Programming for Artificial Intelligence and Data Science

Fundamentals for Data Science

Statistical modeling

Introduction to Artificial Intelligence

Data Engineering

Microservices Architectures

Semantic Web Technologies

Business Intelligence

Programming in R (Elective)

Functional Programming in Scala (Elective)

Big Data and Cloud Computing

Information Retrieval and Natural Language Processing

Human Computer Interaction and Data Visualization

Process Mining

Deep Learning

Data Mining

Programming for Artificial Intelligence and Data Science is a dynamic course designed to equip you with the essential skills for success in the cutting-edge fields of AI and data science. Explore data analysis, machine learning, and AI programming. Gain hands-on experience with the latest tools and techniques.

Intro to Computer Science for Data Science: Designed as an introductory course within Data Science programs, it covers computer science fundamentals and programming skills.

Hands-on Programming: Emphasizes practical programming experience to enable students to work with data effectively.

Data Manipulation: Equips students with skills for data manipulation and analysis, particularly with large datasets.

Foundation for Data Science: Provides a strong foundation for future coursework in data science and AI.

Overall Objective: The primary goal is to prepare students with essential knowledge and skills to thrive at the intersection of computer science and data science.

Statistical Modeling is a comprehensive course that delves deep into the art and science of data analysis. Explore advanced statistical techniques, hypothesis testing, and regression analysis. Master the art of modeling data for meaningful insights. 

Statistical Inference: This course provides a thorough introduction to statistical inference using linear models and related methods.

Topics Covered: It covers a range of topics, including t-tests, chi-square tests, linear regression, model validation, causal inference, ANOVA, and a brief overview of generalized linear models (GLM).

Critical Thinking: Emphasis is placed on developing strong statistical thinking skills, evaluating assumptions, and creating practical tools for real-world applications.

Practical Application: The course is designed to equip students with the knowledge and methods needed for real-world statistical applications.

Mathematical Statistics: It delves into the fundamental concepts and methods in mathematical statistics, with a focus on practical use.

Introduction to Artificial Intelligence is your gateway to the fascinating realm of AI. Explore the fundamental concepts, algorithms, and applications of AI. Dive into machine learning, neural networks, and problem-solving techniques. 

Data Engineering is your gateway to becoming a data architect and expert data handler. Explore the intricacies of data pipelines, storage, and processing. Master the art of transforming raw data into valuable insights. Join us to build the backbone of data-driven decision-making and embark on a career in high-demand data engineering roles.

Data Explosion: In today's world, data is exploding in all sectors, and effectively managing and analyzing it is vital for success.

Big Data Challenge: This course covers the challenges posed by massive datasets and diverse data types, including unstructured, image, video, and more.

Data Lifecycle: It explores the entire data lifecycle, from creation to disposal, and focuses on database engineering, data standards, and quality.

Practical Skills: Students learn to handle big data, ensuring they are well-prepared for real-world data management scenarios in various industries.

Explore the future of software development with our Microservices Architectures course. Dive into the world of scalable and agile microservices. Master the art of designing, deploying, and maintaining microservices-based applications. Join us to revolutionize your approach to software architecture and stay ahead in the ever-evolving tech industry. 

Alternative to Monolithic: Microservices are presented as an alternative to traditional monolithic software architecture.

Loosely Coupled Services: Microservices involve loosely coupled software services developed and deployed independently using various technologies.

RESTAPI Integration: Integration between microservices is typically achieved through a RESTAPI layer.

Project-Oriented: The course focuses on practical application, with students developing a sample system using microservices in small teams.

Hands-on Experience: Students gain hands-on experience by developing and inter-testing various microservices.

Cloud Deployment: The developed microservices APIs are deployed online using cloud containers.

Semantic Web Technologies is an advanced course that delves into the cutting-edge world of web semantics. Explore the power of linked data, ontologies, and knowledge representation. Dive deep into RDF, OWL, and SPARQL.

Semantic Web Foundations: The course covers the conceptual and technical foundations of the Semantic Web.

Knowledge Representation: It explores how to represent knowledge in a machine-understandable way, primarily through ontologies.

RDF and OWL: Focuses on modeling facts using RDF and OWL, essential components of the Semantic Web.

Linked Open Data: Addresses the concept of linked open data and semantic search techniques.

Data Quality: Highlights the importance of semantically enriched data for various domain systems and how it can improve data quality.

Semantic Knowledge Graph: Explains the role of a semantic knowledge graph in enhancing Artificial Intelligence architectures and automating data quality management.

Our Business Intelligence course is your gateway to the world of data-driven decision-making. Explore data analysis, reporting, and visualization. Learn to transform raw data into actionable insights for organizations. Master BI tools and techniques used in today's business landscape. 

BI Tools and Solutions: The course delves into Business Intelligence (BI) tools and Business Process Management (BPM) solutions.

