# Useful Links

## Elementary Mathematics

School Mathematics

Mathematics Education Resources

Fun Mathematics

Teachernet

Recreational Mathematics

M-alpha

Mathematics Reference

Sage is a free open-source mathematics software system

Practicing Math by Playing with Python

Mathematics Education Resources

Fun Mathematics

Teachernet

Recreational Mathematics

M-alpha

Mathematics Reference

Sage is a free open-source mathematics software system

Practicing Math by Playing with Python

## Advance Mathematics

Open Problems

Math Equations

PDE Metasource

American Mathematical Society

A Statistics e-book

Sci-lab Linear Algebra, Calculus

Maxima

Dr Geo Geometric Figures

Euler Numerical Programming

Computer Algebra System

Singular

Macaulay 2

Cocoa

Kash/Kant

Polymake

CaTS

Math Equations

PDE Metasource

American Mathematical Society

A Statistics e-book

Sci-lab Linear Algebra, Calculus

Maxima

Dr Geo Geometric Figures

Euler Numerical Programming

Computer Algebra System

Singular

Macaulay 2

Cocoa

Kash/Kant

Polymake

CaTS

## Other

LaTeX Tutorial

Make TeX Work

Ubuntu Linux Guide

Computer Science--Bibliography

Basic Steps for Research (Guide)

E-books

POVRay Geometric Software

Statlib Data, Software and News from the Statistics Community

The R Project for Statistical Computing

Mathematics Notes

Resonance

Google Scholar

Interesting WWW sites about Science

Make TeX Work

Ubuntu Linux Guide

Computer Science--Bibliography

Basic Steps for Research (Guide)

E-books

POVRay Geometric Software

Statlib Data, Software and News from the Statistics Community

The R Project for Statistical Computing

Mathematics Notes

Resonance

Google Scholar

Interesting WWW sites about Science

## Mathematical
Career Guidance

- AMS Advice for Research
Students in Mathematics

Oded Goldreich’s article “On our duties as scientists“.

A few words on research for graduate students By Fan Chung

The Princeton Companion to Mathematics‘ section on advice to younger mathematicians, with contributions by Sir Michael Atiyah, Béla Bollobás, Alain Connes, Dusa McDuff, and Peter Sarnak.

Advice for the Young Scientist By John Baez

## Mathematical Writing

- Jean-Pierre Serre’s “How to write mathematics badly“
- Michèle Audin’s “Conseils aux auteurs de textes mathématiques“
- Oded Goldreich’s “How to write a paper“.
- Timothy Gowers on “writing examples first!”.
- Paul Halmos’ femous work “How to write mathematics”
- Ashley Reiter’s “Writing a research paper in mathematics“
- David Goss’ “Some hints on mathematical style“

- Statistics

- Books
__O’Reilly: Think Stats:__An introduction to Probability and Statistics for Python programmers.__Introduction to Probability:__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 course that teaches the complete details of statistical analysis.__MIT:Statistical Thinking and Data Analysis:__

__Khan Academy’s Statistics :__An introduction to statistics in a very lucid manner .

Data Science

- Books
__An Introduction to Data Science:__The companion textbook to Syracuse University’s flagship course for their new Data Science program.

- Courses
__UC Berkeley: Introduction to Data Science:__A course that highlights each of the varied skills that a Data Scientist must be proficient with.__CouHow to Process, Analyze and Visualize Data:__A lab oriented course that teaches you the entire pipeline of data science; from acquiring datasets and analyzing them at scale to effectively visualizing the results.__CMCoursera: Introduction to Data Science:__A tour of the basic techniques for Data Science including SQL and NoSQL databases, MapReduce on Hadoop, ML algorithms, and data visualization.__Columbia: Introduction to Data Science:__A very comprehensive course that covers all aspects of data science, with an humanistic treatment of the field.__Columbia: Applied Data Science (with book):__Another Columbia course — teaches applied software development fundamentals using real data, targeted towards people with mathematical backgrounds.__Coursera: Data Analysis (with notes and lectures):__An applied statistics course that covers algorithms and techniques for analyzing data and interpreting the results to communicate your findings.__Kaggle: Getting Started with Python for Data Science:__A guided tour of setting up a development environment, an introduction to making your first competition submission, and validating your results.__http://ischool.syr.edu/future/cas/applieddatasciencemooc.aspx__

Data Management

- Tools
__OpenRefine (formerly Google Refine):__A powerful tool for working with messy data, cleaning, transforming, extending it with web services, and linking to databases. Think Excel on steroids.__DataWrangler:__Stanford research project that provides an interactive tool for data cleaning and transformation.__sed:__“The ultimate stream editor” — used to process files with regular expressions often used for substitution.__awk:__“Another cornerstone of UNIX shell programming” — used for processing rows and columns of information.- Courses
__School of Data: A gentle introduction to cleaning data:__A hands on approach to learning to clean data, with plenty of exercises and web resources.__Predictive Analytics: Data Preparation:__An introduction to the concepts and techniques of sampling data, accounting for erroneous values, and manipulating the data to transform it into acceptable formats.

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 to learning data mining and ML.__Elements of Statistical Learning:__One of the most comprehensive treatments of data mining and ML.__An Introduction to Information Retrieval:__Textbook from a Stanford course on NLP and information retrieval.

- 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.

Data 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 (with__Python port__).__Vega:__A visualization grammer built on top of D3 for declarative visualizations in JSON. Released by the dream team at__Trifacta,__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.

Large Scale Computations

- Books
__Mining Massive Datasets:__Mining Massive Datasets: Stanford course resources on large scale machine learning and MapReduce with accompanying__book.____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.