Search results for deep learning

Found 2819 documents, 5944 searched:

• Web Scraping for Data Science with Python

  We take a quick look at how web scraping can be useful in the context of data science projects, eg to construct a social graph based of S&P 500 companies, using Python and Gephi.

  https://www.kdnuggets.com/2017/12/baesens-web-scraping-data-science-python.html

• Using TensorFlow for Predictive Analytics with Linear Regression

  This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!

  https://www.kdnuggets.com/2017/11/tensorflow-predictive-analytics-linear-regression.html

• Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey">Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey

  The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.

  https://www.kdnuggets.com/2017/11/best-online-masters-analytics-data-science.html

• Tips for Getting Started with Text Mining in R and Python

  This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.

  https://www.kdnuggets.com/2017/11/getting-started-text-mining-r-python.html

• More than the Hype: Beyond Gartner’s Hype Cycle

  Gartner publishes hype cycles across different technologies and sectors. Here we conduct detailed analysis of Gartner’s Hype Cycles.

  https://www.kdnuggets.com/2017/11/beyond-gartners-hype-cycle.html

• Cybersecurity: Managing Risk in the Information age, Harvard online short course

  Learn how to identify and manage operational risk, litigation risk and reputational risk. This course is brought to you by HarvardX in collaboration with GetSmarter, experts in online education for working professionals.

  https://www.kdnuggets.com/2017/11/harvard-cybersecurity-managing-risk-online-course.html

• XGBoost: A Concise Technical Overview">XGBoost: A Concise Technical Overview

  Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.

  https://www.kdnuggets.com/2017/10/xgboost-concise-technical-overview.html

• Density Based Spatial Clustering of Applications with Noise (DBSCAN)

  DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.

  https://www.kdnuggets.com/2017/10/density-based-spatial-clustering-applications-noise-dbscan.html

• Business intuition in data science

  Data Science projects are not just use of algorithms & building models; there are other steps of the project which are equally important. Here we explain them in detail.

  https://www.kdnuggets.com/2017/10/business-intuition-data-science.html

• An opinionated Data Science Toolbox in R from Hadley Wickham, tidyverse

  Get your productivity boosted with Hadley Wickham's powerful R package, tidyverse. It has all you need to start developing your own data science workflows.

  https://www.kdnuggets.com/2017/10/tidyverse-powerful-r-toolbox.html

• What makes a data visualization successful?

  Data visualisation gives very important insights about the data. But it is subjective to the goal of analysis & area of application. Let’s see how.

  https://www.kdnuggets.com/2017/09/what-makes-data-visualization-successful.html

• Closing the Insights-to-Action Gap">Closing the Insights-to-Action Gap

  There are many types of analytics for getting insight out of data, but the bigger and more difficult challenge is turning that insight into action. What should we do differently based on your findings?

  https://www.kdnuggets.com/2017/09/closing-insights-action-gap.html

• How To Write Better SQL Queries: The Definitive Guide – Part 2

  Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.

  https://www.kdnuggets.com/2017/08/write-better-sql-queries-definitive-guide-part-2.html

• How to squeeze the most from your training data

  In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.

  https://www.kdnuggets.com/2017/07/squeeze-most-from-training-data.html

• Marketing Analytics for Data Rich Environments

  A lot is changing in the world of marketing analytics. Marketing scientist Kevin Gray asks Professor Michel Wedel, a leading authority on this topic from the Robert H. Smith School of Business at the University of Maryland, what marketing researchers and data scientists most need to know about it.

  https://www.kdnuggets.com/2017/07/marketing-analytics-data-rich-environments.html

• Exploratory Data Analysis in Python

  We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.

  https://www.kdnuggets.com/2017/07/exploratory-data-analysis-python.html

• What Advice Would You Give Your Younger Data Scientist Self?">What Advice Would You Give Your Younger Data Scientist Self?

