# Mathematics (111)

**Inside DeepMind’s New Efforts to Use Deep Learning to Advance Mathematics**- Dec 10, 2021.

Using deep learning techniques can help mathematicians develop intuitions about the toughest problems in the field.**OpenAI’s Approach to Solve Math Word Problems**- Nov 9, 2021.

OpenAI's latest research aims to solve math word problems. Let's dive a bit deeper into the ideas behind this new research.**How to do “Limitless” Math in Python**- Oct 7, 2021.

How to perform arbitrary-precision computation and much more math (and fast too) than what is possible with the built-in math library in Python.**Path to Full Stack Data Science**- Sep 27, 2021.

Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.**Math 2.0: The Fundamental Importance of Machine Learning**- Sep 8, 2021.

Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.**How Machine Learning Leverages Linear Algebra to Solve Data Problems**- Sep 7, 2021.

Why you should learn the fundamentals of linear algebra.**Antifragility and Machine Learning**- Sep 6, 2021.

Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?**Learning Data Science and Machine Learning: First Steps After The Roadmap**- Aug 24, 2021.

Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML.**Linear Algebra for Natural Language Processing**- Aug 17, 2021.

Learn about representing word semantics in vector space.**KDnuggets™ News 21:n30, Aug 11: Most Common Data Science Interview Questions and Answers; How Visualization is Transforming Exploratory Data Analysis**- Aug 11, 2021.

Most Common Data Science Interview Questions and Answers; How Visualization is Transforming Exploratory Data Analysis; How To Become A Freelance Data Scientist – 4 Practical Tips; How to Query Your Pandas Dataframe; Essential Math for Data Science: Introduction to Systems of Linear Equations**Essential Math for Data Science: Introduction to Systems of Linear Equations**- Aug 6, 2021.

In this post, you’ll see how you can use systems of equations and linear algebra to solve a linear regression problem.**Numerics V: Integrality – When Being Close Enough is not Always Good Enough**- Jun 10, 2021.

Wow, already the fifth blog in this series…What is left to tell about numerics? There is another place where a MIP solver can sneak in minor violations that we have not yet discussed: The integrality conditions.**Essential Math for Data Science: Basis and Change of Basis**- May 28, 2021.

In this article, you will learn what the basis of a vector space is, see that any vectors of the space are linear combinations of the basis vectors, and see how to change the basis using change of basis matrices.**Differentiable Programming from Scratch**- May 19, 2021.

In this article, we are going to explain what Differentiable Programming is by developing from scratch all the tools needed for this exciting new kind of programming.**Essential Linear Algebra for Data Science and Machine Learning**- May 10, 2021.

Linear algebra is foundational in data science and machine learning. Beginners starting out along their learning journey in data science--as well as established practitioners--must develop a strong familiarity with the essential concepts in linear algebra.**Essential Math for Data Science: Linear Transformation with Matrices**- Apr 16, 2021.

You’ll start seeing matrices, not only as operations on numbers, but also as a way to transform vector spaces. This conception will give you the foundations needed to understand more complex linear algebra concepts like matrix decomposition.**KDnuggets™ News 21:n11, Mar 17: Is Data Scientist still a satisfying job? How To Overcome The Fear of Math and Learn Math For Data Science**- Mar 17, 2021.

Must Know for Data Scientists and Data Analysts: Causal Design Patterns; Know your data much faster with the new Sweetviz Python library; The Inferential Statistics Data Scientists Should Know; Natural Language Processing Pipelines, Explained**3 Mathematical Laws Data Scientists Need To Know**- Mar 2, 2021.

Machine learning and data science are founded on important mathematics in statistics and probability. A few interesting mathematical laws you should understand will especially help you perform better as a Data Scientist, including Benford's Law, the Law of Large Numbers, and Zipf's Law.**Essential Math for Data Science: Scalars and Vectors**- Feb 12, 2021.

Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations.**Essential Math for Data Science: Introduction to Matrices and the Matrix Product**- Feb 5, 2021.

As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.**KDnuggets™ News 21:n03, Jan 20: K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines; Essential Math for Data Science: Information Theory**- Jan 20, 2021.

Here is a clever method of getting K-Means 8x faster, 27x lower error than Scikit-learn; Understand information theory you need for Data Science; Learn how to do cleaner data analysis with pandas using pipes; What are the four jobs of the data scientist? and more**Essential Math for Data Science: Information Theory**- Jan 15, 2021.

In the context of machine learning, some of the concepts of information theory are used to characterize or compare probability distributions. Read up on the underlying math to gain a solid understanding of relevant aspects of information theory.**Advice to aspiring Data Scientists – your most common questions answered**- Jan 7, 2021.

Embarking on a new career path can be daunting with many unknowns about how to get started and how to be successful. If you are aspiring to become a Data Scientist, then the answers to these common questions can help set you off on the right foot.**Essential Math for Data Science: The Poisson Distribution**- Dec 29, 2020.

The Poisson distribution, named after the French mathematician Denis Simon Poisson, is a discrete distribution function describing the probability that an event will occur a certain number of times in a fixed time (or space) interval.**Matrix Decomposition Decoded**- Dec 11, 2020.

This article covers matrix decomposition, as well as the underlying concepts of eigenvalues (lambdas) and eigenvectors, as well as discusses the purpose behind using matrix and vectors in linear algebra.**Essential Math for Data Science: Probability Density and Probability Mass Functions**- Dec 7, 2020.

In this article, we’ll cover probability mass and probability density function in this sample. You’ll see how to understand and represent these distribution functions and their link with histograms.**Essential Math for Data Science: Integrals And Area Under The Curve**- Nov 25, 2020.

In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.**What an Argentine Writer and a Hungarian Mathematician Can Teach Us About Machine Learning Overfitting**- Sep 21, 2020.

This article presents some beautiful ideas about intelligence and how they related to modern machine learning.**Math for Programmers**- Sep 10, 2020.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer. Save 50% with code kdmath50.**Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills**- Sep 8, 2020.

We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.**These Data Science Skills will be your Superpower**- Aug 20, 2020.

Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist.**Math for Programmers!**- Jul 30, 2020.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer. Save 50% with code kdmath50.**Math and Architectures of Deep Learning!**- Jul 15, 2020.

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. Save 50% with code kdarch50.**KDnuggets™ News 20:n27, Jul 15: Great explanation of Calculus, the Key to Deep Learning; 8 data-driven reasons to learn Python**- Jul 15, 2020.

We bring you free MIT courses on Calculus, which is the key to understanding Deep Learning - check this amazing explanation of an integral and dx; 8 data-driven reasons to learn Python; How to get and analyze Financial data with Python; Free ebook: The Foundations of Data Science and more.**Math for Programmers**- Jul 8, 2020.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**KDnuggets™ News 20:n25, Jun 24: PyTorch Fundamentals You Should Know; Free Math Courses to Boost Your Data Science Skills**- Jun 24, 2020.

A Classification Project in Machine Learning: a gentle step-by-step guide; Crop Disease Detection Using Machine Learning and Computer Vision; Bias in AI: A Primer; Machine Learning in Dask; How to Deal with Missing Values in Your Dataset**4 Free Math Courses to do and Level up your Data Science Skills**- Jun 22, 2020.

Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.**Math and Architectures of Deep Learning**- Jun 11, 2020.

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. Save 40% off Math and Architectures of Deep Learning with code nlkdarch40**Math for Programmers!**- May 13, 2020.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**A Concise Course in Statistical Inference: The Free eBook**- Apr 27, 2020.

Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.**Learning during a crisis (Data Science 90-day learning challenge)**- Apr 24, 2020.

How can you keep your focus and drive during a global crisis? Take on a 90-day learning challenge for data science and check out this list of books and courses to follow.**Math and Architectures of Deep Learning**- Apr 22, 2020.

This hands-on book bridges the gap between theory and practice, showing you the math of deep learning algorithms side by side with an implementation in PyTorch. You can save 40% off Math and Architectures of Deep Learning until May 13! Just enter the code nlkdarch40 at checkout when you buy from manning.com.**Top KDnuggets tweets, Apr 08-14: Mathematics for #MachineLearning: The Free eBook – KDnuggets**- Apr 15, 2020.

Also Exploratory Data Analysis for Natural Language Processing: A Complete Guide to Python Tools; A professor with 20 year experience to all high school seniors (and their parents). If you were planning to enroll in college next fall - don't.**KDnuggets™ News 20:n14, Apr 8: Free Mathematics for Machine Learning eBook; Epidemiology Courses for Data Scientists**- Apr 8, 2020.

Stop Hurting Your Pandas!; Python for data analysis... is it really that simple?!?; Introducing MIDAS: A New Baseline for Anomaly Detection in Graphs; Build an app to generate photorealistic faces using TensorFlow and Streamlit; 5 Ways Data Scientists Can Help Respond to COVID-19 and 5 Actions to Avoid**Math for Programmers!**- Mar 11, 2020.

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.**Data Science Curriculum for self-study**- Feb 26, 2020.

Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.**KDnuggets™ News 20:n08, Feb 26: Gartner 2020 Magic Quadrant for Data Science & Machine Learning Platforms; Will AutoML Replace Data Scientists?**- Feb 26, 2020.

This week in KDnuggets: The Death of Data Scientists - will AutoML replace them?; Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms; Hand labeling is the past. The future is #NoLabel AI; The Forgotten Algorithm; Getting Started with R Programming; and much, much more.**Free Mathematics Courses for Data Science & Machine Learning**- Feb 25, 2020.

It's no secret that mathematics is the foundation of data science. Here are a selection of courses to help increase your maths skills to excel in data science, machine learning, and beyond.**KDnuggets™ News 20:n07, Feb 19: 20 AI, Data Science, Machine Learning Terms for 2020; Why Did I Reject a Data Scientist Job?**- Feb 19, 2020.

This week on KDnuggets: 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020; Why Did I Reject a Data Scientist Job?; Fourier Transformation for a Data Scientist; Math for Programmers; Deep Neural Networks; Practical Hyperparameter Optimization; and much more!**Fourier Transformation for a Data Scientist**- Feb 14, 2020.

The article contains a brief intro into Fourier transformation mathematically and its applications in AI.**Math for Programmers – your guide for solving math problems in code**- Feb 12, 2020.

**Intro to Machine Learning and AI based on high school knowledge**- Feb 5, 2020.

Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.**KDnuggets™ News 20:n03, Jan 22: I wanna be a data scientist, but… how? Top 10 Technology Trends for 2020**- Jan 22, 2020.

If you want to be a Data Scientist, but not sure how to start - there is a perfect blog for you; Baidu top 10 technology trends for 2020; Math for programmers!; The future of Machine Learning; and more.**Math for Programmers!**- Jan 15, 2020.

**Top KDnuggets tweets, Dec 18-30: A Gentle Introduction to Math Behind Neural Networks**- Dec 31, 2019.

A Gentle Introduction to #Math Behind #NeuralNetworks; Learn How to Quickly Create UIs in Python; I wanna be a data scientist, but... how!?; I created my own deepfake in two weeks**Math for Programmers!**- Dec 11, 2019.

**Top KDnuggets tweets, Nov 13-19: A whole lot of Data Science Cheatsheets**- Nov 21, 2019.

Also: Bring the scientific rigor of reproducibility to your Data Science projects; Neutrinos Lead to Unexpected Discovery in Basic Math ; The media gets really excited about AI. Maybe a bit too excited**The Math Behind Bayes**- Nov 19, 2019.

This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood.**Math for Programmers..**- Oct 9, 2019.

**Math in Our Lives video collection from SIAM**- Oct 7, 2019.

Having trouble explaining why applied math matters to your non-specialist friends and colleagues? As valued members of the applied math community and ambassadors of SIAM, review these short animations and share them with your interested networks! Help us show that math matters and why.**Math for Programmers**- Aug 19, 2019.

**Lagrange multipliers with visualizations and code**- Aug 6, 2019.

In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.**Math for Machine Learning**- Jul 9, 2019.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**Math for Programmers.**- Jun 7, 2019.

**Math for Machine Learning.**- Jun 5, 2019.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**KDnuggets™ News 19:n20, May 22: 7 Steps to Mastering SQL for Data Science; How to build Math Programming Skills**- May 22, 2019.

Also An overview of Pycharm for Data Scientists; How to build a Computer Vision model - key approaches and datasets; k-means clustering tutorial; 60+ useful graph visualization libraries; The Data Fabric for Machine Learning.**Probability Mass and Density Functions**- May 21, 2019.

This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as my understanding of these concepts.**Mathematical programming — Key Habit to Build Up for Advancing Data Science**- May 15, 2019.

We show how, by simulating the random throw of a dart, you can compute the value of pi approximately. This is a small step towards building the habit of mathematical programming, which should be a key skill in the repertoire of a budding data scientist.**Math for Programmers**- Apr 11, 2019.

**Monetizing the Math – are you ready?**- Jan 28, 2019.

We outline an extensive list of things to do or plan for to help fully realize the ROI of your AI and Machine Learning projects in 2019.**Math for Programmers**- Jan 15, 2019.

**Math for Machine Learning**- Jan 4, 2019.

This ebook explains the math involved and introduces you directly to the foundational topics in machine learning.**Southern Illinois University Edwardsville: Director of the Center for Predictive Analytics/(Associate) Professor of Mathematics and Statistics [Edwardsville, IL]**- Jan 4, 2019.

Southern Illinois University Edwardsville (SIUE) is establishing the Center for Predictive Analytics (C-PAN), and is seeking an innovative, visionary director for the center who will provide centralized leadership in establishing research and educational initiatives across academic units at SIUE.**Math for Machine Learning**- Dec 10, 2018.

**A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more**- Dec 7, 2018.

A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.**New Book: Linear Algebra – what you need for Machine Learning and Data Science now**- Oct 24, 2018.

From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.**Top KDnuggets tweets, Oct 10-16: 7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning; 6 Books Every Data Scientist Should Keep Nearby**- Oct 17, 2018.

Also Machine Learning Cheat Sheets; Top 8 Free Must-Read Books on Deep Learning.**University of San Francisco: Assistant Professor, Tenure Track, Mathematics and Statistics [San Francisco, CA]**- Oct 17, 2018.

The University of San Francisco invites applications for a tenure-track Assistant Professor position to begin August 2019. We seek well-qualified candidates in the areas of applied mathematics or statistics, with a focus on the extraction of knowledge from data.**Preprocessing for Deep Learning: From covariance matrix to image whitening**- Oct 10, 2018.

The goal of this post/notebook is to go from the basics of data preprocessing to modern techniques used in deep learning. My point is that we can use code (Python/Numpy etc.) to better understand abstract mathematical notions!**KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R**- Oct 3, 2018.

Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning**Math for Machine Learning**- Sep 28, 2018.

**Learning mathematics of Machine Learning: bridging the gap**- Sep 28, 2018.

We outline the four key areas of Maths in Machine Learning and begin to answer the question: how can we start with high school maths and use that knowledge to bridge the gap with maths for AI and Machine Learning?**KDnuggets™ News 18:n34, Sep 12: Essential Math for Data Science; 100 Days of Machine Learning Code; Drop Dropout**- Sep 12, 2018.

Also: Neural Networks and Deep Learning: A Textbook; Don't Use Dropout in Convolutional Networks; Ultimate Guide to Getting Started with TensorFlow.**Machine Learning Cheat Sheets**- Sep 11, 2018.

Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.**Essential Math for Data Science: ‘Why’ and ‘How’**- Sep 6, 2018.

It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.**Unveiling Mathematics Behind XGBoost**- Aug 14, 2018.

Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model.**Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence**- Jul 18, 2018.

**Learn AI and Data Science rapidly based only on high school math – KDnuggets Offer**- May 25, 2018.

This 3-month program, created by Ajit Jaokar, who teaches at Oxford, is interactive and delivered by video. Coding examples are in Python. Places limited - check special KDnuggets rate.**Top KDnuggets tweets, May 02-08: Boost your data science skills. Learn linear algebra.**- May 9, 2018.

Also: #ApacheSpark: #Python vs. #Scala pros and cons for #DataScience; Loc2Vec: Learning location embeddings with triplet-loss networks; Skewness vs Kurtosis - The Robust Duo.**24houranswers: Analytics / Data Science / Math / Statistics Tutors**- May 9, 2018.

Seeking qualified Ph.D. students or faculty members for the position of Tutor/Instructor to provide one-on-one lectures to the needs of our students in Applied Analytics, Computer Science, Applied Math and Statistics, and more.**KDnuggets™ News 18:n19, May 9: KDnuggets Poll: What tools you used for Analytics/Data Science Projects? 8 Useful Advices for Aspiring Data Scientists**- May 9, 2018.

Also: Boost your data science skills. Learn linear algebra; Apache Spark: Python vs. Scala; Getting Started with spaCy for Natural Language Processing.**Deep Conversations: Lisha Li, Principal at Amplify Partners**- May 3, 2018.

Mathematician Lisha Li expounds on how she thrives as a Venture Capitalist at Amplify Partners to identify, invest and nurture the right startups in Machine Learning and Distributed Systems.**Boost your data science skills. Learn linear algebra.**- May 3, 2018.

The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.**Top KDnuggets tweets, Apr 11-17: Boost your #datascience skills. Learn linear algebra.**- Apr 18, 2018.

Also: Don’t learn #MachineLearning in 24 hours; Top 8 Free Must-Read Books on #DeepLearning; How Attractive Are You in the Eyes of Deep #NeuralNetwork?; Ten #MachineLearning Algorithms You Should Know to Become a #DataScientist**7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning**- Apr 17, 2018.

It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.**R Fundamentals: Building a Simple Grade Calculator**- Mar 19, 2018.

In this tutorial, we'll teach you the basics of R by building a simple grade calculator. While we do not assume any R-specific knowledge, you should be familiar with general programming concepts.**Data Structures Related to Machine Learning Algorithms**- Jan 30, 2018.

If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.**How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?**- Dec 20, 2017.

When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.**Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks.**- Aug 31, 2016.

Cartoon: #DataScientist - sexiest job of the 21st century until ...; What is the Role of the Activation Function in Neural Networks?; LinkedIn Machine Learning team tutorial on building #Recommender system; Create a #Chatbot for #Telegram in #Python to Summarize Text.**Open Source Machine Learning Degree**- Jun 6, 2016.

A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.**Practical skills that practical data scientists need**- May 13, 2016.

The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.**Data scientists mostly just do arithmetic and that’s a good thing**- May 10, 2016.

Are you also wondering how you can get started as data scientist, and become a valuable team player. Understand what really matters as data scientist, and things to focus in the initial stages.**There is more to a successful data scientist than mere knowledge**- May 9, 2016.

Look at Data scientist "definitions" with a wry smile: the "essential" skills very much reflect those that a short time ago were quite novel, and are being used in applications to problems that have recently become solvable.**The Evolution of the Data Scientist**- Mar 16, 2016.

We trace the evolution of Data Science from ancient mathematics to statistics and early neural networks, to present successes like AlphaGo and self-driving car, and look into the future.**How to Tackle a Lottery with Mathematics**- Jan 27, 2016.

With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.**15 Mathematics MOOCs for Data Science**- Sep 23, 2015.

The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.**Deep Learning and the Triumph of Empiricism**- Jul 7, 2015.

Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?**Top KDnuggets tweets, Oct 31 – Nov 2: Nate Silver on 3 Keys to Great Information Design**- Nov 3, 2014.

Best Infographics of the Year: Nate Silver on 3 Keys to Great Information Design; The invention of the Equals Sign jump-started math; A Great Collection of Machine Learning Algorithms; LinkedIn breaking up its data science team.**Poincare Conjecture, Perelman way, and Topology of social networks**- May 3, 2014.

We examine the connections between the $1 million proof of Poincare conjecture by a reclusive math genius and the topological behavior and information diffusion over social networks.**Top KDnuggets tweets, Feb 26-27: Facebook “Relationship Data Mining”; Non-human math: Wikipedia-size computer proof**- Feb 28, 2014.

The gap between data mining and predictive models in Facebook Relationship Data Mining; Non-human math: Computer proof is Wikipedia size; A look at pioneering women in #BigData ; MS in Data Analytics from CUNY: Online and Affordable.**Analytics, Mathematics, and Art of Rene Romero Schuler and Van Gogh**- Jan 13, 2014.

Is there a connection between Analytics, Mathematics and arts? How do we add arts flavor to analytics business is the question. And answer lies in century old painting by Van Gogh called "The Starry Night" which represents perfect turbulence.