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April 2017 Blog Posts (93)

Big Data Analysis

Big Data is not just the ability to store large amounts of data, more important is what we can do to the data in that large volume, how we use the data with such large volumes. One of its uses is for data analysis needs. Big Data Analysis or Big Data Analysis can be done in…

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Added by Jeefri A. Moka on April 30, 2017 at 9:30pm — No Comments

Muse: a Better Music Recommendation Application

Contributed by http://blog.nycdatascience.com/author/sjstebbins/","Spencer";)">Spencer James Stebbins. He enrolled in the NYC Data Science Academy 12-week full time Data Science Bootcamp program taking place between July 5th to September 23rd, 2016. This post is based on their third…

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Added by NYC Data Science Academy on April 30, 2017 at 7:30am — No Comments

5 ways to improve the model accuracy of Machine Learning

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels. With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience.…

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Added by Sandeep Raut on April 29, 2017 at 7:00pm — No Comments

Time Series Analysis With Generalized Additive Models

This article comes from Algobeans Layman tutorials in analytics. 

Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time—we are always interested to foretell the future.

Temporal Dependent Models

One intuitive way to make…

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Added by Emmanuelle Rieuf on April 29, 2017 at 3:00pm — No Comments

Weekly Digest, May 1

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.

Announcements

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Added by Vincent Granville on April 29, 2017 at 8:30am — No Comments

Blockchain Basics

Let’s understand ledgers which form the basis of blockchain implementation. A ledger is a document which records summarized financial information as debits and credits, and shows the current balances.

A traditional ledger entry looks like below. In the current world, companies usually store this ledger at a centralized location – following a Client / Server Architecture…

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Added by Mohita on April 28, 2017 at 7:30am — 2 Comments

Global Cyber Security Market Worth Over $150 Billion by 2021

Global Cyber Security Market – Overview

Cyber Security market is expected to grow with the CAGR of ~8% - 11% during 2016 to 2021, owed to which it is predictable to cross USD $150 billion by 2021, assures the Market Research Future in one of its recently published study report - “Global Cyber Security Market Research Report Forecast 2017- 2021”

With growing industries and preference for centralization amongst the organizations; the computer based systems have become an…

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Added by Sagar kadam on April 27, 2017 at 6:30pm — No Comments

Deep Learning Cheat Sheet (using Python Libraries)

This cheat sheet was produced by DataCamp, and it is based on the Keras library..Keras is an easy-to-use and powerful library for Theano and TensorFlow that provides a high-level neural networks API to develop and evaluate deep learning models. Originally posted here in PDF format. Click on the image below to zoom in. …

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Added by Data Geek on April 27, 2017 at 4:30pm — No Comments

18 Great Blogs Posted in the last 12 Months

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. 

18 Great Blogs Posted in the last 12 Months…

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Added by Vincent Granville on April 27, 2017 at 8:07am — No Comments

Thursday News: AI, Python, Hadoop, ML, Data Science, Automation

Here is our selection of featured articles and resources posted since Monday:

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Added by Vincent Granville on April 27, 2017 at 5:36am — No Comments

Re-thinking Enterprise business processes using Augmented Intelligence

In the 1990s, there was a popular book called Re-engineering the Corporation. Looking back now, Re-engineering certainly has had a mixed success – but it did have an impact over the last two…

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Added by ajit jaokar on April 26, 2017 at 9:39pm — No Comments

Which machine learning algorithm should I use?

By Hui Li, Principal Staff Scientist, Data Science, at SAS.

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including:

  • The size, quality, and nature of data.
  • The available computational time.
  • The urgency of the task.
  • What you want to do with the data.

Even an experienced data…

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Added by Vincent Granville on April 26, 2017 at 6:30pm — 3 Comments

Book: Data Science for the Layman: No Math Added

Want to get started on data science? Our promise: no math added.

This book has been written in layman’s terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.…

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Added by Vincent Granville on April 26, 2017 at 3:00pm — 1 Comment

Key Machine Learning PreReq: Viewing Linear Algebra through the right lenses

Guest blog by Ashwin Rao. Ashwin is Vice President, Data Science & Optimization at Target.

The tech industry has gone berserk - everyone wants to develop “skills in Machine Learning and AI” but few are willing to put in…

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Added by Vincent Granville on April 26, 2017 at 2:58pm — No Comments

Enterprise AI Landscape - Infographic

Added by Tim Matteson on April 26, 2017 at 1:04pm — 3 Comments

18 Data Science Certificates Rated

This infographic was produced by Springboard, and it lists a few short online, inexpensive courses along with some university programs, leading to a certificate. The infographics provides some highlights for each program, for comparison purposes. For more data science programs and certificates, click here or…

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Added by Data Geek on April 26, 2017 at 10:00am — No Comments

The [Marketing Data Scientist’s] Search for a Home: Part I

A thought provoking series that gives my account of becoming a marketing data scientist hybrid during one of the most chaotic times in the marketing industry, the Big Data boom. In the series, I discuss my enlightenment, highlight challenges to the modern marketer and those who have developed several skills classically associated with Data Scientists; conclude with recommendations for my peers and the marketing industry. The overlap of responsibilities and required skills among Data Analyst,…

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Added by Allyn Fowler on April 26, 2017 at 9:30am — No Comments

Introduction to Principal Component Analysis

This formula-free summary provides a short overview about how PCA (principal component analysis) works for dimension reduction, that is, to select k features (also called variables) among a larger set of n features, with k much smaller than n. This smaller set of k features built with PCA is the best subset of k features, in the sense that it minimizes the variance of the residual noise when fitting data to a…

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Added by Vincent Granville on April 26, 2017 at 8:30am — 3 Comments

Keeping Your Job in the Age of Automation

Summary:  What are the real threats of job loss from real and AI enhanced virtual robots?  How do we position ourselves and our children to succeed in this new environment?

 …

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Added by William Vorhies on April 25, 2017 at 8:09am — No Comments

Handling imbalanced dataset in supervised learning using family of SMOTE algorithm.

Consider a problem where you are working on a machine learning classification problem. You get an accuracy of 98% and you are very happy. But that happiness doesn’t last long when you look at the confusion matrix and realize that majority class is 98% of the total data and all examples are classified as majority class. Welcome to the real world of imbalanced data sets!!…

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Added by Rohit Walimbe on April 24, 2017 at 10:00pm — No Comments

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