  I was asked this question recently via LinkedIn message: "What advice would you give your younger data scientist self?" The best piece of advice I honestly think I can give is this: Forget about "data science."

  https://www.kdnuggets.com/2017/07/advice-younger-data-scientist-self.html

• Spotlight on the Remarkable Potential of AI in KYC (Know Your Customer)

  Most people would have heard of the headline-making tremendous achievements in artificial intelligence (AI): Systems defeating world champions in board games like GO and winning quiz shows. These are small realizations of AI, but there is a silent revolution taking place in other areas, including Regulatory Compliance in Financial Services.

  https://www.kdnuggets.com/2017/07/spotlight-remarkable-potential-ai-kyc.html

• The Surprising Complexity of Randomness

  The reason we have pseudorandom numbers is because generating true random numbers using a computer is difficult. Computers, by design, are excellent at taking a set of instructions and carrying them out in the exact same way, every single time.

  https://www.kdnuggets.com/2017/06/surprising-complexity-randomness.html

• 7 Ways to Get High-Quality Labeled Training Data at Low Cost

  Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.

  https://www.kdnuggets.com/2017/06/acquiring-quality-labeled-training-data.html

• Data Science for Newbies: An Introductory Tutorial Series for Software Engineers

  This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.

  https://www.kdnuggets.com/2017/05/data-science-tutorial-series-software-engineers.html

• Will Data Science Eliminate Data Science?

  There are elements of what we do which are AI complete. Eventually, Artificial General Intelligence will eliminate the data scientist, but it’s not around the corner.

  https://www.kdnuggets.com/2017/05/data-science-eliminate-data-science.html

• Top 10 Recent AI videos on YouTube

  Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.

  https://www.kdnuggets.com/2017/05/top-10-recent-ai-videos-on-youtube.html

• The Quant Crunch: The demand for data science skills

  This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.

  https://www.kdnuggets.com/2017/05/quant-crunch-demand-data-science-skills.html

• The Value of Exploratory Data Analysis

  In this post, we will give a high level overview of what exploratory data analysis (EDA) typically entails and then describe three of the major ways EDA is critical to successfully model and interpret its results.

  https://www.kdnuggets.com/2017/04/value-exploratory-data-analysis.html

• The 42 V’s of Big Data and Data Science">The 42 V’s of Big Data and Data Science

  It's 2017 now, and we now operate in an ever more sophisticated world of analytics. To keep up with the times, we present our updated 2017 list: The 42 V's of Big Data and Data Science.

  https://www.kdnuggets.com/2017/04/42-vs-big-data-data-science.html

• A Brief History of Artificial Intelligence">A Brief History of Artificial Intelligence

  This post is a brief outline of what happened in artificial intelligence in the last 60 years. A great place to start or brush up on your history.
   
   

  https://www.kdnuggets.com/2017/04/brief-history-artificial-intelligence.html

• How to stay out of analytic rabbit holes: avoiding investigation loops and their traps

  Data scientists tend to think that their main job is to answer complex questions and gain in-depth insights, bu in reality it is all about solving problems – and the only way to solve a problem is to act on it.

  https://www.kdnuggets.com/2017/04/avoid-analytic-rabbit-holes-investigation-loops.html

• A Beginner’s Guide to Tweet Analytics with Pandas

  Unlike a lot of other tutorials which often pull from the real-time Twitter API, we will be using the downloadable Twitter Analytics data, and most of what we do will be done in Pandas.

  https://www.kdnuggets.com/2017/03/beginners-guide-tweet-analytics-pandas.html

• How to think like a data scientist to become one

  The author went from securities analyst to Head of Data Science at Amazon. He describes what he learned in his journey and gives 4 useful rules based on his experience.

  https://www.kdnuggets.com/2017/03/think-like-data-scientist-become-one.html

• A Simple XGBoost Tutorial Using the Iris Dataset

  This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.

  https://www.kdnuggets.com/2017/03/simple-xgboost-tutorial-iris-dataset.html

• Every Intro to Data Science Course on the Internet, Ranked">Every Intro to Data Science Course on the Internet, Ranked

  For this guide, I spent 10+ hours trying to identify every online intro to data science course offered as of January 2017, extracting key bits of information from their syllabi and reviews, and compiling their ratings.

  https://www.kdnuggets.com/2017/03/every-intro-data-science-course-ranked.html

• What is Customer Churn Modeling? Why is it valuable?

  Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.

  https://www.kdnuggets.com/2017/03/datascience-customer-churn-modeling.html

• The Data Science Project Playbook">The Data Science Project Playbook

  Keep your development team from getting mired in high-complexity, low-return projects by following this practical playbook.

  https://www.kdnuggets.com/2017/03/data-science-project-playbook.html

• What I Learned Implementing a Classifier from Scratch in Python

  In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.

  https://www.kdnuggets.com/2017/02/learned-implementing-classifier-scratch-python.html

• The Human Data Scientist: Safeguarding Your Career in the World of Automation

  "Data scientist" continues to be recognized as a top career, but does this mean unending spoils for the data scientist? With large scale mass automation on the horizon for numerous professions, what can we do to safeguard our positions?

  https://www.kdnuggets.com/2017/02/human-data-scientist-world-automation.html

• Top KDnuggets tweets, Jan 04-10: Cartoon: When Self-Driving Car takes you too far; A massive collection of free programming books

  Also AI #DataScience #MachineLearning: Main Developments 2016, Key Trends 2017; Scikit-Learn Cheat Sheet: #Python #MachineLearning

  https://www.kdnuggets.com/2017/01/top-tweets-jan04-10.html

• Smart Data Platform – The Future of Big Data Technology

  Data processing and analytical modelling are major bottlenecks in today’s big data world, due to need of human intelligence to decide relationships between data, required data engineering tasks, analytical models and it’s parameters. This article talks about Smart Data Platform to help to solve such problems.

  https://www.kdnuggets.com/2016/12/smart-data-platform-future-big-data-technology.html

• 10 Tips to Improve your Data Science Interview

  Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.

  https://www.kdnuggets.com/2016/11/tips-improve-your-data-science-interview.html

• How Bayesian Inference Works">How Bayesian Inference Works

  Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.

  https://www.kdnuggets.com/2016/11/how-bayesian-inference-works.html

• Data Science 101: How to get good at R

  Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.

  https://www.kdnuggets.com/2016/11/data-science-101-good-at-r.html

• Data Science of Sales Calls: The Surprising Words That Signal Trouble or Success

  While not as profound a problem as uncovering the secrets of the universe, how to conduct a successful sales conversation is an age-old problem, impacting millions of people every day.

  https://www.kdnuggets.com/2016/09/data-science-sales-calls-words-trouble-success.html

• Introduction to Local Interpretable Model-Agnostic Explanations (LIME)

  Learn about LIME, a technique to explain the predictions of any machine learning classifier.

  https://www.kdnuggets.com/2016/08/introduction-local-interpretable-model-agnostic-explanations-lime.html

• How to Become a Data Scientist – Part 1">How to Become a Data Scientist – Part 1

  Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!

  https://www.kdnuggets.com/2016/08/become-data-scientist-part-1.html

• Does Data Scientist Mean What You Think It Means?

