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  • Free Advice For Building Your Data Science Career

    Got hired as data scientist, where to go now from here? Understand how you can make the most of your career by following the different paths like managerial, consulting, or as a domain expert.

  • Building effective “Citizens Data Scientist” teams

    The idea of citizen data scientists is being for more than a year, which suggests businesses to put the people from the business side in the work of exploring and analyzing data. Understand how you and your organisation can be benefitted by this.

  • 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.

  • The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line

    A great guide for the MBA, or any relatively non-technical convert, for getting comfortable with the command line and other technical skills required to excel in data science.

  • TD: Sr. Manager, Analytics Innovation

    Seeking a Senior Manager, Analytics Innovation, to be part of a dynamic, innovative team, exhibiting hands-on experience working in Big Data Analytical environments, a deep understanding of analytical techniques, and previous success building strong working relationships.

  • TD: Sr. Manager, Data Science

    Seeking a Senior Manager, Data Science, with a deep understanding of data mining and analytical technique, with a passion for doing leading-edge analytical work with big data alongside a dynamic, innovative team.

  • TD: Data Scientist

    Seeking a Data Scientist with a deep understanding of data mining and analytical technique, with a passion for doing leading-edge analytical work with big data alongside a dynamic, innovative team.

  • RetailMeNot: Principal Data Scientist, Attribution

    Lead the development of our retailer/brand attribution modeling and pricing analytics, and collaborate with product/ business teams to translate insights into a holistic pricing strategy to optimize for retailer/brand ROI goals and help RMN grow our share of marketing budgets.

  • Automatic Data Science: DataRobot, Quill and Loom Systems

    Automatic data science is on the rise. This post examines three recent visionary solutions: DataRobot, Quill and Loom Systems.

  • Ravel: Senior Data Scientist

    Seeking a Lead or Senior Data Scientist to join our rapidly growing team; A senior member of the Data Science team, you will design systems to tackle tough problems in the legal domain and expand our award-winning products.

  • Open Data in Elections: Using Visualization and Graphical Discovery Analysis for Voter Education and Civic Engagement

    This article makes a case for the importance of innovating using open data, its also proves that adapting open data principles with visual design can enhance transparency, foster accountability, and aid citizen and voter education in elections.

  • 3 Ways to Build an Analytics Dream Team

    So your March Madness bracket is busted. Maybe that new algorithm can through the first round next year. It's never too early to start building your analytics Dream Team.

  • Civis Analytics: Data Scientist, Statistics

    Be part of the Research and Development team, responsible for developing the fundamental data science methods, techniques, and best practices that power the mission of our company, performing predictive analytics, algorithm development, experimental design, visualization, and survey research.

  • Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department

    An exploration of data science team building, with insight into why engineers should not write ETL, and other not-so-subtle pieces of advice.

  • HR Analytics Starter Kit – Intro to R

    We review tools to help you start performing HR analytics with a focus on R platform, and providing useful examples for the HR and Workforce analytics using R.

  • 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.

  • AlphaGo is not the solution to AI

    The field will be better off without an bust cycle it is important for people to keep and inform a balanced view of successes and their extent. AlphoGo might be a step forward for the AI community, but it is still no way close to the true AI.

  • Top KDnuggets tweets, Feb 29 – Mar 06: Data Science Process; Wisdom of Crowds fails to solve this simple puzzle

    Wisdom of Crowds fails to solve this simple #math #puzzle ; #DataScience Process - the work flow of a data scientist; R is the fastest-growing language on #StackOverflow; @DeepDrumpf #DeepLearning #Twitterbot imitates #DTrump, more plausible than real one.

  • Automated Data Science and Data Mining

    Automated Data Science is becoming more popular. Here is our initial list of automated Data Science and Data Mining platforms.

  • Wells Fargo: Data Scientist

    Apply statistical/ machine learning techniques, to develop segmentations, predictive models, experimental designs, and decision analyses; Develop algorithms to optimize product offering, pricing, and distribution channel, and more.

  • Webinar: Building Data Products for Predictive Maintenance, Mar 9

    Dr. Kirk Borne, Data Science thought leader, will address the interesting trends in the world of data science. DataRPM Co-founder Ruban Phukan will show how a cognitive data science platform like DataRPM helps companies to build data products.

