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February 2017 Blog Posts (87)

How to Kick-start Your Career In Big Data & Data Science

Big data has become the new buzz word in this decade. If you’re looking for a big leap to transform your career, then you should explore this extremely abundant field of big data. Data science is the new frontier of the 21st century right now. The data analyst jobs are extremely attractive, lucrative & fulfilling. A lot of major organizations have realized the dynamic future of analytics & have started to create teams in…

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Added by Yoey Thamas on February 28, 2017 at 7:30pm — 4 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 February 28, 2017 at 6:56pm — No Comments

Deep Learning lets Regulated Industries Refocus on Accuracy

Summary:  Count yourself lucky if you’re not in one of the regulated industries where regulation requires you to value interpretability over accuracy.  This has been a serious financial weight on the economy but innovations in Deep Learning point a way out.

As Data…

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Added by William Vorhies on February 28, 2017 at 9:21am — No Comments

Ontologies: Practical Applications

In the previous post, "Why Ontologies", we explored concepts at a very high level about what an ontology is, and how they can be used in AI, NLP, data integration, and knowledge management applications. So what…

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Added by Randall Shane on February 28, 2017 at 8:00am — No Comments

Deducer Tutorial: Creating Linear Model using R Deducer Package

Linear Model better known as linear regression is one of the most common and flexible analysis framework to identify relationship between two or more variables. The widely used linear model is represented by drawing the best fit line through a series of data points represented on a scatter plot. 

For any budding business analyst this should be the starting point to understand how model works at the very core of its design.

Selecting the Variables in Deducer…

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Added by Sunil Kappal on February 28, 2017 at 7:00am — No Comments

Data Science, Machine Learning, BI Explained in a Amazing Few Pictures

Guest blog post by Rubens Zimbres, PhD.

This article brings images from my work modeling with Mathematica, my experience as a Business Analyst and also my doctorate lessons. For me, the borders between a properly executed Business Intelligence and Data Science (with substantive knowledge in Management) are fuzzy. See the picture below:

What is a Data Scientist ? In…

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Added by Vincent Granville on February 27, 2017 at 2:30pm — 5 Comments

Two New Data Science Books

Here is an update from Zacharias Voulgaris, Data Science Author, Video Producer, and Acquisitions Consultant. The message below is from Zacharias.



1. Produced Videos for O'Reilly Safari on Data Science, Programming Languages, and AI



I have a bunch of videos now live on topics including; becoming a data scientist, artificial Intelligence, and which…

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Added by Vincent Granville on February 27, 2017 at 2:29pm — No Comments

View the New DSC Classified Section

Here are just a few featured listings in our new section:

View all listings here. …

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Added by Tim Matteson on February 27, 2017 at 1:00pm — No Comments

How B2B Ecosystems & (Big) Data Can Transform Sales and Marketing Practices

Managing your relationships with customers, suppliers, and partners and constantly improving their experience is a proven way to build a sustainable and profitable business, and contrary to popular assumption, this doesn’t apply to B2C businesses only. With…

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Added by Ronald van Loon on February 27, 2017 at 8:00am — No Comments

Data Science Summarized in One Picture

After posting Machine Learning Summarized in One Picture, here is a picture for data science:

I tried to find the source for this picture, but could not. I've found it on LinkedIn,…

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Added by Vincent Granville on February 26, 2017 at 1:00pm — 4 Comments

Leaping to a New Optimum in the Office Workspace

I listened once to an old professor talking about working out factorial ANOVA and multiple regression on paper, back in the day. He described a whole room full of papers, all in a particular order, every sum contributing to the next sum and if you got one number wrong the whole thing would be wrong and you'd have to start over. Adding machines made it go quicker, and when punch-card mainframes came they were, well, frustrating at times -- lots of graphic and emotional stories there! -- but…

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Added by Jim Kennedy on February 26, 2017 at 6:30am — No Comments

Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis

In this post, we consider different approaches for time series modeling. The forecasting approaches using linear models, ARIMA alpgorithm, XGBoost machine learning algorithm are described. Results of different model combinations are shown. For probabilistic modeling the approaches using copulas and Bayesian inference are considered.

INTRODUCTION

Time series analysis, especially forecasting, is an important problem of modern…

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Added by Bohdan Pavlyshenko on February 26, 2017 at 5:30am — 2 Comments

Dealing with Unstructured Input

It isn’t too unusual for surveys to contain open-ended questions:  respondents would be free to enter their comments in any manner.  More on an operational basis, not surveys but rather client systems might hold such comments; and not the respondents themselves but customer service agents would be responsible for entering the information.  These same agents would likely classify the nature of the exchange or comments maybe using drop-down menu choices, radials, and check-boxes.  One approach…

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Added by Don Philip Faithful on February 25, 2017 at 10:47am — No Comments

Weekly Digest, February 27

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 Contributions

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Added by Vincent Granville on February 25, 2017 at 10:30am — No Comments

How Big Data is Becoming Smaller Than Small Data

If you've been struggling to get useful results from big data, you're not alone. The buzz has been that any company not doing analytics is behind the times. But big data even has been a challenge in itself. Perhaps small data is the…

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Added by Jennifer Livingston on February 23, 2017 at 9:00pm — No Comments

21 Great Articles and Tutorials on Time Series

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, …

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Added by Vincent Granville on February 23, 2017 at 4:15pm — No Comments

Food and health demographics in the USA

Contributed by Yannick Kimmel. He  is currently in the NYC Data Science Academy …

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Added by NYC Data Science Academy on February 23, 2017 at 9:00am — No Comments

How IoT is Changing the World: Cases from Visa, Airbus, Bosch & SNCF

The Internet of Things (IoT) is changing our world. This may seem like a bold statement, but consider the impact this revolutionary technology has already had on communications, education, manufacturing, science, business, and many other fields of life. Clearly, the IoT is moving really fast from concept to reality and transforming how industries…

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Added by Ronald van Loon on February 23, 2017 at 7:30am — No Comments

Thursday News: R, Python, Julia, AI, IoT, DataViZ, Hadoop, ML

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

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Added by Vincent Granville on February 22, 2017 at 7:30pm — No Comments

Why Ontologies?

In short, an ontology is the specification of a conceptualization. What does that mean from the perspective of the information sciences? Wikipedia's definition: "formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse." This basically means, like all models, that it is a representation of what actually exists in reality. A statistical or mathematical…

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Added by Randall Shane on February 22, 2017 at 5:30pm — No Comments

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