  Do we have an accurate idea of what "data scientist" actually means? Read this thought-provoking opinion on the topic.

  https://www.kdnuggets.com/2016/08/data-scientist-mean-think-means.html

• Data Science Statistics 101

  Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.

  https://www.kdnuggets.com/2016/07/data-science-statistics-101.html

• 10 Algorithm Categories for AI, Big Data, and Data Science

  With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.

  https://www.kdnuggets.com/2016/07/10-algorithm-categories-data-science.html

• Storytelling: The Power to Influence in Data Science

  Data scientists need to share results, which is different than talking shop with other data scientists. Read about influencing people and telling stories as a data scientist.

  https://www.kdnuggets.com/2016/07/storytelling-power-influence-data-science.html

• Improving Nudity Detection and NSFW Image Recognition

  This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?

  https://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html

• Apache Spark Key Terms, Explained

  An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.

  https://www.kdnuggets.com/2016/06/spark-key-terms-explained.html

• Three Pitfalls to Avoid When Building Data Science Into Your Business

  An overview of pitfalls to avoid when building data science into your business, how to avoid them, and what to do instead.

  https://www.kdnuggets.com/2016/04/pitfalls-building-data-science-business.html

• Lift Analysis – A Data Scientist’s Secret Weapon

  Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.

  https://www.kdnuggets.com/2016/03/lift-analysis-data-scientist-secret-weapon.html

• Elementary, My Dear Watson! An Introduction to Text Analytics via Sherlock Holmes

  Want to learn about the field of text mining, go on an adventure with Sherlock & Watson. Here you will find what are different sub-domains of text mining along with a practical example.

  https://www.kdnuggets.com/2016/02/dato-introduction-text-analytics-sherlock-holmes.html

• How to Check Hypotheses with Bootstrap and Apache Spark

  Learn how to leverage bootstrap sampling to test hypotheses, and how to implement in Apache Spark and Scala with a complete code example.

  https://www.kdnuggets.com/2016/01/hypothesis-testing-bootstrap-apache-spark.html

• Everything You Need to Know about Natural Language Processing

  Natural language processing (NLP) helps computers understand human speech and language. We define the key NLP concepts and explain how it fits in the bigger picture of Artificial Intelligence.

  https://www.kdnuggets.com/2015/12/natural-language-processing-101.html

• Create or machine-learn fuzzy logic rules for use with an on-line inference engine

  New DocAndys SaaS service supports user-created embeddable Fuzzy Logic Expert Systems. Use rule language Darl to hand-create or machine-learn rule sets from data and use them via REST interfaces.

  https://www.kdnuggets.com/2015/12/docandys-machine-learning-fuzzy-logic-rules.html

• Overview of Python Visualization Tools

  An overview and comparison of the leading data visualization packages and tools for Python, including Pandas, Seaborn, ggplot, Bokeh, pygal, and Plotly.

  https://www.kdnuggets.com/2015/11/overview-python-visualization-tools.html

• The Data Science Machine, or ‘How To Engineer Feature Engineering’

  MIT researchers have developed what they refer to as the Data Science Machine, which combines feature engineering and an end-to-end data science pipeline into a system that beats nearly 70% of humans in competitions. Is this game-changing?

  https://www.kdnuggets.com/2015/10/data-science-machine.html

• Paradoxes of Data Science

  There are many paradoxes, ironies and disconnects in today’s world of data science: pain points, things ignored, shoved under the rug, denied or paid lip.

  https://www.kdnuggets.com/2015/08/paradoxes-data-science.html

• Three Essential Components of a Successful Data Science Team

  A Data Science team, carefully constructed with the right set of dedicated professionals, can prove to be an asset to any organization,

  https://www.kdnuggets.com/2015/08/3-components-successful-data-science-team.html

• Understanding Basic Concepts and Dispersion

  In analytics it is a common practice to understand the basic statistical properties of its variables viz. range, mean and deviation. Centrality measures are the most important to them, explore how to use these measures.

  https://www.kdnuggets.com/2015/08/statistics-understanding-basic-concepts-dispersion.html

• Interview: Joseph Babcock, Netflix on Genie, Lipstick, and Other In-house Developed Tools

  We discuss role of analytics in content acquisition, data architecture at Netflix, organizational structure, and open-source tools from Netflix.

  https://www.kdnuggets.com/2015/06/interview-joseph-babcock-netflix-in-house-developed-tools.html