  • Top KDnuggets tweets, Feb 22-28: Quantifying Similarity in Structured Data; #Oscar #DataScience: 4-5 nominations no guarantee of winning

    A Statistical View of #DeepLearning; Impressive tutorial - Tree Kernels: Quantifying Similarity in Structures; Conversation with Data Scientist Sebastian Raschka - new podcast; How to become a #Bayesian in eight easy steps.

  • Top KDnuggets tweets, Feb 15-21: Is Big Data Still a Thing? 10 types of #regression. Which one to use?

    10 types of #regression. Which one to use? Is Big Data Still a Thing? 2016 #BigData Landscape; Demystifying #DeepReinforcement Learning; #TextMining #SouthPark.

  • Simplilearn and Tableau to educate 200,000 Data Scientists by 2020

    Simplilearn partners with Tableau to nurture talent pool of 200,000 Data Science professionals by 2020. The partnership will offer high quality instructor-led training, e-learning, and projects on the latest version of Tableau.

  • Embedding Open Cognitive Analytics at the IoT’s Edge

    Cognitive computing is penetrating more aspects of the IoT as algorithms enable edge devices and applications. Understand how unstructured data captured by IoT edge devices with the help of cognitive algorithms distilled into actionable insights.

  • The Next Big Inflection in Big Data: Automated Insights

    To keep up with big data and improve our use of information, we need insightful applications that will quickly and inexpensively extract correlations while associating insights with actions.

  • 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.

  • SanDisk: Senior Big Data Engineer/Hadoop Developer

    Planning and designing next-generation Big Data System architectures, managing the development and deployment of Hadoop applications.

  • Top 10 tweets Jan 25-31: DataViz: how a decision tree works; Nice and Brief Tutorial on Python

    DataViz - how a decision tree makes classifications; Very Nice and Brief Tutorial on #Python #DataScience #DataViz; Per Einstein, time flows slower in Meetings than in empty space #hum; Top 10 Skills for #DataScience professionals.

  • FirstFuel Software: Data Scientists, Senior/Junior positions

    Developing and deploying the core statistical/machine learning algorithms with the Research group; coding these algorithms in production quality software with the engineering team.

  • 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.

  • Top KDnuggets tweets, Jan 11-24: Why R Users will inevitably become #Bayesians; Is #Quran really more violent that #Bible?

    TextAnalytics examines: Is #Quran really more violent that #Bible? Why R Users will inevitably become #Bayesians; Next #MachineLearning problem: what to do with 80% accurate algorithm? ;Learning to Code #NeuralNetworks #MachineLearning Tutorial;

  • SanDisk: Senior Staff Hadoop Developer

    Planning and designing next-generation Big Data System architectures, managing the development and deployment of Hadoop applications.

  • Schwab: Director, Data Science

    Use data mining and advanced statistical methods to provide decision makers across Schwab with insights and solutions to important business projects.

  • Minority Report Visualized – Chicago Police Analyzed

    Who watches the watchmen? This article examines the recently available Chicago Police misconduct allegation dataset from the Invisible Institute.

  • Data Science and Prejudice – Blessing or Curse ?

    We examine the deep nature of bias and prejudice and wonder if prejudiced minds and 'good' data scientists coexist in harmony and if they can coexist, does it lead to disruption or disruptive innovation?

  • 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.

  • 10 Business Intelligence Trends for 2016

    BI analysts, industry players predict the rise of self-service, Big Data analytics, real-time data in the coming year.

  • Importance of Data Science for IoT business

    Here, we have explored how IoT businesses can leverage data science for IT strategies, service analysis stack, capacity planning, hardware maintenance, competitive advantages and anomaly detection. Along with, the different application in multiple IoT industries.

  • Which Database is best for an Analyst?

    Database comparisons usually look at architecture, cost, scalability, and speed, but rarely address the other key factor: how hard is writing queries for these databases? We examine which of the top 8 databases are easiest to use.

  • 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.

  • Top KDnuggets tweets, Nov 30 – Dec 6: The Balkanization of #DataScience: will it lead to empire or republics?

