[unable to retrieve full-text content]Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data
Original Post: KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail
[unable to retrieve full-text content]Read this insightful interview with Bokeh’s core developer, Bryan Van de Ven, and gain an understanding of what Bokeh is, when and why you should use it, and what makes Bryan a great fit for helming this project.
Original Post: Beautiful Python Visualizations: An Interview with Bryan Van de Ven, Bokeh Core Developer
[unable to retrieve full-text content]Visualize your data, Demonstrate its value, and tailor your pitch – learn how from the industry leaders in Boston.
Original Post: Big Data Innovation, Data Visualization Summits, Boston, Sep 7-8
[unable to retrieve full-text content]DataCamp is celebrating 1 millions learners on its platform and is offering 50% off for unlimited access until May 23. Learn R and Python for data science interactively at your own pace.
Original Post: Join 1 Million Others on DataCamp (50% off until May 23)
[unable to retrieve full-text content]Also Is #MachineLearning overtaking #BigData? What Do Frameworks Offer Data Scientists that #Programming Languages Lack?; Seeing Theory – A Brown University visual intro to probability and stats.
Original Post: Top KDnuggets tweets, May 03-09: New Poll: What software you used for Analytics, Data Science? Approaching (Almost) Any #MachineLearning Problem
[unable to retrieve full-text content]An analysis of NYC Open Data health inspections showing that craft beer bar kitchens in Manhattan are cleaner than the average establishment by a statistically significant margin. An encouraging finding for Dry January.
Original Post: Clean Data Science: Evaluating The Cleanliness of NYC Craft Beer Bar Kitchens
By DataScience.com Sponsored Post. Prerequisites Experience with the specific topic: Novice Professional experience: No industry experience The reader should be familiar with basic data analysis concepts and have some experience with a programming language (Python is ideal but not required). The dataset used can be downloaded here. You will only need day.csv after unzipping the dataset. Introduction to Data Visualization Data visualization is a key part of any data science workflow, but it is frequently treated as an afterthought or an inconvenient extra step in reporting the results of an analysis. Taking such a stance is a mistake — as the cliché goes, a picture is worth a thousand words. Data visualization should really be part of your workflow from the very beginning, as there is a lot of value and insight to be gained from just looking at your data. Summary…
Original Post: Creating Data Visualization in Matplotlib
A look at beer features to determine whether a specific brew might be better served (pun intended) by being classified under a different style. kNN analysis supported with in-post plots and linked iPython notebook. By Reginal Eps, EndlessPint. Not One Thing Beer is delicious but it is not one thing. If you disagree with the former part of the previous…
Original Post: Neighbors Know Best: (Re) Classifying an Underappreciated Beer
SnappyData is launching a FREE cloud service called iSight-Cloud so anyone can try our engine and provide us some feedback. You can try our simple demos in a visual environment or even bring your own data sets to try. By Jags Ramnarayan, CTO SnappyData. Introducing iSight Cloud – Lightning fast visualizations on large data sets at a fraction of the…
Original Post: iSight Cloud – Lightning fast visualizations on large data sets
Join hackathon to explore data science models and D3 visualizations to help the efforts of the Citizens Police Data Project.Join Metis for a day-long hackathon, where we explore data science models and D3 visualizations to help the efforts of the Citizens Police Data Project. This is donation-based event, where all proceeds will go to Invisible Institute/CPDP.All attendees will receive a…
Original Post: Data Science Hackathon, Metis / Invisible Institute, Nov 19, San Francisco