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Syllabus ( MATH 411 )


   Basic information
Course title: Introduction to Data Analysis
Course code: MATH 411
Lecturer: Prof. Dr. Nuri ÇELİK
ECTS credits: 5
GTU credits: 3 ()
Year, Semester: 2019-2020, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Departmental Elective
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: Math 113
Professional practice: No
Purpose of the course: Collecting, organizing, summarizing and analyzing the data for any research.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Understand the importance of the data for any research.

    Contribution to Program Outcomes

    1. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
    2. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Organize and summarize the data.

    Contribution to Program Outcomes

    1. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
    2. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.
    3. Adapt to a fast-changing technological environment, improving their knowledge and abilities constantly.
    4. Exhibiting professional and ethical responsibility.

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Analyse the data and estimate or forecast from the knowledge of the data.

    Contribution to Program Outcomes

    1. Communicating between mathematics and other disciplines, and building mathematical models for interdisciplinary problems.
    2. Describing, formulating, and analyzing real-life problems using mathematical and statistical techniques.

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
Week 1: Introduction to Data Analysis, Population, Sample, Measures.
Week 2: Orginizing the data, Graphs, Frequency tables, stem and leaf displays.
Week 3: Descriptive statistics for location, quantiles.
Week 4: Descriptive statistics for scale, box plot, skewness, kurtosis
Week 5: Relation analysis for qualitative data.
Week 6: Midterm 1 and solutions
Week 7: Relation analysis for quantitative data.
Week 8: Normal Distribution and Its Applicaitons
Week 9: Estimation and Hypothesis Testing
Week 10: Simple Linear Regression Analysis
Week 11: Multiple Regression Analysis
Week 12: Midterm 2 and solutions
Week 13: ANOVA models
Week 14: Time Series and SPSS applications
Week 15*: -
Week 16*: Final Exam
Textbooks and materials: Andrew Gelman, Jennifer Hill, 2006, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press
Recommended readings: Gordon S. Linoff, 2012, Data Analysis Using SQL and Excel, Wiley.
Jeff Leek, 2005, The Elements of Data Analytic Style, Kindle Edition.
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 6-12 60
Other in-term studies: 0
Project: 0
Homework: 0
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 3 14
Own studies outside class: 3 14
Practice, Recitation: 0 0
Homework: 0 0
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 10 2
Mid-term: 4 2
Personal studies for final exam: 10 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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