EPOKA UNIVERSITY
FACULTY OF ECONOMICS AND ADMINISTRATIVE SCIENCES
DEPARTMENT OF BUSINESS ADMINISTRATION
COURSE SYLLABUS
2025-2026 ACADEMIC YEAR
COURSE INFORMATIONCourse Title: PYTHON FOR DATA SCIENCE |
| Code | Course Type | Regular Semester | Theory | Practice | Lab | Credits | ECTS |
|---|---|---|---|---|---|---|---|
| BIDS 405 | B | 1 | 3 | 0 | 2 | 3 | 8 |
| Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature) | Dr. Florenc Skuka fskuka@epoka.edu.al |
| Main Course Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | Dr. Florenc Skuka fskuka@epoka.edu.al |
| Second Course Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours: | NA |
| Language: | English |
| Compulsory/Elective: | Compulsory |
| Study program: (the study for which this course is offered) | Master of Science in Business Intelligence and Data Science |
| Classroom and Meeting Time: | |
| Teaching Assistant(s) and Office Hours: | NA |
| Code of Ethics: |
Code of Ethics of EPOKA University Regulation of EPOKA University "On Student Discipline" |
| Attendance Requirement: | |
| Course Description: | This course will introduce students to the field of data science. First, and foremost, students will learn how to conduct data science by learning how to analyze data. That includes knowing how to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily shareable reports. Students will be introduced to two powerful areas of data analysis: machine learning and natural language processing. To conduct data analysis, you'll learn a collection of powerful, open-source, tools including: ● python ● jupyter notebooks ● pandas ● numpy ● matplotlib ● scikit learn ● nltk ● And many other tools And you won't be learning these tools in isolation. You will learn these tools all within the context of solving compelling data science problems. |
| Course Objectives: | The course emphasizes research topics that underlie the advanced visual effects that are becoming increasingly common in commercials, music videos and movies. Topics include classical computer vision algorithms and exciting recent developments that form the basis for future effects (such as natural image matting, multi-image compositing, image retargeting, and view synthesis). We also discuss the technologies behind motion capture and three-dimensional data acquisition. Analysis of behind-the-scenes videos and in-depth interviews with visual effects tie the mathematical concepts to real-world filmmaking. |
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BASIC CONCEPTS OF THE COURSE
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COURSE OUTLINE
|
| Week | Topics |
| 1 | Couse Introduction |
| 2 | Python Programming Fundamentals |
| 3 | Python Data Structures |
| 4 | Working with Data in Python |
| 5 | Notebooks and NumPy |
| 6 | Advanced NumPy Operations |
| 7 | Data Manipulation with Pandas |
| 8 | Data Cleaning and Preparation |
| 9 | Midterm Exam |
| 10 | Data Visualization |
| 11 | Data Analysis Examples |
| 12 | Introduction to Machine Learning, supervised vs. unsupervised learning, Linear and Logistic Regression using Scikit-learn. |
| 13 | Project Presentation |
| 14 | Review |
| Prerequisite(s): | Signals & Systems, Digital Signal Processing and Image Processing. |
| Textbook(s): | 1 - Computer Vision: Algorithms and Applications ,Rick Szeliski 2 -Computer Vision for Visual Effects by Richard J. Radke |
| Additional Literature: | |
| Laboratory Work: | |
| Computer Usage: | |
| Others: | No |
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COURSE LEARNING OUTCOMES
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| 1 | Learn image representation and analyze binary images. |
| 2 | Learn to filter – enhance images and pattern recognition from images. |
| 3 | Understand graph cut segmentation, video matting, and matting extensions. |
| 4 | Explain multiresolution blending and Poisson image editing. |
| 5 | Understand photomontage and image Inpainting. |
| 6 | Explain feature detectors. |
| 7 | Explain video matching, morphing, and view synthesis. |
| 8 | Knows how to make motion capture setup and forward kinematics and Inverse kinematics and motion editing. |
| 9 | Understand facial and markerless motion capture, LiDAR and time-of-flight sensing, Structured light scanning. |
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COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES
(Blank : no contribution, 1: least contribution ... 5: highest contribution) |
| No | Program Competencies | Cont. |
| Master of Science in Business Intelligence and Data Science Program | ||
| 1 | Demonstrate understanding the value of data driven decision making. | 3 |
| 2 | Graduates will acquire the ability to make informed decisions based on data analysis and interpretation. | 4 |
| 3 | Identify the basic concepts that underpin today’s organizational IT infrastructures, such as concepts of databases, information systems, operations and processes, cloud computing, data warehousing and Big Data, Data Mining and Machine Learning. | 5 |
| 4 | Students will develop advanced skills in data analysis techniques, including statistical analysis, data mining, data visualization, and predictive modeling. | 4 |
| 5 | Apply data mining/analytics (statistical and machine-learning) in order to solve real-world business problems. | 5 |
| 6 | Develop skills related to data analytics pipeline from collection, processing, analysis and interpretation | 5 |
| 7 | Graduates will develop strong communication skills to effectively present complex data analysis findings to diverse stakeholders. | 4 |
| 8 | Effectively communicate to top management the results and implications arising from data analytics, security risk assessments, and emerging technologies. | 3 |
| 9 | Demonstrate professionalism and leadership by taking initiatives within their domain of responsibility while working effectively with other team members. | 3 |
| 10 | The program offers practical training and exposure to industry-standard software and tools used in business intelligence and data analysis. | 5 |
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COURSE EVALUATION METHOD
|
| Method | Quantity | Percentage |
| Homework |
6
|
5
|
| Project |
1
|
30
|
| Final Exam |
1
|
40
|
| Total Percent: | 100% |
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ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)
|
| Activities | Quantity | Duration(Hours) | Total Workload(Hours) |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 5 | 80 |
| Hours for off-the-classroom study (Pre-study, practice) | 10 | 4 | 40 |
| Mid-terms | 1 | 20 | 20 |
| Assignments | 0 | ||
| Final examination | 1 | 27.5 | 27.5 |
| Other | 1 | 20 | 20 |
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Total Work Load:
|
187.5 | ||
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Total Work Load/25(h):
|
7.5 | ||
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ECTS Credit of the Course:
|
8 | ||
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CONCLUDING REMARKS BY THE COURSE LECTURER
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This course has taken students from the fundamentals of Python programming through the full workflow of modern data science. Each week built progressively from core coding skills and data structures to NumPy, Pandas, and essential techniques for preparing, analyzing, and visualizing data. Students were also introduced to machine learning concepts and applied models using Scikit-learn, culminating in hands-on project presentations. By the end of the course, students have developed practical, end-to-end data science skills that prepare them for advanced study and real-world analytical challenges. |