*Written by Jun Wu.*

Every year since I worked on wall street, traditional trading-is-an-art-form traders are leaving and retiring.

Their jobs are replaced by a new breed of experts who are savvy with numbers, systems, and the market. These experts don’t sleep, eat or drink. They are the AI Systems that can run on a thousand machines to…

ContinueAdded by Capri Granville on September 22, 2019 at 1:30pm — No Comments

This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A.

Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes. Most subjects presented here are covered rather minimally; we intend…

ContinueAdded by Capri Granville on September 22, 2019 at 11:30am — No Comments

The charts below represents the main findings of some recent analysis of 1,000 data scientist LinkedIn profiles, using a web scraper. It was limited to Singapore, and for people having "data scientist" on their profile. Of course, many have a different job title especially in fields such as Fintech (quant engineer) or Healthcare (biostatistician) but the findings are interesting nevertherless and seem to apply to other locales as well.

The first chart features the educational…

ContinueAdded by Capri Granville on September 12, 2019 at 10:00am — No Comments

Interesting analysis done in R, about salaries of R developers broken down by country, featuring salary range and median salary.

The dataset consists of survey answers from nearly 90,000 respondents. About 5,000 of them reported using R for “extensive development work over the past year”. The first filter used reduces the dataset from 88,883 respondents to 5,048. The second filter…

ContinueAdded by Capri Granville on September 5, 2019 at 7:00am — No Comments

I found an interesting websites featuring hundreds of charts derived from US census data. It shows contrasts between states, cities, regarding education, jobs, languages spoken, salaries, even discrepencies between men and women or Asians and Caucasians, regarding various metrics broken down by location, education, or other criteria. I selected four of these charts.…

ContinueAdded by Capri Granville on June 10, 2019 at 5:00pm — No Comments

This chart communicates the same insights as a contour plot. What is interesting is the choice of hexagonal buckets (rather than squares) to aggregate data. In fact, any tessellation would work, in particular Voronoi tessellations.…

ContinueAdded by Capri Granville on June 9, 2019 at 8:00am — 1 Comment

*Not to be confused with this free Microsoft book with same title.*

Data science underlies Amazon's product recommender, LinkedIn's People You Know feature, Pandora's personalized radio stations, Stripe's fraud detectors, and the incredible insights arising from the world's increasingly ubiquitous sensors. In the future,…

ContinueAdded by Capri Granville on May 23, 2019 at 9:00am — No Comments

I stumbled upon this book by chance, when searching for material about time series (probably the most interesting chapter in this collection.) The various chapters are accessible from the top tabs, on this web page. It is mostly about R, but it has a few interesting chapters on statistical science too. Below is a…

ContinueAdded by Capri Granville on May 23, 2019 at 9:00am — No Comments

*By Avrim Blum, John Hopcroft, and Ravindran Kannan (2018). *

Computer science as an academic discipline began in the 1960s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970s, the study of algorithms was added as an important component of…

ContinueAdded by Capri Granville on May 23, 2019 at 9:00am — No Comments

There are plenty of resources on the Internet to learn linear algebra or to get a refresher, including our own tutorial (here). Below are three interesting books found on Amazon. …

ContinueAdded by Capri Granville on May 23, 2019 at 9:00am — 1 Comment

The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here…

ContinueAdded by Capri Granville on May 23, 2019 at 9:00am — No Comments

A free online version of the second edition of the book based on Stat 110, *Introduction to Probability* by Joe Blitzstein and Jessica Hwang, is now available here. Print copies are available via CRC Press, Amazon, and…

Added by Capri Granville on May 23, 2019 at 8:30am — 1 Comment

This glossary defines general machine learning terms as well as terms specific to TensorFlow. Below is a small selection of the most popular entries. You can access this glossary here. For other related glossaries, follow this link.

- A/B testing
- activation…

Added by Capri Granville on May 23, 2019 at 8:30am — No Comments

Interesting cartoon featuring the decision tree used in medical diagnosis. To see other cartoons about data science, follow this link.…

ContinueAdded by Capri Granville on April 25, 2019 at 9:00am — 1 Comment

Lecture notes for the Statistical Machine Learning course taught at the Department of Information Technology, University of Uppsala (Sweden.) Updated in March 2019. Authors: Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön.

*Source: page 61 in these lecture notes…*

Added by Capri Granville on April 25, 2019 at 9:00am — No Comments

*Guest blog by Stephan Loyd.*

Hello everybody, this is my first post here, so forgive me if I screw it up.

Let me firstly introduce background of my work. Several years ago I landed onto a Perl job. It also involves some other languages like Python and R, but it was mainly Perl, until last year focus of my role switched and I still do some Perl but much less since then. I was a little…

ContinueAdded by Capri Granville on April 14, 2019 at 6:30pm — No Comments

This 12-page document lists hundreds of Excel functions and formulas, covering

- Lookup/Ref Functions
- String/Text Functions
- Date/Time Functions
- Math/Trig Functions
- Statistical Functions
- Logical Functions
- Information Functions
- Financial Functions
- Database Functions
- Engineering Functions
- File/Directory Functions
- Data Type Conv. Functions
- More Lookup…

Added by Capri Granville on April 7, 2019 at 6:30am — No Comments

*This is the fourth article in a* DeepTech Series* by Margaretta Colangelo and Dmitry Kaminskiy. **Dmitry Kaminskiy**, General Partner at Deep Knowledge Ventures, is based in London. Dmitry is Managing Trustee…*

Added by Capri Granville on April 4, 2019 at 10:30am — No Comments

Creating *Info We Trust* is a craft that puts the world into forms that are strong and true. It begins with maps, diagrams, and charts — but must push further than dry defaults to be truly effective. How do we attract attention? How can we offer audiences valuable experiences worth their time? How can we help people access complexity?…

Added by Capri Granville on April 3, 2019 at 8:00am — No Comments

I was comparing home prices in San Francisco between 1994 and 2018, and I noticed that it has increased by a factor 4 over 25 years. In the meanwhile, the inflation index increased by a factor 1.7 (see here.) I am not saying here that my sources are correct or wrong -- entire books have been written on the subject -- but instead, my purpose here is to show how some visualizations can be…

ContinueAdded by Capri Granville on March 10, 2019 at 5:00pm — 1 Comment

- AI Trading the Market
- The Math of Machine Learning - Berkeley University Textbook
- What are the Typical Data Scientist Profiles on LinkedIn? Survey Results
- R Users’ Salaries from the 2019 Stackoverflow Survey
- Interesting Charts and Maps Obtained Using Census Data
- Interesting Type of Chart: Hexagonal Binning
- Upcoming Book: Foundations of Data science

- The Math of Machine Learning - Berkeley University Textbook
- Probability Cheat Sheet - Harvard University
- Traditional Programming versus Machine Learning, in One Picture
- Which Programming Language to Choose?
- Free Book: Introduction to Statistics
- Free Textbook: Probability Course, Harvard University (Based on R)
- New Data Science Cheat Sheet, by Maverick Lin

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions