Lectures related to Semi-Structured Data

Explores the Semantic Web, database schemas, XML data model, and ontologies.

Data Modeling: Concepts and Applications

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

Explores data representation, databases, cloud computing, and challenges in the cloud environment.

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Spark Data Frames

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Handling Data: Data Models and Wrangling

Explores data handling fundamentals, including models, sources, and wrangling, emphasizing the importance of understanding and addressing data problems.

Google Analytics 4: Understand & set up GA4

Introduces Google Analytics 4 and guides on setting it up for WordPress websites, emphasizing key differences and event tracking.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Building a Data Model to Support a Scientific Usecase MOOC: Simulation Neurocience

Explores building a data model for scientific usecases and the significance of tracking data origin and usage.

Real-time Intelligence: Data Challenges and Hardware Evolution

Explores data challenges and hardware evolution for real-time intelligence in the era of big data.

Data Modeling: Concepts and Tools MOOC: Geographical Information Systems 1

Covers data modeling in GIS and software engineering, including entity-relationship models, associations, and modeling tools.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Model Representation: Structures and Functions

Covers the representation of mathematical models, functions, and data structures in information systems.

Data modeling: Geographic Information Systems MOOC: Geographical Information Systems 1

Introduces data modeling in the context of Geographic Information Systems, covering the added value and methods used.

Data Modeling: Concepts and Applications

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

Data Science Fundamentals

Covers the fundamentals of data science, emphasizing breadth over depth and practical application in machine learning and data analysis.

Analytics on Data at Rest and Data in Motion

Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.

Introduction: What do we mean by Data Science?

Introduces the team, provides a crash course on Python, and explores the journey into Data Science and the importance of refining data.

Database Systems: Challenges and Innovations

Explores challenges and innovations in database systems, emphasizing the need for efficient data management and adapting to modern hardware advancements.

Knowledge Representation: Semantics and Data Structures Log in to Mediaspace to watch this video

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.