Course Content
Week 1: Introduction to data scalability and analytics. Core concepts, objectives, and application domains.
Week 2: Introduction to big data. Characteristics, challenges, and modern data ecosystems.
Week 3: Data storage architectures. Relational and non-relational databases.
Week 4: Distributed systems and distributed data processing.
Week 5: Cloud computing infrastructures and scalable data services.
Week 6: Data preprocessing and data cleaning in large-scale environments.
Week 7: Exploratory data analysis and basic statistical techniques.
Week 8: Data analytics methods and knowledge extraction from data.
Week 9: Data visualization and presentation of analytical results.
Week 10: Performance, efficiency, and scalability in data analytics systems.
Week 11: Selection of tools and technologies for data analytics applications.
Week 12: Case studies and applications to real-world problems.
Learning Outcomes
Upon successful completion of the course, students will be able to:
- Understand the core concepts of data scalability and analytics in modern information environments,
- Describe the main architectures and technologies for storing, processing, and analyzing large-scale data,
- Distinguish between traditional and distributed approaches to data management,
- Select appropriate techniques, tools, and infrastructures for data processing and analytics in scalable environments,
- Apply basic methods for data preprocessing, analysis, and visualization,
- Analyze problems related to performance, scalability, and efficiency in data systems,
- Evaluate alternative solutions for data management and analytics based on technical and operational criteria,
- Design basic data workflows that support analytical applications and decision-making,
- Collaborate in the implementation of small-scale data analytics projects using modern technological tools.
