CS498 Special Topics in Computer Science I

Print this page Please use the scale options of your printing settings for adjustments.

Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
CS498 Special Topics in Computer Science I 3 2 6 Monday 09:00-10:50; Wednesday 10:00-10:50
Prerequisite It is a prerequisite to
Lecturer Office Hours / Room / Phone

Currently not available

E-mail
Assistant Assistant E-mail etahirovic@ius.edu.ba
Course Objectives After completing this course, you will (1) have solid foundations about data analysis; (2) understand the whole process from acquiring data over cleaning and manipulating it, to presenting and reporting on it; and (3) be able to do all this practically using R statistical software.
Textbook This course does not have a single all encompassing textbook. This is due to a very heterogenous set of skills and concepts needed for computer aided data engineering and analysis. We will use two books that can be used as a reference source: 1. "R for Data Science" - G. Grolemund, H. Wickham, O'Reillly Jan. 2017, First edition (available online under http://r4ds.had.co.nz/) 2. "Advanced R" - H. Wickham, Sept. 2014., Chapman and Hall/CRC; 1 edition (available online https://adv-r.hadley.nz/).
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Have a clear picture of Data Analysis Project (DAP) and all elements / processes involved (data manipulation, data vizualization, reporting tools, programming concepts and data technologies)
  2. Govern basic skills for data manipulation using R (data tables, data tidying, selecting and filtering, reshaping, aggregation, joins and merges)
  3. Govern basic skills for data visualization using R (basics, statistical graphics, efficient displays, design and aesthetics considerations)
  4. Understand programming with emphasis on data analysis (data types and structures, variables, functions control flow structures, regular expressions)
  5. Know how to report when data analysis is done and make use of different reporting tools (markdown syntax, dynamic documents, writing reports)
Teaching Methods Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Homeworks that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results.
WEEK TOPIC REFERENCE
Week 1 Introduction, presenting the syllabus itemwise, introducing logistics and class policies; What is data science?
Week 2 Overall review of RStudio workspace; Basic data types in R and their implementation in vectors; Introduction to atomic R functions for understanding and implementing concepts of atomicity, vectorization, recycling and subsetting.
Week 3 Review of more data structures like arrays and lists. Discussion of the traditional base graphics approach that is based on R vectors.
Week 4 Typical storage formats of tabular data and relationship to R data frames; Importing tables in R, basic manipulation of data frames;
Week 5 Programming basics for Data Analysis (Part 1): write basic functions, the notion of R expressions, and introduction to conditionals.
Week 6 Programming basics for Data Analysis (Part 2): iteration, we review control flow structures such as for loops, while loops, repeat loops, and the apply family functions
Week 7 Programming basics for Data Analysis (Part 2): iteration, we review control flow structures such as for loops, while loops, repeat loops, and the apply family functions.
Week 8 Quiz 1
Week 9 Manipulating character strings and testing functions: character strings, and how to perform basic manipulation of strings; in parallel, we'll keep working on writing functions, especially focusing on testing functions
Week 10 Regular expressions, a tool to describe a certain amount of text called "patterns". We'll describe the basic concepts of regex and the common operations to match text patterns.
Week 11 Two-stage experiments; Conditional expectation; Conditional variance; Conditional distributions; Bayes formula;
Week 12 Concept of the mean of a random sample (calculation, interpretation, variability…). Covariance, variance and corellation coefficient as elements to build best linear prediction of Y on X.
Week 13 Quiz 2
Week 14 Random numbers and simulations: we'll look at some basic problems involving working with random numbers and creating simulations (Part2)
Week 15 Concept and examples of statistical tests (Could the data analysis result be a coincidence?): T-test, Paired T-test, Fisher exact test, Wilcoxon
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam / Final Project 1 40 3
Semester Evaluation Compenents
Tutorial Participation and Active Contribution 1 10 1,5
Quizzes 2 25 2,4
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture Hours 3 15 45 Final Project 2 14 28
Active Tutorials 2 14 28 Active Tutorials 2 14 28
Home Study 2 14 28 Final Exam Study 13 1 13
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 04/03/2020
QR Code for https://ecampus.ius.edu.ba/syllabus/cs498-special-topics-computer-science-i

Print this page Please use the scale options of your printing settings for adjustments.