intervals, hypothesis testing and apply appropriate tests for population mean, proportion, variance and difference, independence, and goodness to fit. Students will be able to apply Analysis of Variance and Linear Regression using modern tools. |
| Course Description in Turkish |
Bu ders istatistiksel analize giriş dersi olarak tasarlanmıştır. Dersin amacı farklı kaynaklardan elde edilen verilerin analiz yöntemleri konusunda öğrencileri geliştirmektir. Dersin içeriği farklı veri tiplerini, bunların merkezi eğilim, dağılım ve şekil parametrelerinin tespiti, birden fazla değişken arasındaki ilişkinin incelenmesini de içermektedir. Öğrencilerin dersi başarıyla tamamlamaları halinde evren ortalaması, varyansı, ilişkili ve ilişkisiz örneklemler ortalama farkları gibi parametreler için güvenirlik aralığı hesaplayabilmeleri, hipotez testlerini yapabilmeleri ve bu testlerin sonuçlarını yorumlayabilmeleri beklenmektedir. Ayrıca öğrencilerin bağımsızlık testlerini yapabilmeleri, doğrusal regresyon modellerini kurabilmeleri, model parametrelerini hesaplayabilmeleri ve bunları kullanarak istatistiksel tahmin yapabilmeleri beklenmektedir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Summarize numerical and categorical data by frequency distribution, histograms, and computing descriptive statistics (mean, median, variance) by hand and\or using Excel and\or Google Sheets. Analyze and interpret association between two variables using covariance and correlation coefficient by hand and/or using Excel and\or Google Sheets.
2) Understand the basic concepts of sampling distributions, estimation, confidence intervals, and hypothesis testing (type I and II errors), explain the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions.
3) Design, solve and interpret the results of hypothesis tests (t-test, z-test, chi-square test, F-test) related to population mean, population proportion, population differences, goodness to fit, tests of independence, and population variances.
4) Design, solve and interpret the results of Analysis of Variance (ANOVA) to compare population means. Analyze numerical data by graphs, create and test the validity of a simple and multiple linear regression models using Excel. Understand the use of regression models in prediction and estimation.
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
|
|
|
|
| 2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
|
|
|
|
| 3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
|
|
|
|
| 4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
|
|
|
|
| 5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
|
|
|
|
| 6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
|
|
|
|
| 7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
|
|
|
|
| 8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
|
|
|
|
| 9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
|
|
|
|
| 10) Understanding of social and environmental aspects of machine learning applications. |
|
|
|
|
Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
|
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
H |
|
| 2) |
Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
H |
|
| 3) |
Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
H |
|
| 4) |
Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
H |
|
| 5) |
Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
S |
|
| 6) |
Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
S |
|
| 7) |
Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
S |
|
| 8) |
Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
N |
|
| 9) |
Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
S |
|
| 10) |
Understanding of social and environmental aspects of machine learning applications. |
N |
|
| Prepared by and Date |
UTKU KOÇ , |
| Course Coordinator |
UTKU KOÇ |
| Semester |
Fall |
| Name of Instructor |
Öğr. Gör. SERKAN CERAN |
Course Contents
| Week |
Subject |
| 1) |
1. Data and Statistics
1.1 Data, data types, sources of data
2. Descriptive Statistics
2.1 Summarizing data for categorical variables
|
| 2) |
2. Descriptive Statistics
2.2 Summarizing data for quantitative variables
2.3 Measures of location
2.4 Measures of variability
2.5 Measures of distribution shape
|
| 3) |
2. Descriptive Statistics
2.6 Box plots
2.7 Measures of association between two variables
2.8 Summarizing data for two variables
2.9 The weighted mean and working with grouped data
|
| 4) |
3. Sampling Distributions and Interval Estimation
3.1 Point estimation
3.2 Sampling distribution of sample mean
3.3 Law of large numbers and the central limit theorem
3.4 Sampling distribution of sample proportion
|
| 5) |
3. Sampling Distributions and Interval Estimation
3.5 Interval estimation for population mean, known variance
3.6 Interval estimation for population mean, unknown variance
3.7 Interval estimation for population differences
|
| 6) |
3. Sampling Distributions and Interval Estimation
3.7 Interval estimation for population differences (continued)
3.8 Interval estimation for population variance
|
| 7) |
4. Hypothesis Testing Basics
4.1 Null and alternative hypothesis
4.2 Type I and II errors
4.3 Statistical techniques for hypothesis testing
|
| 8) |
5. Hypothesis Testing Involving Single Sample
5.1 Testing population mean, known variance
5.2 Testing population mean, unknown variance
5.3 Testing population proportion
5.4 Testing population variance
|
| 9) |
6. Hypothesis Testing Involving Multiple Samples
6.1 Testing population differences, known variances
6.2 Testing population differences, unknown variances
6.3 Testing population differences, paired samples
|
| 10) |
6. Hypothesis Testing Involving Multiple Samples
6.4 Testing the ratio of population variances
6.5 Goodness to Fit test and Testing independence
|
| 11) |
Analysis of Variance |
| 12) |
8. Linear Regression
8.1 Simple linear regression
8.2 Prediction and estimation using linear regression
|
| 13) |
8. Linear Regression
8.3 Multiple regression
8.4 Logistic regression
|
| 14) |
Review on deciding the appropriate tests for different problems
|
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
| Required/Recommended Readings | None |
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning |
| Homework and Projects | Students are required to complete a portfolio to be able to enter the final exam |
| Laboratory Work | Students will apply the methods they learned using Excel or Google Sheets at the laboratory hours |
| Computer Use | Students will apply the methods they learned using Excel or Google Sheets at the laboratory hours |
| Other Activities | None |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Quiz(zes) |
8 |
% 20 |
| Homework Assignments |
1 |
% 30 |
| Project |
1 |
% 10 |
| Final Examination |
1 |
% 40 |
| TOTAL |
% 100 |
|
| Course Administration |
kocu@mef.edu.tr
02123953600
Course Instructor: Asst. Prof. Utku KOÇ, email: utku.koc(at)mef.edu.tr., office: A block, 5th floor
Grading and evaluation: Evaluation will be based on the student learning outcomes. All the students are required to hand in a complete portfolio to able to enter the final exam. The portfolio will include the proofs of student abilities to perform the minimal tasks provided in this course. Note that quizzes and portfolio constitute 50% of the total grade. It is strongly recommended to complete all the work in a timely fashion. Late responses will be graded in a reduced scale. The deadline for completing the portfolios is the day before the last day of the courses. Students who cannot complete the portfolio on time cannot enter the final exam, thus their grade will be FZ.
Portfolio: The course portfolio will include all the material you have created during this course. The term “proven ability” in the following items means that the student “can do” the items individually during laboratory hours or a quiz. The instructor and teaching assistants will guide the students to complete each item in the portfolio. The portfolio will include:
1. Proven ability to summarize categorical data (Student outcome 1)
2. Proven ability to summarize quantitative data (Student outcome 1)
3. Proven ability to detect outliers (Student outcome 1)
4. Proven ability to use statistical tables correctly (Student outcome 2)
5. Proven ability to calculate confidence interval for single population (Student outcome 2)
6. Proven ability to perform hypothesis test and comment on the results for single population (Student outcome 2,3)
7. Proven ability to perform hypothesis test and comment on the results for population differences (Student outcome 3)
8. Proven ability to differentiate matched and unmatched samples (Student outcomes 2,3)
9. Proven ability to perform ANOVA and comment on the results (Student outcome 4)
10. Proven ability to employ estimation/prediction using linear regression models (Student outcome 4)
|
Information Package / Course Catalogue