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      BUS 220 | Course Introduction and Application Information

      Course Name
      Data Analytics for Business and Economics
      Code
      Semester
      Theory
      (hour/week)
      Application/Lab
      (hour/week)
      Local Credits
      ECTS
      BUS 220
      Spring
      2
      2
      3
      5

      Prerequisites
      None
      Course Language
      English
      Course Type
      Required
      Course Level
      First Cycle
      Mode of Delivery -
      Teaching Methods and Techniques of the Course Application: Experiment / Laboratory / Workshop
      Lecture / Presentation
      National Occupation Classification -
      Course Coordinator
      • Prof. Dr. Mehmet Gençer
      Course Lecturer(s)
      • Dr. Öğr. Üyesi EZGİ ÖZSÖĞÜT
      • Dr. Öğr. Üyesi Sinem Tokcaer
      • Prof. Dr. Mehmet Gençer
      Assistant(s)
      • Araş. Gör. Hasan GÜLOĞLU
      Course Objectives Processing analysis of data is a requirement for all professionals in today’s digital environment. This course aims to develop fundamental data analytics skills necessary in the business and economic fields.
      Learning Outcomes

      The students who succeeded in this course;

      • Explain the structure of a program in procedural language.
      • Debug Python programs by testing.
      • Employ Python programming patterns to solve a business data processing problem, which may involve skills such as data connection web scraping, and so on.
      • Describe the advantages and capabilities of, and best practices for R statistical programming platform and add on RStudio software for analytics tasks in a business or research team setting.
      • Produce data insights using R notebooks.
      • Identify common big data processing and analytics pipelines.
      • Describe technologies and supply approaches to implement big data processing and analytics pipelines.
      Course Description This course aims to develop data processing and analysis skills required in the fields of business and economics. In this course students learn computer coding skills focused on data processes, with case studies in their fields. In contrast to coding courses for students aiming an expertise in computing, this course approaches algorithms in terms of their function in business and economics problems and focuses on features and applications of data processing patterns. In this applied course students learn the programming languages Python and R, which are very common in business practice and research. In addition, the course covers the properties of big data analytics and technologies used for it. The course consists of three modules: 1-Big data (2 weeks): technologies (Hadoop, MapReduce), competencies, real time data processing, possible value creation pipelines in big data 2-Statistical processing with R (6 weeks): Exploratory statistics in R. 3-Introduction to coding for data analytics with Python (6 weeks): data types, searching/sorting, list processing for statistical calculations, web scraping for data
      Related Sustainable Development Goals

       



      Course Category

      Core Courses
      Major Area Courses
      Supportive Courses
      Media and Management Skills Courses
      Transferable Skill Courses

       

      WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

      Week Subjects Related Preparation
      1 MODULE 1: Big Data. Big data introduction, Big data competencies, real time data processing, essential data transformations in big data. Big data technologies: Hadoop “Big Data Analytics: Concepts, Technologies, and Applications” https://aisel.aisnet.org/cais/vol34/iss1/65/?utm_source=aisel.aisnet.org%2Fcais%2Fvol34%2Fiss1%2F65&utm_medium=PDF&utm_campaign=PDFCoverPages
      2 Big data: Value creation pipelines in big data. Real time or offline value creation pipelines in big data. “Big Data Analytics: Concepts, Technologies, and Applications” https://aisel.aisnet.org/cais/vol34/iss1/65/?utm_source=aisel.aisnet.org%2Fcais%2Fvol34%2Fiss1%2F65&utm_medium=PDF&utm_campaign=PDFCoverPages
      3 MODULE 2: Statistical Programming With R Getting started with R and Rstudio, R scripts, R panes, installing packages, R basics (objects, workspace, variable names), Chapter 1 Introduction to Data Science; Chapter 1 R for Data Science https://rafalab.github.io/dsbook/
      4 R and programming basics: Data types and vectors; matrices; factors; data frames; Chapter 2 Introduction to Data Science
      5 lists; indexing; subsetting Case Sudy: US Gun murders Chapter 4 Introduction to Data Science
      6 Introduction to visualisation with ggplot2 package (grammar of graphs, aestetics, facets, transformations) Miles per Gallon and Diamond carat data sets Chapter 3 R for Data Science https://r4ds.had.co.nz/index.html
      7 Exploratory Data Analysis (Variation, missing values, covariation) Chapter 7 R for Data Science
      8 Reporting with Rmarkdown and Wrapping up with a case study Gapminder data set (GDP per capita, life expectancy and fertility) Chapter 9 Introduction to Data Science
      9 MODULE 3: Introduction to Python data processing patterns * Python editor and interface. The syntax and grammar and vocabulary. Simple data types.help system. * Python scripts “Introduction to Python Programming for Business and Social Science Applications”, Chapter 1
      10 * Loops * Design patterns with loops “Introduction to Python Programming for Business and Social Science Applications”, Chapter 2
      11 Exploratory Data Analysis (Variation, missing values, covariation “Introduction to Python Programming for Business and Social Science Applications”, Chapter 3
      12 Python data structures Functions “Introduction to Python Programming for Business and Social Science Applications”, Chapter 4
      13 Using files .csv “Introduction to Python Programming for Business and Social Science Applications”, Chapter 5
      14 Statistical calculations with "Matlib" and "statistics" libraries “Introduction to Python Programming for Business and Social Science Applications”, Chapter 6
      15 Semester Review
      16 Final Exam

       

      Course Notes/Textbooks

      Introduction to Python Programming for Business and Social Science Applications (2020) Frederick Kaefer, Paul Kaefer, Sage publications

       

      Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. " O'Reilly Media, Inc.".

       

      Tutorial: “Big Data Analytics: Concepts, Technologies, and Applications”  

      https://aisel.aisnet.org/cais/vol34/iss1/65/?utm_source=aisel.aisnet.org%2Fcais%2Fvol34%2Fiss1%2F65&utm_medium=PDF&utm_campaign=PDFCoverPages

      Suggested Readings/Materials

       

      EVALUATION SYSTEM

      Semester Activities Number Weigthing
      Participation
      Laboratory / Application
      Field Work
      Quizzes / Studio Critiques
      4
      30
      Portfolio
      Homework / Assignments
      Presentation / Jury
      1
      20
      Project
      1
      20
      Seminar / Workshop
      Oral Exams
      Midterm
      Final Exam
      1
      30
      Total

      Weighting of Semester Activities on the Final Grade
      6
      70
      Weighting of End-of-Semester Activities on the Final Grade
      1
      30
      Total

      ECTS / WORKLOAD TABLE

      Semester Activities Number Duration (Hours) Workload
      Theoretical Course Hours
      (Including exam week: 16 x total hours)
      16
      2
      32
      Laboratory / Application Hours
      (Including exam week: '.16.' x total hours)
      16
      2
      32
      Study Hours Out of Class
      16
      2
      32
      Field Work
      0
      Quizzes / Studio Critiques
      4
      3
      12
      Portfolio
      0
      Homework / Assignments
      0
      Presentation / Jury
      1
      7
      7
      Project
      1
      25
      25
      Seminar / Workshop
      0
      Oral Exam
      0
      Midterms
      0
      Final Exam
      1
      10
      10
          Total
      150

       

      COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

      #
      Program Competencies/Outcomes
      * Contribution Level
      1
      2
      3
      4
      5
      1

      To be able to identify and analyze problems in the field of trade and finance, and to develop solutions.

      -
      -
      -
      -
      -
      2 To be able to use the theoretical and practical knowledge gained in the field of International Trade and Finance.
      -
      -
      -
      -
      -
      3 To be able to analyze the developments in global markets by using critical thinking skills.
      -
      -
      -
      -
      -
      4 To be able to analyze and interpret data in the field of finance, commerce and economics by using information technologies effectively.
      -
      -
      -
      -
      -
      5 To be able to acquire knowledge about the legal regulations and practices in the field.
      -
      -
      -
      -
      -
      6 To be able to foresee and define the risks that could be encountered in the field of trade and finance and to take decisions to manage such risks.
      -
      -
      -
      -
      -
      7 To be able to acquire and use verbal and numerical skills necessary for the nature of international trade and finance program.
      -
      -
      -
      -
      -
      8 To be able to obtain, synthesize and report the information related to the fields of trade and finance.
      -
      -
      -
      -
      -
      9 To be able to contribute to the solution of problems as individual, team member or leader.
      -
      -
      -
      -
      -
      10

      To be able to evaluate the issues related to the field with an ethical perspective and social sensitivity.

      -
      -
      -
      -
      -
      11 To be able to collect data in the areas of International Trade and Finance and communicate with colleagues in a foreign language ("European Language Portfolio Global Scale", Level B1).
      -
      -
      -
      -
      -
      12 To be able to speak a second foreign at a medium level of fluency efficiently.
      -
      -
      -
      -
      -
      13 To be able to relate the knowledge accumulated throughout human history to their field of expertise.
      -
      -
      -
      -
      -

      *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest


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