• Insights from Data Science Handbook

  Here you can find perspective of lead data scientists on the definitions ranging from data science, metrics selection while solving a problem, work ethics, the art of storytelling and why data science is important in todays world.

  https://www.kdnuggets.com/2015/05/insights-from-data-science-handbook.html

• Top 10 Data Mining Algorithms, Explained

  Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.

  https://www.kdnuggets.com/2015/05/top-10-data-mining-algorithms-explained.html

• How to Lead a Data Science Contest without Reading the Data

  We examine a “wacky” boosting method that lets you climb the public leaderboard without even looking at the data . But there is a catch, so read on before trying to win Kaggle competitions with this approach.

  https://www.kdnuggets.com/2015/05/data-science-contest-leaderboard-without-reading-data.html

• Top LinkedIn Groups for Analytics, Big Data, Data Mining, and Data Science – Discussions up, Engagement down

  While discussions are growing, the comments and engagements are falling, especially since 2012. We cluster groups into 4 quadrants by activity level and identify most active and engaged groups. Open groups are twice as active as closed.

  https://www.kdnuggets.com/2015/05/top-linkedin-groups-analytics-big-data-mining-activity-engagement.html

• Top KDnuggets tweets, Mar 16-18: 87 Studies shown that accurate numbers aren’t more useful than the ones you make up (Dilbert)

  Also Sirius - a free, open-source version of Siri; #PI art: the first 13,689 digits of pi; Great tutorial + #Python code: 1-Layer Neural Networks.

  https://www.kdnuggets.com/2015/03/top-tweets-mar16-18.html

• KDnuggets™ News 14:n35, Dec 29

  Features | Software | Opinions | Interviews | News | Courses | Meetings | Jobs | Academic | Tweets | CFP | Quote Features 2015 Read more »

  https://www.kdnuggets.com/2014/n35.html

• KDnuggets™ News 14:n31, Nov 25

  Features | Opinions | Interviews | Reports | News | Webcasts | Jobs | Academic | Publications | Tweets | CFP | Quote Features Update: Read more »

  https://www.kdnuggets.com/2014/n31.html

• Apache Spark: O’Reilly Certification, EU Training, University Program

  Recent news on Apache Spark includes developer certification from O'Reilly, upcoming training workshops in EU by Databricks, and Spark tutorial events at major universities.

  https://www.kdnuggets.com/2014/09/apache-spark-training-certification-program.html

• Most Viewed Data Mining Talks at Videolectures

  Watch the top 25 most viewed popular data mining lectures on VideoLectures.NET to learn about topics ranging general big-data tutorials to monetizing data mining startups.

  https://www.kdnuggets.com/2014/09/most-viewed-data-mining-talks-videolectures.html

• KDnuggets™ News 14:n20, Aug 6

  Features | Software | News | Opinions | Interviews | Reports | Webcasts | Meetings | Jobs | Academic | Publications | Tweets | Quote Read more »

  https://www.kdnuggets.com/2014/n20.html

• Data Science Skills and Business Problems

  Discover what skills a data scientist benefits from learning and how the concept of a data scientist, and what businesses expect of them, has developed over time.

  https://www.kdnuggets.com/2014/06/data-science-skills-business-problems.html

• Data Lakes vs Data Warehouses

  Data Warehouses, traditionally popular for business intelligence tasks, are being replaced by less-structured Data Lakes which allow more flexibility.

  https://www.kdnuggets.com/2014/06/data-lakes-vs-data-warehouses.html

• Cartoon: Data Scientist Salary Negotiation

  New KDnuggets Cartoon looks at Data Scientist Salary Negotiation situation.

  https://www.kdnuggets.com/2014/04/cartoon-data-scientist-salary-negotiation.html

• Fractal Analytics Interview Highlights

  Fractal Analytics CEO on starting the company, competing with the best, managing attrition, attributes he looks for when hiring, 4 different analytics career tracks, strategic bets, and advice for starting data scientists.

