SOFTWARE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN4103	Data Analysis with R	Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
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.

Learning Outcomes

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

Course Content

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.

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks:	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/"

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 40
Midterms	1	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	3	42
Homework Assignments	2	9	18
Midterms	6	3	18
Final	6	3	18
Total Workload			138

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Be able to specify functional and non-functional attributes of software projects, processes and products.
2)	Be able to design software architecture, components, interfaces and subcomponents of a system for complex engineering problems.
3)	Be able to develop a complex software system with in terms of code development, verification, testing and debugging.
4)	Be able to verify software by testing its program behavior through expected results for a complex engineering problem.
5)	Be able to maintain a complex software system due to working environment changes, new user demands and software errors that occur during operation.
6)	Be able to monitor and control changes in the complex software system, to integrate the software with other systems, and to plan and manage new releases systematically.
7)	Be able to identify, evaluate, measure, manage and apply complex software system life cycle processes in software development by working within and interdisciplinary teams.
8)	Be able to use various tools and methods to collect software requirements, design, develop, test and maintain software under realistic constraints and conditions in complex engineering problems.
9)	Be able to define basic quality metrics, apply software life cycle processes, measure software quality, identify quality model characteristics, apply standards and be able to use them to analyze, design, develop, verify and test complex software system.
10)	Be able to gain technical information about other disciplines such as sustainable development that have common boundaries with software engineering such as mathematics, science, computer engineering, industrial engineering, systems engineering, economics, management and be able to create innovative ideas in entrepreneurship activities.
11)	Be able to grasp software engineering culture and concept of ethics and have the basic information of applying them in the software engineering and learn and successfully apply necessary technical skills through professional life.
12)	Be able to write active reports using foreign languages and Turkish, understand written reports, prepare design and production reports, make effective presentations, give clear and understandable instructions.
13)	Be able to have knowledge about the effects of engineering applications on health, environment and security in universal and societal dimensions and the problems of engineering in the era and the legal consequences of engineering solutions.