DATA SCIENCE RESOURCES: Large Scale Computations

Large Scale Computations

When you start operating with data at the scale of the web, the fundamental approach and process of analysis must change. To combat the ever increasing amount of data, Google developed theMapReduce  paradigm. This programming model has become the de facto standard for large scale batch processing since the release of ApacheHadoop  in 2007, the open-source MapReduce framework

• Books
• Mining Massive Datasets:Mining Massive Datasets: Stanford course resources on large scale machine learning and MapReduce with accompanyingbook. 
• Data-Intensive Text Processing with MapReduce:Data-Intensive Text Processing with MapReduce: An introduction to algorithms for the indexing and processing of text that teaches you to “think in MapReduce.”
• Hadoop: The Definitive Guide: The most thorough treatment of the Hadoop framework, a great tutorial and reference alike.
• Programming Pig: An introduction to the Pig framework for programming data flows on Hadoop.

• Courses
• UC Berkeley: Analyzing Big Data with Twitter: A course — taught in close collaboration with Twitter — that focuses on the tools and algorithms for data analysis as applied to Twitter microblog data (with project based curriculum).
• Coursera: Web Intelligence and Big Data: An introduction to dealing with large quantities of data from the web; how the tools and techniques for acquiring, manipulating, querying, and analyzing data change at scale.
• CMU: Machine Learning with Large Datasets: A course on scaling machine learning algorithms on Hadoop to handle massive datasets.
• U of Chicago: Large Scale Learning: A treatment of handling large datasets through dimensionality reduction, classification, feature parametrization, and efficient data structures.
• UC Berkeley: Scalable Machine Learning: A broad introduction to the systems, algorithms, models, and optimizations necessary at scale.

DATA SCIENCE RESOURCES: Visualization

Visualization

• Books
• Tufte: The Visual Display of Quantitative Information:Not freely available, but perhaps the most influential text for the subject of data visualization. A classic that defined the field.

• Courses
• UC Berkeley: Visualization: UC Berkeley: Visualization: Graduate class on the techniques and algorithms for creating effective visualizations.
• Rice: Data Visualization: Rice: Data Visualization: A treatment of data visualization and how to meaningfully present information from the perspective of Statistics.
• Harvard: Introduction to Computing, Modeling, and Visualization: Connects the concepts of computing with data to the process of interactively visualizing results.
• School of Data: From Data to Diagrams: A gentle introduction to plotting and charting data, with exercises.
• Predictive Analytics: Overview and Data visualization: An introduction to the process of predictive modeling, and a treatment of the visualization of its results.
• Tools
• D3.js: Data-Driven Documents — Declarative manipulation of DOM elements with data dependent functions (withPython port ).
• Vega: A visualization grammer built on top of D3 for declarative visualizations in JSON. Released by the dream team atTrifacta,   it provides a higher level abstraction than D3 for creating “ or SVG based graphics.
• Rickshaw: A charting library built on top of D3 with a focus on interactive time series graphs.
• modest maps: A lightweight library with a simple interface for working with maps in the browser (with ports to multiple languages).
• Chart.js: Very simple (only six charts) HTML5 “ based plotting library with beautiful styling and animation.

DATA SCIENCE RESOURCES: Statistics

Statistics

• Books
• O’Reilly: Think Stats:An introduction to Probability and Statistics for Python programmers.
• Introduction to Probability: Textbook for Berkeley’s Stats 134 class, an introductory treatment of probability with complementary exercises.
• Lecture notes for Introduction to Probability: Compiled lecture notes of above textbook, complete with exercises.
• OpenIntro: Statistics: Introductory text book with supplementary exercises and labs in an online portal.
• Think Bayes: An simple introduction to Bayesian Statistics with Python code examples.

• Courses
• edx:Introduction to Statistics: A basic introductory statistics course.
• Coursera: Statistics one : A first course of Statistics from Andrew Conway of Princeton University
• Coursera: Statistics, Making sense of Data: A applied Statistics course that teaches the complete pipeline of statistical analysis.
• MIT:Statistical Thinking and Data Analysis: Introduction to probability, sampling, regression, common distributions, and inference.
• Khan Academy’s Statistics : A wonderful introduction to all things statistics in a very lucid manner .

DATA SCIENCE RESOURCES: Machine Learning and Algorithms

Machine Learning and Algorithms

• Books
• A first encounter with Machine Learning: An introduction to machine learning concepts focusing on the intuition and explanation behind whythey work.
• A Programmer’s Guide to Data Mining: A web based book complete with code samples (in Python) and exercises.
• Data Structures and Algorithms: An introduction to computer science with code examples in Python — covers algorithm analysis, data structures, sorting algorithms, and object oriented design.
• An Introduction to Data Mining: An interactive Decision Tree guide (with hyperlinked lectures) to learning data mining and ML.
• Elements of Statistical Learning: One of the most comprehensive treatments of data mining and ML, often used as a university textbook.
• An Introduction to Information Retrieval: Textbook from a Stanford course on NLP and information retrieval with sections on text classification, clustering, indexing, and web crawling.

• Courses
• Coursera: Machine Learning: Stanford’s famous machine learning course taught by Andrew Ng.
• Coursera: Computational Methods for Data Analysis: Statistical methods and data analysis applied to physical, engineering, and biological sciences.
• MIT: Data Mining: MIT: Data Mining: An introduction to the techniques of data mining and how to apply ML algorithms to garner insights.
• edx: Introduction to Artificial Intelligence: The first half of Berkeley’s popular AI course that teaches you to build autonomous agents to efficiently make decisions in stochastic and adversarial settings.
• edx: Introduction to Computer Science and Programming: MIT’s introductory course to the theory and application of Computer Science.

Tools for Social Network Analysis

Here are the list of open-source tools for analyzing social networks

Gephi (http://gephi.org/). Visualization and basic network metrics.
NetLogo (modeling network dynamics)
iGraph (for programming)

Pajek (http://pajek.imfm.si/doku.php). Very extensive functionality via drop-down menus. Open-Source. Works only on windows.

NodeXL (http://nodexl.codeplex.com/). SNA integrated into Excel. Windows-only. Free. In beta.

NetworkX (http://networkx.lanl.gov/). Extensive functionality. Open Source. Scales to large networks by taking advantage of existing C, Fortran libs.

SNA Package for R (http://cran.r-project.org/web/packages/sna/index.html). Extensive, statistics-heavy functionality

Social Network Image Animator (http://www.stanford.edu/group/sonia/)

Books: 
Exploratory Social Network Analysis with Pajek (Structural Analysis in the Social Sciences)
Social Network Analysis: History, Theory and Methodology
Understanding Social Networks: Theories, Concepts, and Findings

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Python for bigdata