    The Balkanization of #DataScience, #BigData: will it lead to empire or republics;7 Essential Resources, Tips To Get Started With Data Science ; Data Science #Cartoon Contest has 3 winners.

  • American Family Insurance: Research & Analytics Director

    Focus on the analytics that support our products, pricing, and claims. Top candidates will have deep technical experience in predictive analytics, machine learning, and main tools.

  • Microsoft: Data Solution Architect

    Drive high priority customer initiatives, leveraging Azure data services to solve the biggest and most complex data challenges faced by Microsoft enterprise customers.

  • A Community Event for Innovative Spark Apps: A Datapalooza Dispatch

    Datapalooza, which is holding its inaugural event this week in San Francisco, is proving to be a seedbed for innovation apps in the Spark community. James Kobielus describes the highlights.

  • Disney Parks & Resorts: Decisioning Development Marketing Manager

    Responsible the in-house management of Guest decisioning in WDPR real-time personalization tool.

  • Datapalooza: Produce Your Data Application Development Concert, Nov 10-12, San Francisco

    Datapalooza will enable you to take your data-science skills to the next level. You’ll gain hands-on experience, enjoy one-on-one coaching, and learn how to build a practical data-science product in just three days - Nov 10-12 in San Francisco.

  • 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.

  • SAS Adds Certifications for Big Data and Data Science

    SAS has been in the business of analytics and data science for long time, this new offering comes at an opportune time as big data technologies are requiring new skills and demand for analytical talent is at an all-time high.

  • Top KDnuggets tweets, Oct 20-26: Why Self-Driving Cars Must Be Able to Kill: an impossible dilemma of algorithmic morality

    Why Self-Driving Cars Must Be Able to Kill: an impossible dilemma of algorithmic morality; Cartoon: KDnuggets Addiction; Good overview: #BigData Infrastructure at IFTTT.

  • Top stories for Oct 18-24: R vs Python: head to head data analysis; Big Data + Wrong Method = Big Fail

    R vs Python: head to head data analysis; The Best Advice From Quora on How to Learn Machine Learning; Big Data + Wrong Method = Big Fail; Infographic - Data Scientist or Business Analyst? Knowing the Difference.

  • Renovate America: Data Scientist – Hero Gov Team

    Highly motivated Data Scientist specializing in quantitative analysis ideally with experience working GIS, focus on forecasting and benchmarking economic impact in HERO Communities.

  • 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?

  • Top KDnuggets tweets, Oct 6-12: Big innovations in Data Science yet to come; 5 steps to learn Data Science

    Big innovations in #DataScience yet to come: new #algorithms, data, new thinking; 5 steps to learn #DataScience: 1. Learn to love data; Why Tracy-Widom Mysterious Statistical Law so common - Phase transitions; Best of /r/MachineLearning in September.

  • 5 steps to actually learn data science

    Data science is a broad and varied field, and hence the path to becoming a unicorn is full of darkness. To light up your path and guide you to become one, here are 5 simple steps to be followed.

  • Xerox Research Centre India: Research Scientist/Engineer: Speech and Signal Processing

    Speech group main goal is to enable Human-Computer-Smartphone interactions to be speech enabled in our day-to-day and professional lives.

  • Xerox Research Centre India: Research Scientist/Engineer: Text and Graph Analytics

    The team is working on challenging research problems with real life relevance pertaining to different business verticals such as Customer Care, Social Media, Healthcare, Transportation and Education.

  • Xerox Research Centre India: Research Scientist/Engineer: Process Mining and/or Business Process Management

    Seeking researchers/engineers with strong technical expertise in the area of Business Process Modeling, Mining, Analysis and Optimization, to participate in exciting research and technology development projects.

  • Xerox Research Centre India: Research Scientist/Engineer: Multimedia Analytics

    Seeking dynamic and talented researchers for (Senior) Research Scientist/Engineer positions, with expertise in Multimedia analytics including video/image processing, speech/audio/text processing, Machine Learning, and Data Mining.

