A recent post has generated an intense discussion about finding "unicorn" data scientists with a combination of all the needed skills, or whether that skillset is best filled by a team. Here are the highlights, including a proposal how to train well-rounded data scientists.
Data science is permeating every facet of our daily lives - from our culture to our classrooms. Look for data science to make an even greater impact in 2014.
The emergence of Apache Spark is a key development for Big Analytics; 5 Free Excel add-Ins to help Marketers analyze #BigData; Key Skills of Top @kaggle Competitors: R (90%), Random Forests (60%); Netflix open sources Suro: data traffic "cop" which directs #BigData to destination
Key themes were: Customer Obsessed Marketer, Segment of One, SoLoMo (Social, Local and Mobile), and Big Data - actionable insights and decision making.
New book: Data Mining Applications with R; Data Scientist catches up with Statistician; What is Wrong with the Definition of Data Science; Making sense of #BigData : mining Twitter names
A veteran statistician argues that 3 different areas usually included in "Data Science" require dramatically different, skills, education, and training with very little overlap.
Data Mining Book Review: "Visualize This" from @flowingdata; Top NYU Professor Vasant Dhar on Data Science and Prediction - what do they mean; Analysis reveals #MOOC problems: student participation drops dramatically.
Covers 15 real-world applications on data mining with R, including R code and data, covering business background and problems, data extraction and exploration, data preprocessing, modeling, model evaluation, findings and model deployment.
What does "Data Science" and #BigData mean? Is there something unique about it? What skills do "data scientists" need to be productive in a world deluged by data? What are the implications for scientific inquiry?
Analytically sophisticated businesses combine predictive analytics and decision models with optimization to solve complex problems and achieve good results. Top FICO expert explains.
Poll Results: R has a big lead, but Python is gaining; Who are Data Scientists and why they are or are not unicorns; 2014 Predictions: Machine-generated data will grow; #BigData + Big Pharma = Big Privacy Catastrophe
Poll results show that R has a big lead, but Python is gaining among data scientists; We re-analyze top LinkedIn Groups for Analytics, Big Data and Data Science; Top 2013 Stoeries on KDnuggets and more.
Guest blog of SkyTree CEO Martin Hack looks at 2 Key Trends in Predictive Analytics in 2014: high performance machine learning will penetrate the mainstream, and privacy issues associated with Big Data will be debated by business owners and consumers alike.
A billion rows per second in Python; #BigData Dashboard Dizziness - what you get after careful consideration of 437 charts; Import.io turns any website into a database; 2014 Predictions: Machine-generated data
Highlights of the IEEE ICDM 2013 Conference on Data Mining: Good organization in icy conditions, How to do clustering in high dimensions, Discovering unexpected sequential patterns, and perspectives on #BigData.
Facebook hires Deep Learning expert Yann LeCun to head its new AI lab; New Data Mining and Machine Learning books from CRC Press - Save 25%; Import.io turns any website into a database; 2014 World Cup Group Stage, per ESPN: Brazil, Argentina, Germany, France advance
Written by leaders in the data mining community, this new book provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors.
Save 25% on new books Data Mining and Machine Learning books, including Multilinear Subspace Learning, Bayesian Programming, Computational Business Analytics, and Multi-Label Dimensionality Reduction.
More fuel thrown into Data Science Wars: Python vs. R; Data Science Toolbox virtual environments for command-line data science; T-index is like academic H-index; Movie Analytics in India: Dhoom 3 to Don 3
The LIONbook on machine learning and optimization, written by co-founders of LionSolver software, is provided free for personal and non-profit usage. Chapter 17 looks at Semi-supervised learning.
The LIONbook on machine learning and optimization, written by co-founders of LionSolver software, is provided free for personal and non-profit usage. Chapter 16 looks at Visualizing graphs and networks by nonlinear maps.
Predictive Analytics and Game theory can help answer questions like Can Dhoom 3 or Don 3 be as successful as Mother India, or which actor should have the main role for movie to be successful.
European Travel Patterns; Cloudera resources for Data Science beginners; New Book: A Programmer Guide to Data Mining - free download; 3 stages of Big Data to help clarify the confusion
New Poll: Did you switch between R and Python; 3 Stages of Big Data; Why statistical community is disconnected from Big Data and how to fix it; Why RapidMiner? By Usama Fayyad; and more analytics/data mining news
This book provides an in-depth introduction to the application of data mining and analytics techniques in science, medicine, industry, commerce, and other sectors.
New book "A Programmer Guide to Data Mining" - a guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. Free download of all chapters.
A public list of R #rstats freelancers - great resource; Top 10 Big Ideas in Harvard Statistics Class; 3 stages of Big Data to help clarify the confusion; Trifacta, maker of #BigData platform for machine-learning powered data visualization
The Big Ideas in Statistics include: Conditioning (the soul of statistics), Random variables and random vectors, Stories, Symmetry, Linearity of expectation, LOTUS, Variance, covariance, and correlation.
R is great for stats on one file, but for more complex data analysis use Python; How Facebook own Edgerank algorithm is killing it; Gates Foundation awards grants for using Big Data for Social Good; Preview of book Data Mining Applications with R
Highlights from a vigorous discussion on Statistical community and Big Data, including: Are data scientists reinventing statistics? Did statisticians miss the boat in 1990s? Is more data always better? Statistics 2.0?
Bruce Ratner examines how to combine skills acquired by experience (art) and a technique that reflects a precise application of fact or principle (science).
Google "Deep Learning" is outsmarting its human employees; Udacity Creates Online Degree Program For Data Science; JSON and #BigData will Shape the Internet of Things: RESTful APIs a key component; The Case Against #BigData In Sports
The 2013 Yahoo! Fellow Kalev Leetaru talks about Big Data, Global Diplomacy and Digital Heartbeat and application of Big Data to understanding international relationships.
Open Source Data Science MS Curriculum; 5 ways to handle #BigData in R; Yahoo SAMOA, Open Source Platform Mining Big Data Streams; 3 Levels of Data: fits in Excel; fits in RAM; a world of pain
CIO Review special report on 20 Most Promising Data Analytics Companies, which cover Big Data, real-time insights, enterprise analytics, employee analytics, health care, and even neuroscience based data analytics.
Unicorn Data Scientists vs Data Science Teams - Dec 30, 2013.
A recent post has generated an intense discussion about finding "unicorn" data scientists with a combination of all the needed skills, or whether that skillset is best filled by a team. Here are the highlights, including a proposal how to train well-rounded data scientists.
