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1.1 Caltech Welcome - S.G. Djorgovski
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1.2 JPL Welcome - R. Doyle and D. Crichton
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2.1 Ashish Mahabal: Best Programming Practices (Part 1)
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2.2 Ashish Mahabal: Best Programming Practices (Part 2)
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2.3 Ashish Mahabal : Best Programming Practices (Part 3)
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2.4 Ashish Mahabal : Best Programming Practices (Part 4)
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3.1 Matthew Graham: Data (Part 1)
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3.2 Matthew Graham: Data Models (Part 2)
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3.3 Matthew Graham: Relational Databases (Part 3)
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3.4 Matthew Graham: SQL 1 (Part 4)
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3.5 Matthew Graham: Advanced SQL (Part 5)
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3.6 Matthew Graham: Alternative database (Part 6)
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4.1 Amy Braverman (Part 1): Inference and Uncertainty
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4.2 Amy Braverman (Part 2): Basic Probability - 1
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4.3 Amy Braverman (Part 3): Basic Probability - 2
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4.4 Amy Braverman (Part 4): Basics of Inference - 1
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4.5 Amy Braverman (Part 5): Basics of Inference - 2
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4.6 Amy Braverman (Part 6): The Bootstrap
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4.7 Amy Braverman (Part 7): Subsampling
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5.1 Ashish Mahabal : R (Part 1)
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5.2 Ashish Mahabal : R (Part 2)
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5.3 Ashish Mahabal : R (Part 3)
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5.4 Ashish Mahabal : R (Part 4)
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5.5 Ashish Mahabal : R (Part 5)
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5.6 Ashish Mahabal : R (Part 6)
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5.7 Ashish Mahabal : R (Part 7)
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6.1 Ciro Donalek: Introduction to Machine Learning: General Aspects
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6.2 Ciro Donalek: Introduction to Machine Learning: Supervised Learning
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6.3 Ciro Donalek: Introduction to Machine Learning: Unsupervised Learning
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6.4 Ciro Donalek: Classification: general aspects
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6.5 Ciro Donalek: Classification: Neural Networks
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6.6 Ciro Donalek: Clustering: General Aspects
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6.7 Ciro Donalek: Clustering: k-Means
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6.8 Ciro Donalek: Clustering: Self-Organizing Maps
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7.1 Thomas Fuchs: Lecture 1: Decision Trees
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7.2 Thomas Fuchs: Lecture 2: Random Forests
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7.3 Thomas Fuchs: Lecture 3: Properties of Random Forests
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7.4 Thomas Fuchs: Lecture 4: Random Forests in Space Exploration
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7.5 Thomas Fuchs: Lecture 5: Random Forests in Cancer Research
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8.1 David Thompson (Part 1): Local Methods for Pattern Recognition
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8.2 David Thompson (Part 2): Nearest Neighbors and the Curse of Dimensionality
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8.3 David Thompson (Part 3): Feature Selection
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8.4 David Thompson (Part 4): Linear Dimensionality Reduction
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8.5 David Thompson (Part 5): Metric Learning
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8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA
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9.1 Santiago Lombeyda: Lecture 1: What is Visualization?
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9.2 Santiago Lombeyda: Lecture 2: Understanding the Landscape
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9.3 Santiago Lombeyda: Lecture 3: A Tool Taxonomy
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9.4 Santiago Lombeyda: Lecture 4: Principles of Data Representation
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9.5 Santiago Lombeyda: Lecture 4a: ... on Color
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9.6 Santiago Lombeyda: Lecture 4b: ... on Mapping Multiple Dimensions
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9.7 Santiago Lombeyda: Lecture 5: Addressing Bottlenecks
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9.8 Santiago Lombeyda: Lecture 6: Putting It All Together
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10.1 Scott Davidoff (Part 1): Brief Introduction to Data Visualization
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10.2 Scott Davidoff (Part 2): Perception and Dimensional Mapping
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10.3 Scott Davidoff (Part 3): Visual Communication Fundamentals
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10.4 Scott Davidoff (Part 4): Multi-dimensional Mapping
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10.5 Scott Davidoff (Part 5): Graphs and Trees
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10.6 Scott Davidoff (Part 6): Interaction
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11.1 Introduction to Cloud Computing - J. Bunn
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11.2 Algorithmic Approaches to Big Data - M. Graham
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11.3 Matthew Graham: Semantics (Part 1)
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11.4 Matthew Graham: Semantics (Part 2)
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11.5 Practical Genetic Algorithms - J. Bunn
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12.1 Chris Mattmann (Part 1): Big Data Architecture: Fundamentals
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12.2 Chris Mattmann (Part 2): Big Data Architecture: Fundamentals
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12.3 Chris Mattmann (Part 3): Big Data Architecture: Fundamentals
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12.4 Chris Mattmann (Part 4): Content Detection and Analysis for Big Data
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12.5 Chris Mattmann (Part 5): Content Detection and Analysis for Big Data
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12.6 Chris Mattmann (Part 6): Content Detection and Analysis for Big Data
Description:
Dive into a comprehensive 19-hour course on Big Data Analytics offered by the California Institute of Technology. Explore best programming practices, data models, relational databases, SQL, and alternative databases. Learn about inference, uncertainty, and probability basics. Master the R programming language for data analysis. Delve into machine learning concepts, including supervised and unsupervised learning, classification, and clustering techniques. Study decision trees, random forests, and their applications in space exploration and cancer research. Examine pattern recognition, dimensionality reduction, and feature selection methods. Discover data visualization principles, tools, and techniques for effective communication. Investigate cloud computing, algorithmic approaches to big data, semantics, and genetic algorithms. Conclude with an in-depth look at big data architecture fundamentals and content detection and analysis for large-scale datasets.

Big Data Analytics

California Institute of Technology
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#Data Science #Big Data #Data Analysis #Programming #Domain-Specific Languages (DSL) #SQL #Databases #Relational Databases #Mathematics #Statistics & Probability #Probability #Database Design #Data Modeling #Big Data Analytics #Computer Science #Machine Learning #Inference
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1.1 Caltech Welcome - S.G. Djorgovski