  https://www.kdnuggets.com/2014/03/fractal-analytics-interview-highlights.html

• Split on Data Science Skills: Individual vs Team Approach

  The results of latest KDnuggets poll show an almost equal split between those who favor individual and those who favor the team approach. See the counterintuitive regional differences and interesting comments.

  https://www.kdnuggets.com/2014/01/split-on-data-science-skills-individual-vs-team-approach.html

• Interpreting Model Performance with Cost Functions

  Cost functions are critical for the correct assessment of performance of data mining and predictive models. This series goes deep into the statistical properties and mathematical understanding of each cost function and explores their similarities and differences.

  https://www.kdnuggets.com/2014/01/salford-interpreting-model-performance-with-cost-functions.html

• 2014 Jan: Courses and Events: Analytics, Big Data, Data Mining and Data Science

  All (69) | News, Software (19) | Courses, Events (20) | Publications (15) JMP Analytically Speaking Webcasts: Rob Reul (Jan 29), Michael Schrage (Feb 12) Read more »

  https://www.kdnuggets.com/2014/01/courses-events-old.html

• Unicorn Data Scientists vs Data Science Teams

  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.

  https://www.kdnuggets.com/2013/12/unicorn-data-scientists-vs-data-science-teams-discussion.html

• Statistics Software

  commercial | free Analyse-it!, accurate low-cost statistical software for Microsoft Excel. Appricon's Analysis Studio, a statistical analysis and modeling software with advanced logistic regression modeling, Read more »

  https://www.kdnuggets.com/software/statistics.html

• Software for Intelligent Agents and Bots

  Artificial Life, develops, markets and supports intelligent robots for the Internet. Autonomy provides bayesian-based infrastructure for user profiling, aggregation, categorization, and personalization of unstructured information. Read more »

  https://www.kdnuggets.com/software/agents-bots.html

• Bioinformatics Companies

  A B C D E F G H IJ K L M N O P Q R S T U V W XYZ A2IDEA is Read more »

  https://www.kdnuggets.com/companies/bioinformatics.html

• Top 10 trends in text analytics

  Data Driven Business recently interviewed forward thinking text analytics professionals from leading companies like Bank of America, Home Depot and PayPal, on challenges they are face, overcoming them, and the industry as a whole.

  https://www.kdnuggets.com/2013/11/top-10-trends-text-analytics.html

• Is Data Science The End of Statistics? A Discussion

  Here is an interesting discussion on LinkedIn, started by a provocative post "Data Science: The End of Statistics?" What is the relationship between Data Science and Statistics and in what sense are "Statistics" ending?

  https://www.kdnuggets.com/2013/04/data-science-end-statistics-discussion.html

• KDnuggets™ News 13:n23, Sep 24

  Features (8) | Software (3) | Webcasts (2) | Courses, Events (4) | Meetings (3) | Jobs (10) | Academic (3) | Competitions (1) | Publications Read more »

  https://www.kdnuggets.com/2013/n23.html

• KDnuggets™ News 13:n17, July 17

  Features (10) | Software (2) | Webcasts (3) | Courses, Events (3) | Meetings (4) | Jobs (4) | Academic (4) | Competitions (1) | Publications Read more »

  https://www.kdnuggets.com/2013/n17.html

• Cloud Analytics and SaaS Providers

  Algorithms.io, offering API to embed popular machine learning algorithms into applications; R as a service. Alpine Data Labs, helps you uncover the predictive analytic power Read more »

  https://www.kdnuggets.com/companies/cloud-analytics-saas.html

• KDnuggets™ News 13:n08, Mar 27

  Features (9) | Software (5) | Webcasts (1) | Courses, Events (2) | Meetings (1) | Jobs (6) | Academic (4) | Competitions (3) | Publications Read more »

  https://www.kdnuggets.com/2013/n08.html