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Data Science Fundamentals
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Related lectures (46)
Excel Upgrade: Advanced Functions and Data Analysis
Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.
Data Science for Engineers: Part 2
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Explores data manipulation, exploration, and visualization in data science projects using Python.
Understanding Data Models
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Explores the significance of data models in scientific research and the challenges posed by big data.
Urban Metabolism Analysis
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Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Digital History and Digitized Press
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Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.
Introduction to Data Science
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Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Air Pollution Data Analysis
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Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.
Deep Learning: Data, Models, and Challenges
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Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.
Machine Learning Basics
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Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Robert Boyle's Literary Technology
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Explores Robert Boyle's literary technology and its impact on knowledge production and communication.
Communication: Computer-Mediated Communication
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Explores computer-mediated communication tools, media richness, grounding theory, biases in communication, and managing virtual teams.
Boolean Hidden Matching Problem
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Covers the Boolean Hidden Matching Problem and its applications to lower bounds.
Introduction to NLP and the Course
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Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.
Principal Component Analysis: Applications and Limitations
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Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.
Data Management: Overview
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Introduces fundamental concepts of data management, including data models, databases, and key tasks.
Image Processing: Practical
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Covers practical image processing using Fiji software, emphasizing data quality importance.
Near Neighbors Retrieval: Efficient Techniques
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Covers techniques for efficiently retrieving similar items using similarity search queries.
Homomorphic Encryption
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Introduces homomorphic encryption, enabling computation on encrypted data without decryption, covering security, applications, and practical aspects.
Designing Structures: Marshmallow Challenge and Mechanics
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Covers the Marshmallow Challenge and explores structural mechanics concepts like beams, bars, and trusses.
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