Data Warehouse Basics: It provides a foundational introduction to Data Warehousing, emphasizing its relevance for managerial purposes.

Technical Understanding: Students gain a basic understanding of Data Warehousing from a technical perspective.

Practical Exercises: The course includes hands-on exercises during class hours to reinforce learning.

Discover the future of data management with our Big Data and Cloud Computing course. Dive into the world of massive data sets and cloud technologies. Learn how to harness the power of data in cloud environments. 

Comprehensive Overview: The course offers a comprehensive introduction to Big Data infrastructure technologies and tools.

Cloud-Based Focus: Emphasis is placed on cloud-based Big Data infrastructure and analytics solutions, showcasing how they can be integrated into an organization's IT and data infrastructure.

Core Functionality: Students will learn the core functionality of major Big Data infrastructure components and understand how they work together to provide business benefits.

Hands-On Experience: Hands-on exercises provide practical insights into simplifying Big Data processing using cloud-based services for Hadoop, Machine Learning, and data analytics.

Apache Hadoop Ecosystem: Specific attention is given to the Apache Hadoop ecosystem, including components like MapReduce, Spark, and HBase.

Types of Cloud Platforms: The course covers different types of cloud platforms and discusses their advantages and disadvantages for Big Data analysis, including scalability and performance considerations.

Information Retrieval and Natural Language Processing is an exciting course that delves into the fascinating realms of language and data. Explore how machines understand and process human language. Learn advanced techniques for extracting information from text and documents.

Information Retrieval Basics: The course covers fundamental concepts and methods related to information retrieval, focusing on making information accessible from diverse document collections.

Hands-On Web Retrieval Projects: Students engage in practical, hands-on projects related to web information retrieval, gaining real-world experience in this field.

NLP Integration: Text information retrieval involves Natural Language Processing (NLP) methods, which are also covered in the course.

NLP Pipeline Building: Students learn to build NLP pipelines, enhancing their ability to process and analyze text data effectively.

Custom NLP Models: The course includes training custom machine learning NLP models, enabling students to tailor their solutions to specific tasks and challenges.

Discover the art of Human-Computer Interaction and Data Visualization in our engaging course. Dive into the principles of designing intuitive user interfaces and learn the art of data storytelling through compelling visualizations. Gain practical skills in user experience (UX) design and data presentation. 

Fundamental Theories: The course introduces students to the fundamental theories of HCI, emphasizing the importance of human faculties in designing interactive systems.

User Interface Design: Students learn how to apply HCI knowledge to create effective user interfaces.

Product Life Cycle: The course covers the entire product life cycle, including design, implementation, and evaluation of user-centered systems.

Data Visualization: As data complexity increases, students explore methods and techniques for enhancing data comprehensibility through visualization.

Process Mining is a transformative course that dives deep into the world of process analysis and optimization. Discover how to uncover hidden insights from your organization's operations using data-driven techniques. Learn to streamline processes, improve efficiency, and drive informed decision-making.

Bridge Between Modeling and Data Analysis: The course serves as a bridge between model-based process analysis and data-oriented analysis techniques.

Data Science for Process Improvement: Students gain data science knowledge that can be directly applied to analyze and enhance processes in diverse domains.

Process Discovery Algorithms: The course covers various process discovery algorithms to extract insights from event data.

Conformance Techniques: Introduces conformance techniques for comparing processes and event data to identify discrepancies and optimizations.

Hands-on Experience: Students have the opportunity to experiment with real tools, gaining practical experience in process mining.

Deep Learning is an intensive course focused on one of the most exciting frontiers of artificial intelligence. Dive deep into neural networks, advanced algorithms, and the foundations of deep learning. Discover how to build intelligent systems that can recognize patterns, process complex data, and make predictions.

Introduction to Deep Learning: The course provides an introduction to deep learning, a subset of machine learning focused on modern neural networks.

Layered Data Representations: Deep learning algorithms extract layered high-level data representations to optimize performance in various tasks.

AI Advancements: Deep learning has played a pivotal role in recent AI advancements.

Topics Covered: The course covers fundamental topics including basic neural networks, convolutional and recurrent network structures, deep unsupervised learning, reinforcement learning, and their applications in domains like speech recognition and computer vision.

Data Mining is an advanced course that delves deep into the art and science of uncovering valuable insights from vast datasets. Explore the intricacies of data preprocessing, pattern discovery, and predictive modeling. Master cutting-edge techniques and tools in data mining.

Introduction to Data Mining: The course provides an introduction to data mining concepts and practical machine learning applications.

Algorithms and Principles: It covers the principles and implementation of widely used machine learning algorithms in data mining.

Supervised and Unsupervised Learning: Students will learn about both supervised and unsupervised learning techniques.

Model Types: The course explores various model types, including decision trees, classification and association rules, linear and non-linear models, and clusters.

Practical Application: Students gain practical experience in using these models for real-world data mining tasks.