CS423 Parallel Computing

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
Tuesday 3pm-5:50pm
Prerequisite It is a prerequisite to

None

Lecturer Džejla Međedović Office Hours / Room / Phone
Monday:
9:00-15:00
Tuesday:
12:00-17:00
A F1.33
E-mail dzmedjedovic@ius.edu.ba
Assistant Assistant E-mail
Course Objectives The goal of this course is to equip students with Python basics, data analysis, data structures and visualization in Python.
A student who successfully completes this course will have achieved the foundation to become a junior data scientist, capable of preprocessing, analyzing, visualizing and drawing useful insights from various datasets. The course does not assume any knowledge of Python per se, but it assumes prior programming experience, knowledge of basic probability, data structures and algorithms.
Textbook The main textbook: Python for Data Analysis, Wes McKinney, O'Reilly, 2013 (PDA) The secondary textbook: High Performance Python, Micha Gorelick and Ian Oszvald, O'Reilly, 2014 (HPP)
Additional Literature
  • The course will also feature a set of Jupyter notebooks that will be provided for practice to students.
  • There is a rich literature on Python, data science, data structures and algorithms in Python that will be introduced on a need-by basis.
  • There will be separate literature introduced for the visualization part of the course.
Learning Outcomes After successful  completion of the course, the student will be able to:
    Teaching Methods Practical and active problem-solving by the instructor and students, capstone data science project, exams.
    Teaching Method Delivery Online Teaching Method Delivery Notes Lectures + Tutorials with practical problem-solving sessions
    WEEK TOPIC REFERENCE
    Week 1 Course introduction N/A --- materials from lecture
    Week 2 Environment setup/Jupyter overview / Python crash course intro N/A --- materials from lecture
    Week 3 Python crash course: Lists and tuples HPP Ch 3
    Week 4 Python crash course: Dictionaries and sets HPP Ch 4
    Week 5 Python for Data Analysis: NumPy PDA Ch 4
    Week 6 Python for Data Analysis: Pandas PDA Ch 5
    Week 7 Python for Data Analysis: Pandas PDA Ch 5
    Week 8 Data loading, storage and file formats PDA Ch6
    Week 9 Mid-term I
    Week 10 Python for Data Visualization: Matplotlib // Data Capstone project out PDA Ch8 (and more materials)
    Week 11 Python for Data Visualization: Seaborn PDA Ch8 (and more materials)
    Week 12 Python for Data Visualization: Plotly / Python built-in visualization PDA Ch8 (and more materials)
    Week 13 Cufflinks, geographical plotting PDA Ch8 (and more materials)
    Week 14 Time series PDA Ch10
    Week 15 Mid-term 2 // Data Capstone Project due
    Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
    Final Exam (Data Capstone Project)) 1 30
    Semester Evaluation Compenents
    Mid-term I 1 25
    Mid-term II 1 25
    Tutorial weekly exercises 1 20
    ***     ECTS Credit Calculation     ***
     Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
    Lectures 3 15 45 Tutorials 2 15 30
    In-term exam prep 7 2 14 Weekly self study 3 15 45
    Data Capstone 16 1 16
            Total Workload Hours =
    *T= Teaching, P= Practice ECTS Credit =
    Course Academic Quality Assurance: Semester Student Survey Last Update Date: 16/04/2021
    QR Code for https://ecampus.ius.edu.ba/syllabus/cs423-parallel-computing

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