A Data Science Central Community

This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies. All this material is available for free, and consists of content mostly created in 2019 and 2018, by various top experts in their respective fields. A few of these documents are available on LinkedIn: see last…

ContinueAdded by Vincent Granville on October 13, 2019 at 11:00am — No Comments

I have used synthetic data sets many times for simulation purposes, most recently in my articles Six degrees of Separations between any two Datasets and How to Lie with p-values. Many…

ContinueAdded by Vincent Granville on October 2, 2019 at 5:00pm — No Comments

This is an interesting data science conjecture, inspired by the well known six degrees of separation problem, stating that there is a link involving no more than 6 connections between any two people on Earth, say between you and anyone living (say) in North Korea.

Here the link is between any two univariate data sets…

ContinueAdded by Vincent Granville on September 9, 2019 at 10:30am — No Comments

The material discussed here is also of interest to machine learning, AI, big data, and data science practitioners, as much of the work is based on heavy data processing, algorithms, efficient coding, testing, and experimentation. Also, it's not just two new conjectures, but paths and suggestions to solve these problems. The last section contains a few new, original exercises, some with solutions, and may be useful to students, researchers, and instructors offering math and statistics classes…

ContinueAdded by Vincent Granville on September 8, 2019 at 4:09am — No Comments

Being extremely versatile general purpose, professional programming language, Python offers plenty of applications. Python language is user-friendly and simple to grasp and this made it popular throughout the world. Python plays a critical role for data scientists to find out lucrative job opportunities.

Today, Python has become the most in-demand programming language in the data science world. Python offers an extensive range…

ContinueAdded by Divyesh Aegis on September 5, 2019 at 12:00am — No Comments

Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners.

To demystify machine learning and to offer a learning path for those who are new to the core…

ContinueAdded by Vincent Granville on August 30, 2019 at 11:08am — No Comments

I introduce here a family of very peculiar statistical distributions governed by two parameters: *p*, a real number in [0, 1], and *b*, an integer > 1.

Potential applications are found in cryptography, Fintech (stock market modeling), Bitcoin, number theory, random number…

ContinueAdded by Vincent Granville on August 30, 2019 at 10:11am — No Comments

Continued fractions are usually considered as a beautiful, curious mathematical topic, but with applications mostly theoretical and limited to math and number theory. Here we show how it can be used in applied business and economics contexts, leveraging the mathematical theory developed for continued fraction, to model and explain natural phenomena. …

ContinueAdded by Vincent Granville on August 30, 2019 at 9:42am — No Comments

In the data-driven enterprise system, Spark has become a popular name that is easy to use, offer speed and versatility. The data can be understood at fast speed allowing one to make faster decisions. The Big Data has a huge benefit with the faster data processing of Spark. This clustering of large datasets works with a framework in open source that helps in analyzing. The codes are done in the Scala that has made it possible and easier for data processing that gives a certain boost to the…

ContinueAdded by Divyesh Aegis on August 13, 2019 at 12:51am — No Comments

In my previous posts, I compared model evaluation techniques using Statistical Tools & Tests and commonly used Classification and Clustering evaluation techniques

In this post, I'll take a look at how you can compare regression models. Comparing regression models is perhaps one of the trickiest tasks to complete in the "comparing models" arena; The reason is that there are literally dozens of statistics you can calculate to compare regression models, including:

**1.…**

Added by Vincent Granville on August 8, 2019 at 10:37am — No Comments

Sometimes, you see a diagram and it gives you an ‘aha ha’ moment. Here is one representing forward propagation and back propagation in a neural network:

A brief explanation is:

- Using the input variables x and y, The forwardpass (left half of the figure) calculates output z as a function of x and y i.e. f(x,y)
- The right side…

Added by Vincent Granville on August 8, 2019 at 10:29am — No Comments

Decision Trees, Random Forests and Boosting are among the top 16 data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:

- A decision tree is a simple, decision making-diagram.
- Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
- Gradient boosting machines also combine decision trees, but start the combining…

Added by Vincent Granville on August 8, 2019 at 10:25am — No Comments

Properly implemented Machine Learning (ML) models can have a positive effect on organizational efficiency. It is first necessary to understand how these models are created, how they function, and how they are put into production.

**The Definition of a Machine Learning Model**

When a computer is presented with questions within a particular domain, a machine learning model will run an algorithm that will enable it to resolve those questions. These algorithms are not…

ContinueAdded by Arash Aghlara on August 7, 2019 at 3:30am — No Comments

Python is an extremely popular programming language. It is not just apt for generic purposes but it is extremely easy to read and use as well. The main reason why Python is used by a majority of people these days is the fact that it allows the programmers to save their time by using only limited lines of codes. In order to accomplish tasks, the developers do not have to spend a lot of time on coding, unlike the other languages. Rather, all they can do is, spend time on…

ContinueAdded by Divyesh Aegis on July 25, 2019 at 12:53am — No Comments

Python was introduced in 1991 by Guido Van Rossum as a high level, general purpose language. Even today, it supports multiple programming paradigms including procedural, object oriented and functional. Soon, it became one of the most popular languages in the industry, and in fact is the very language that influence Ruby and Swift. Even TIOBE Index reports mentions python as the third most popular…

ContinueAdded by Divyesh Aegis on July 16, 2019 at 12:55am — No Comments

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum (or trending behavior as shown in the figure below), its price on the current period is more likely to increase (decrease) if it has already increased (decreased) on the previous period.

When the return of a stock at time t depends in some way on the return at the previous time t-1, the returns are said to be autocorrelated. In…

ContinueAdded by Vincent Granville on July 8, 2019 at 10:25am — No Comments

*Summary:** The annual Burtch Works salary survey tells us a lot about which industries are using the most data scientists and the difference between higher and lower skilled data scientists. Salary increases show us whether demand is increasing, and finally we take a shot at determining which skills are most in demand.*

What a difference a few years can make. We used to say that everyone loves a data scientist – and wants to be one. …

ContinueAdded by Vincent Granville on July 8, 2019 at 10:18am — No Comments

By Ajit Jaokar. This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge the gap between high school maths and deep learning.

**Background**

As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University of Oxford, I see more students who are familiar with programming than with mathematics.

They have last learnt maths…

ContinueAdded by Vincent Granville on June 27, 2019 at 12:22pm — No Comments

Originally published in 2014 and viewed more than 200,000 times, this is the oldest data science cheat sheet - the mother of all the numerous cheat sheets that are so popular nowadays. I decided to update it in June 2019. While the first half, dealing with installing components on your laptop and learning UNIX, regular expressions, and file management hasn't changed much, the second half, dealing with machine learning, was rewritten entirely from scratch. It is amazing how things changed in…

ContinueAdded by Vincent Granville on June 6, 2019 at 8:27pm — No Comments

It will be unwise to expect you will generate lot of sales if you have significant amount of web traffic. It alone cannot be of much help in this matter. You will need to track the website metrics properly in order to take necessary measure to convert the traffic into your business prospects. You will need to analyze your website from time to time to ensure that it is not only accessible to the users but also provides all necessary guidance to show them the right way to make a…

ContinueAdded by Jenny Richards on June 6, 2019 at 1:30am — No Comments

- 40+ Modern Tutorials Covering All Aspects of Machine Learning
- New Books in AI, Machine Learning, and Data Science
- Python as a tool benefiting data scientists in many ways
- Different Ways to Incorporate Data in Business Strategy for Security
- The Power of Machine Learning Models
- Is Python Completely Object Oriented?
- Questions To Answer And Factors To Consider For Web Analytics

- 40+ Modern Tutorials Covering All Aspects of Machine Learning
- Surprising Uses of Synthetic Random Data Sets
- Six Degrees of Separation Between Any Two Data Sets
- Two New Deep Conjectures in Probabilistic Number Theory
- Python as a tool benefiting data scientists in many ways
- 10 Machine Learning Methods that Every Data Scientist Should Know
- A Strange Family of Statistical Distributions

- 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know
- Data science jobs not requiring human interactions
- Data Science – the Foundation for Leading Banks
- The 8 worst predictive modeling techniques
- Monte Carlo Analysis and Simulation
- Fake data science
- Machine Learning with Python- Why do they form the best combination

- analytics (140)
- data (137)
- asymptotix (131)
- Analytics (125)
- Data (113)
- Business (49)
- predictive (46)
- big (44)
- Intelligence (42)
- Big (42)

**2019**

**2018**

- December (3)
- November (1)
- October (3)
- September (8)
- August (13)
- July (6)
- June (7)
- May (16)
- April (10)
- March (10)
- February (14)
- January (14)

**2017**

- December (7)
- November (14)
- October (15)
- September (13)
- August (17)
- July (13)
- June (9)
- May (10)
- April (23)
- March (8)
- February (8)
- January (10)

**2016**

- December (12)
- November (24)
- October (3)
- September (4)
- August (17)
- July (19)
- June (6)
- May (21)
- April (14)
- March (15)
- February (13)
- January (11)

**2015**

- December (25)
- November (19)
- October (24)
- September (21)
- August (26)
- July (34)
- June (30)
- May (16)
- April (21)
- March (17)
- February (25)
- January (19)

**2014**

- December (29)
- November (29)
- October (36)
- September (15)
- August (18)
- July (40)
- June (29)
- May (24)
- April (38)
- March (42)
- February (49)
- January (67)

**2013**

- December (66)
- November (76)
- October (79)
- September (90)
- August (106)
- July (89)
- June (72)
- May (72)
- April (63)
- March (61)
- February (74)
- January (54)

**2012**

- December (45)
- November (83)
- October (119)
- September (82)
- August (95)
- July (77)
- June (85)
- May (104)
- April (41)
- March (74)
- February (73)
- January (73)

**2011**

- December (83)
- November (64)
- October (77)
- September (105)
- August (39)
- July (25)
- June (44)
- May (64)
- April (46)
- March (34)
- February (50)
- January (40)

**2010**

- December (76)
- November (54)
- October (42)
- September (73)
- August (39)
- July (35)
- June (34)
- May (27)
- April (24)
- March (20)
- February (26)
- January (36)

**2009**

- December (49)
- November (57)
- October (48)
- September (44)
- August (39)
- July (27)
- June (41)
- May (38)
- April (53)
- March (47)
- February (37)
- January (38)

**2008**

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC 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