Big Data Analytics Virtual Summer School

These are the lectures from the Caltech-JPL Virtual Summer School in Big Data Analytics recorded in 2014-2015, and offered on Coursera. These were given by a group of lecturers from Caltech and JPL. The videos linked below are on the Caltech YouTube website. There is also a FaceBook group associated with it.

Best Programming Practices - A. Mahabal

Best Programming Practices (Part 1) * Slides (pdf) * Video
Best Programming Practices (Part 2) * Slides (pdf) * Video
Best Programming Practices (Part 3) * Slides (pdf) * Video
Best Programming Practices (Part 4) * Slides (pdf) * Video
Exercise: Best Programming Practice (pdf)

Databases - M. Graham

Data * Slides (pdf) * Video
Data Models * Slides (pdf) * Video
Relational Databases * Slides (pdf) * Video
SQL * Slides (pdf) * Video
Advanced SQL * Slides (pdf) * Video
Alternative database * Slides (pdf) * Video
Database Exercise 1 (pdf) , Database Exercise 2 (pdf) , and a zipped folder

Statistics: Basic Probability and Inference - A. Braverman

Inference and Uncertainty * Slides (pdf) * Video
Basic Probability - 1 * Slides (pdf) * Video
Basic Probability - 2 * Slides (pdf) * Video
Basics of Inference - 1 * Slides (pdf) * Video
Basics of Inference - 2 * Slides (pdf) * Video
The Bootstrap * Slides (pdf) * Video
Subsampling * Slides (pdf) * Video
Statistics exercise (pdf) and a demo data file (R)

Statistics: Introduction to R - A. Mahabal

R (Part 1) * Slides (pdf) * Video
R (Part 2) * Slides (pdf) * Video
R (Part 3) * Slides (pdf) * Video
R (Part 4) * Slides (pdf) * Video
R (Part 5) * Slides (pdf) * Video
R (Part 6) * Slides (pdf) * Video
R (Part 7) * Slides (pdf) * Video
R exercises (pdf) and a demo data file (txt)

Machine Learning: Introduction, Classification, and Clustering - C. Donalek

Introduction to Machine Learning: General Aspects * Slides (pdf) * Video
Introduction to Machine Learning: Supervised Learning * Slides (pdf) * Video
Introduction to Machine Learning: Unsupervised Learning * Slides (pdf) * Video
Classification: general aspects * Slides (pdf) * Video
Classification: Neural Networks * Slides (pdf) * Video
Classification: overview on models and tools * Slides (pdf) * No Video
Clustering: General Aspects * Slides (pdf) * Video
Clustering: k-Means * Slides (pdf) * Video
Clustering: Self-Organizing Maps * Slides (pdf) * Video
Exercises: Classification and Clustering (pdf) and a demo data file (tab)

Machine Learning: Decision Trees and Random Forests - T. Fuchs

Decision Trees * Slides (pdf) * Video
Random Forests * Slides (pdf) * Video
Properties of Random Forests * Slides (pdf) * Video
Random Forests in Space Exploration * Slides (pdf) * Video
Random Forests in Cancer Research * Slides (pdf) * Video
Exercise 1 (pdf) , Exercise 2 (pdf) , and
a zipped folder with the data

Machine Learning: Dimensionality Reduction - D. Thompson

Local Methods for Pattern Recognition * Slides (pdf) * Video
Nearest Neighbors and the Curse of Dimensionality * Slides (pdf) * Video
Feature Selection * Slides (pdf) * Video
Linear Dimensionality Reduction * Slides (pdf) * Video
Metric Learning * Slides (pdf) * Video
Nonlinear Dimensionality Reduction: KPCA * Slides (pdf) * Video
Dimensionality reduction exercises (pdf) and a zipped data folder

Visualization, Part I - S. Lombeyda

What is Visualization? * Slides (pdf) * Video
Understanding the Landscape * Slides (pdf) * Video
A Tool Taxonomy * Slides (pdf) * Video
Principles of Data Representation * Slides (pdf) * Video
On Color * Slides (pdf) * Video
On Mapping Multiple Dimensions * Slides (pdf) * Video
Addressing Bottlenecks * Slides (pdf) * Video
Putting It All Together * Slides (pdf) * Video

Visualization, Part II - S. Davidoff

Brief Introduction to Data Visualization * Slides (pdf) * Video
Perception and Dimensional Mapping * Slides (pdf) * Video
Visual Communication Fundamentals * Slides (pdf) * Video
Multi-dimensional Mapping * Slides (pdf) * Video
Graphs and Trees * Slides (pdf) * Video
Interaction * Slides (pdf) * Video
Some examples (zipped folder)

Cloud Computing, Semantics, and Genetic Algorithms - J. Bunn, M. Graham

Introduction to Cloud Computing - J. Bunn * Slides (pdf) * Video
Algorithmic Approaches to Big Data - M. Graham * Slides (pdf) * Video
Matthew Graham: Semantics (Part 1) * Slides (pdf) * Video
Matthew Graham: Semantics (Part 2) * Slides (pdf) * Video
RDF exercise (python notebook)
Practical Genetic Algorithms - J. Bunn * Slides (pdf) * Video
Exercises: Genetic Algorithms (pdf)

Big Data Architecture and Content Detection - C. Matmann

Big Data Architecture: Fundamentals (Part 1) * Slides (pdf) * Video
Big Data Architecture: Fundamentals (Part 2) * Slides (pdf) * Video
Big Data Architecture: Fundamentals (Part 3) * Slides (pdf) * Video
Exercises: big data architectures (pdf)
Content Detection and Analysis for Big Data (Part 4) * Slides (pdf) * Video
Content Detection and Analysis for Big Data (Part 5) * Slides (pdf) * Video
Content Detection and Analysis for Big Data (Part 6) * Slides (pdf) * Video
Exercises: content detection (pdf)