| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| SEN4103 | Data Analysis with R | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Hybrid |
| Course Coordinator : | Dr. Öğr. Üyesi ÖZGE YÜCEL KASAP |
| Course Objectives: | In this course, students will learn how to program in R and how to use R for effective data analysis and visualization. |
|
The students who have succeeded in this course; Understand the fundamental syntax of R through readings, practice exercises, demonstrations, and writing R code. Apply critical programming language concepts such as data types, iteration, control structures, functions, and boolean operators by writing R programs and through examples Import a variety of data formats into R using Rstudio Prepare or tidy datas for in preparation for analysis Query data using SQL and R Analyze a data set in R and present findings using the appropriate R packages Visualize data attributes using ggplot2 and other R packages |
| The R programming language was designed to work with data at all stages of the data analysis process. In this course, students will examine how R can help structure, organize, and clean your data using functions and other processes. They will explore the fundamental concepts associated with R. |
| Week | Subject | Related Preparation | |
| 1) | What is R? • Installing R and RStudio • RStudio Overview • Working in the Console • Arithmetic Operators • Logical Operations • Using Functions | ||
| 2) | "Data structures, variables, and data types • Creating Variables • Numeric, Character and Logical Data • Vectors • Data Frames • Factors • Sorting Numeric, Character, and Factor Vectors • Special Values" | ||
| 3) | "R packages and scripts • Installing and loading packages • Setting up your working directory • Downloading and importing data • Working with missing data • Extracting a subset of a data frame • Writing R scripts • Adding comments and documentation • Creating reports" | ||
| 4) | "Descriptive statistics in R • Measures of central tendency • Measures of variability • Skewness and kurtosis • Summary functions, describe functions, and descriptive statistics by group • Correlations" | ||
| 5) | "Statistical graphs • Scatter Plots • Box Plots • Scatter Plots and Boxand-Whisker Plots Together • Histograms" | ||
| 6) | "Working with messy data • Messy Data • Renaming Columns (Variable Names) • Attaching / Detaching • Tabulating Data: Constructing Simple Frequency Tables • Ordering Factor Variables" | ||
| 7) | "Conditional statements • If / else • Boolean logical operatorsIteration • while loops • for loops" | ||
| 8) | "Data exploration and visualization • Using the ggplot2 package to visualize data • Applying themes from ggthemes to refine and customize charts and graphs • Building data graphics for dynamic reporting" | ||
| 9) | "Data querying: SQL and R • Writing SQL statements in R • Using the Select, From, Where, Is, Like, Order By, Limit, Max, Min SQL functions" | ||
| 10) | "Writing functions • Creating functions • Calling functions" | ||
| 11) | "Interactive reporting with Rmarkdown • RMarkdown basics • Text formatting • Code chunks • YAML header • Preview of notebooks, presentations ,websites, and dashboards" | ||
| 12) | "Machine Learning Project in R Classification and Regression Trees (CART). k-Nearest Neighbors (kNN)." | ||
| 13) | "Machine Learning Project in R Support Vector Machines (SVM) with a linear kernel. Random Forest (RF)" | ||
| 14) | Case Study | ||
| Course Notes: | Wickham, H. & Grolemund, G. (2018). for Data Science. O’Reilly: New York. |
| References: | R: http://www.r-project.org/ RStudio (additional libraries required): http://www.rstudio.com/" |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 2 | % 40 |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 20 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 14 | 3 | 42 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 2 | 9 | 18 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 6 | 3 | 18 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 6 | 3 | 18 |
| Total Workload | 138 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |