Raise your hand if your company is making more than 15!
1. Day-dreaming that analytics is a plug & play magic wand that will bring very short term ROI. Well…
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
Featured Resources and Technical ContributionsContinue
Added by Vincent Granville on October 28, 2018 at 9:00am — No Comments
Something that has always troubled me with statistics is the pretense of certainty. The conclusions – being closely associated with calculations – tend to be reached rapidly. I might only be starting to give a problem some thought – although a statistician has already drawn conclusions. Over time, this can make a person feel insecure about his intellectual capacity – and perhaps cause him to write a blog on the subject. Consider the simulated data below: a special program was…Continue
Added by Don Philip Faithful on October 28, 2018 at 8:05am — No Comments
Added by Pedro URIA RECIO on October 27, 2018 at 8:01pm — No Comments
You will find here a few tables of random digits, used for simulation purposes and/or testing or integration in statistical, mathematical, and machine learning algorithms. These tables are particularly useful if you want to share your algorithms or simulations, and make them replicable. We also provide techniques to use in applications where secrecy is critical, such as cryptography, bitcoin or lotteries: in this case, you don't want to share your table of random numbers; to the contrary you…Continue
Added by Vincent Granville on October 27, 2018 at 9:00am — No Comments
This article was written by Tirthajyoti Sarkar. Below is a summary. The full article (accessible from link at the bottom) also features courses that you could attend to learn the topics listed below, as well as numerous comments. We also added a few topics that we think are important and missing in the original article.…Continue
Added by Andrea Manero-Bastin on October 26, 2018 at 5:00pm — No Comments
With the nascent stage of the data revolution past us, organisations are entering a new level of proficiency in handling data expertly. Gone are the days when organisations…Continue
Added by Ronald van Loon on October 26, 2018 at 3:35am — No Comments
Added by Benjamin Waxer on October 26, 2018 at 2:00am — No Comments
Below is my contrarian answer to one question recently posted on Quora.
It depends on what you mean by “no experience”. An NASA scientist who has processed petabytes of data and found great insights, for example discovered exoplanets, is de facto a data scientist and may have no interest in having his job title changed.
Then there is a bunch of people who call themselves “data science enthusiasts” and know nothing other than what they learned in a two-hour…Continue
Here is our selection of featured articles and technical resources posted since Monday:
Added by Vincent Granville on October 25, 2018 at 8:30am — No Comments
I am an advertising and marketing veteran who is currently transitioning towards data science. The purpose of this write-up is to give you some baseline understanding of marketing, grounded in my professional experience. I am hoping that my write-up will help you gain a bigger share of voice when working with advertising & marketing teams. Eventually, you might ask bigger questions and thus move beyond just optimizing their work.
I will expand this post into a…Continue
Facial recognition technology was always a mythical concept that we thought could be a tool that could solve many of our problems but would never see the light of day. Today, facial recognition is everywhere and is a part of the everyday technology that we use. The…
Added by Abhimanyu on October 25, 2018 at 12:50am — No Comments
Outliers are patterns in data that do not confirm to the expected behavior. While detecting such patterns are of prime importance in Credit Card Fraud, Stock Trading etc. Detecting anomaly or outlier observations are also of importance when training any of the supervised machine learning models. This brings us to two very important questions: concept of a local outlier, and why a local outlier?
In a multivariate dataset where the rows are generated independently from a probability…Continue
Added by Deepankar Arora on October 25, 2018 at 12:30am — No Comments
Let’s look at several techniques in machine learning and the math topics that are used in the process.
In linear regression, we try to find the best fit line or hyperplane for a given set of data points. We model the output of our linear function by a linear combination of the input variables using a set of parameters as weights.
The parameters are found by minimizing the residual sum of squares. We find a critical point by setting the vector of derivatives of…Continue
Added by Richard Han on October 24, 2018 at 6:00pm — No Comments
scikit-learn is a wonderful tool for machine learning in Python, with great flexibility for implementing pipelines and running experiments (see,…Continue
Added by Civis Analytics on October 24, 2018 at 10:44am — No Comments
An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst’s creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects.
Since the data mining process breaks up the overall task…Continue
Added by Mehmet Gökce on October 24, 2018 at 8:58am — No Comments
Learn how to transform data into business insight with these Data Tutorials and eBooks.
Deep Reinforcement Learning Hands-On By Maxim Lapan
This practical guide will teach…Continue
Added by Packt Publishing on October 24, 2018 at 3:19am — No Comments
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…Continue
Added by Ridhima Kumar on October 23, 2018 at 11:00am — No Comments
The work is done by Jatinder Singh (also co-authored this article) and Iresh Mishra. Also thanks to Saurabh…Continue
Added by Rudradeb Mitra on October 23, 2018 at 11:00am — No Comments
Summary: Even if you’re not big enough to have a full blown data science group that shouldn’t hold you back from benefiting from AI. The market has evolved so that there are now industry and process specific vertical applications available from 3rd party AI vendors that you can implement. There are just a few things to look out for.
Added by William Vorhies on October 23, 2018 at 7:30am — No Comments