Lectures related to Distributed Computing: Challenges and Solutions

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Data Wrangling with Hive: Managing Big Data Efficiently

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

Data Warehousing: Overview and Challenges

Introduces data warehousing fundamentals, challenges, and the innovative concept of a 'lakehouse'.

Introduction to Data: Data Types and Quality

Covers data types, quantity, quality, and representativeness in the world of data.

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Fast and Effective Analytics for Big Data: Multi-Dimensional Insights

Explores challenges and solutions in analyzing big multi-dimensional data, focusing on complex data types and anomaly detection.

Big Data: Processing and Dimensions

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Information Systems: Overview

Covers the overview of information systems, data modeling, managing data, and the distinction between data and information.

Introduction to Machine Learning

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Modeling: Concepts and Applications

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

Advanced C Data Types

Covers advanced data types and memory management in C programming, emphasizing type consistency and dynamic array allocation.

Handling Data: Data Models and Wrangling

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

Image Processing Techniques: Seam Carving and Pixel Manipulation

Discusses image processing techniques, focusing on seam carving and pixel manipulation in Python programming.

Data Warehouses and Data Lakes

Covers data warehouses, data lakes, OLTP vs. OLAP, data quality, and the Data Lakehouse concept.

Python Programming: Dictionaries and Classes

Introduces Python programming concepts, focusing on dictionaries and classes, including their definitions, usage, and practical examples.

Advanced Types in C: Enums, Typedefs, and Structs

Discusses advanced types in C, including enumerated types, typedefs, and structures, with practical examples to illustrate their usage.

Data Warehouses: Introduction and Challenges

Covers the introduction and challenges of data warehouses, including integrating data, managing metadata, and optimizing query performance.

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.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.