Master Of Computer Science

This course equips students to analyze and implement statistical methods for data science: descriptive statistics & EDA, probability & inferential statistics (estimation and hypothesis testing), and correlation & regression, using Python and BigQuery, emphasizing critical-analytical thinking to formulate and solve data-based problems.

• CPMK1 — Able to implement descriptive statistics and exploratory data analysis (EDA) using Python/BigQuery
• CPMK2 — Able to implement hypothesis testing and inferential estimation using Python
• CPMK3 — Able to implement correlation and regression analysis for data modeling using Python

This course equips students to analyze, design, and implement machine learning models: supervised learning (regression & classification), unsupervised learning (clustering & dimensionality reduction), and model validation and evaluation, using Python in the DataLab environment, emphasizing critical thinking and application to real data problems.

• CPMK1 — Able to implement regression models (simple & multiple linear) for prediction using Python
• CPMK2 — Able to implement classification models (Logistic Regression, k-NN, Decision Tree, Naïve Bayes, SVM) using Python
• CPMK3 — Able to implement clustering and to validate and evaluate machine learning models using Python

This course equips students to analyze and implement algorithms and data structures for data science: complexity analysis & fundamental data structures, algorithm design techniques (divide-and-conquer, greedy, dynamic programming), and graph algorithms and optimization, using Python, emphasizing complexity analysis and formulating computational solutions to complex data problems.

• CPMK1 — Able to implement algorithm complexity analysis and fundamental data structures using Python
• CPMK2 — Able to implement algorithm design techniques (divide-and-conquer, greedy, dynamic programming) using Python
• CPMK3 — Able to implement graph algorithms and optimization techniques for data science problems using Python

This course equips students to formulate research problems and conduct systematic literature reviews, design valid research methodology (design, data collection, and analysis), and produce and publish scholarly work in accordance with research principles and ethics, emphasizing critical thinking, data integrity, and publishable research outputs.

• CPMK1 — Able to apply research problem formulation and systematic literature review
• CPMK2 — Able to apply research design and data collection and analysis methods
• CPMK3 — Able to apply scholarly writing principles and research ethics in preparing an article for publication

This course equips students with a technology-based entrepreneurial mindset and skills: identifying opportunities and building business models, managing innovation and AI/data-based product development, and commercializing technology products through digital business, emphasizing the technopreneurship spirit, innovative thinking, and integration of intelligent solutions (Computer Vision, e-Business, Intelligent Systems).

• CPMK1 — Able to apply the technopreneurship mindset and build a business model from a technology opportunity
• CPMK2 — Able to apply innovation management and AI/data-based technology product development
• CPMK3 — Able to apply technology product commercialization strategies through digital business

This course equips students to apply cryptographic principles and algorithms to secure data and information: classical and symmetric cryptography, asymmetric cryptography, hash functions and digital signatures, key management, and applied data protection, using Python and Google Cloud services (KMS, DLP), emphasizing data integrity and critical evaluation of security mechanisms.

• CPMK1 — Able to apply classical cryptography and symmetric encryption algorithms using Python
• CPMK2 — Able to apply asymmetric cryptography, hash functions, and digital signatures using Python
• CPMK3 — Able to apply key management and data protection for information system security

This course equips students to analyze and apply Natural Language Processing (NLP): text preprocessing and representation, language modeling and sequence models (RNN/Transformer), and large language models (LLM) and their applications, using Python, TensorFlow, and Vertex AI, emphasizing designing computational approaches and implementing language models.

• CPMK1 — Able to apply text preprocessing and language representation (embeddings) using Python
• CPMK2 — Able to apply language modeling and sequence models (RNN/Transformer) using Python
• CPMK3 — Able to apply and deploy large language models (LLM) for NLP applications

This course guides students to develop a magister thesis research proposal: problem formulation and literature synthesis, methodology and research-plan design, and proposal writing and defense in accordance with research principles and ethics, emphasizing scholarly reasoning, academic integrity, and a proposal ready to proceed to a thesis.

• CPMK1 — Able to apply research problem formulation and literature synthesis for a proposal
• CPMK2 — Able to apply methodology and research-plan design
• CPMK3 — Able to compose and defend a research proposal in line with principles and ethics

This course equips students to apply cloud computing concepts and services: infrastructure (compute, storage, networking), containerization and Kubernetes orchestration, and scalable, reliable distributed system architectures, using Google Cloud, emphasizing theoretical computing foundations and implementing distributed systems.

• CPMK1 — Able to apply cloud infrastructure concepts and services (compute, storage, networking)
• CPMK2 — Able to apply containerization and Kubernetes orchestration for distributed applications
• CPMK3 — Able to apply scalable and reliable distributed system architectures on the cloud

This course equips students to apply the end-to-end data science workflow: exploratory data analysis (EDA), feature engineering, predictive modeling (including BigQuery ML), evaluation, and model presentation and deployment, using Python, BigQuery, and Vertex AI, emphasizing analyzing and formulating data problems and designing computational approaches.

• CPMK1 — Able to apply the data science workflow and exploratory data analysis (EDA) using Python/BigQuery
• CPMK2 — Able to apply feature engineering and predictive modeling (including BigQuery ML)
• CPMK3 — Able to apply building, evaluation, and presentation of data science models