  • Disney: Decisioning Development Manager II

    Responsible for the in-house management of Guest decisioning in WDPR real-time personalization tool; engage the business to frame, structure and prioritize business decisioning needs, and meet those needs through the creation, and coding, of content decisioning logic resulting in tailored recommendations.

  • 30 Can’t miss Harvard Business Review articles on Data Science, Big Data and Analytics

    Here are 30 Harvard Business Review (HBR) articles on big data, data science and analytics that provide insights about the latest technology and happenings in the world of data.

  • Blast Analytics & Marketing: Technical Business Analyst

    Work with Marketing Directors and execs to help them define goals, understand levers, and get meaningful insights from their data; provide accurate analysis of clickstream digital analytics and other data.

  • AspenTech: Data Scientist

    If you want a shot at greatness, as a member of the data science team, you will develop and investigate hypotheses, structure experiments and build mathematical models to understand data patterns and relationships and prescribe actions and options.

  • Doing Data Science at Twitter

    Data scientist career exciting, fulfilling and multidimensional career path. Understand through the journey of a data scientist of twitter about data scientists roles, responsibilities and skills required to perform them.

  • Syracuse iSchool: Asst. Professor, Data Science

    Syracuse iSchool is soliciting applications from scholars to join its renowned and interdisciplinary faculty in the area of data science.

  • Aug 2015 Analytics, Big Data, Data Mining, Data Science Acquisitions, Startup roundup

    Aug 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: HyperVerge, Top 100 by funding, Deep Genomics, Driveway Software, BigML, and many more.

  • Big Data Analytics in Hotel Industry

    The Hotel industry is another data rich industry that captures huge volumes of data of different types. Find out, how Customer Segmentation, Energy Consumption, Investment Management, and Resource Allocation for it can be revolutionized using big data analytics.

  • Top KDnuggets tweets, Aug 25-31: How to become a #DataScientist for Free; The R universe of Hadley Wickham

    How to become a Data Scientist for Free; #BigData is Out, #MachineLearning is in; The universe of Hadley Wickham, the Man Who Revolutionized R; Book review: Fundamentals of #DeepLearning.

  • The Present and the Future of the KDD Cup Competition

    KDD cup is the first and among most prestigious competitions in data science, Among key takeaways from KDD Cup 2015: XGBoost – Gradient Boosted Decision Trees package works wonders in data classification, feature engineering is the king, and team work is crucial.

  • eBay: Data Scientist – Statistician

    Conduct A/B tests and deep dive statistical analysis/modeling for marketing analytic at eBay. Critical skills/experience on R, SQL, and online A/B experiments required.

  • Business Analytics & Business Intelligence Online Certificates & Degrees

    Here's a comprehensive list of Online graduate degrees and certificate programs in Business Analytics and Business Intelligence along with their curriculum & program costs. Most of these programs also have partnerships with industry certifications by market leaders.

  • 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.

  • Top KDnuggets tweets, Aug 11-17: Data Science Breakthrough in avoiding overfitting; Top Big Data, Data Science influencers

    Understanding #Convolution in #DeepLearning; Top #BigData #DataScience influencers @hmason @hackingdata @kirkdborne @flowingdata; Data Science Breakthrough in avoiding #overfitting: The reusable holdout method; R Programming: Where are 50,000 R programmers?

  • Nexon: Sr. Game Analyst

    Work closely with various internal teams to help drive innovation in our games through analytics, insights and data-driven decisions.

  • Overcoming Overfitting with the reusable holdout: Preserving validity in adaptive data analysis

    Misapplication of statistical data analysis is a common cause of spurious discoveries in scientific research. We demonstrate a new approach for addressing the challenges of adaptivity based on insights from privacy-preserving data analysis.

  • 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,

  • 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.

  • SapientNitro: Director, Performance/Marketing Analytics

    Lead multi-channel, real time, measurement and analytics initiatives, have experience of developing product and service propositions and taking them to market.

  • Interview: Stefan Groschupf, Datameer on Why Domain Expertise is More Important than Algorithms

    We discuss large-scale data architectures in 2020, career path, open source involvement, advice, and more.

  • Interview: Brian Kursar, Toyota on Big Data & Advanced Analytics – Cornerstones of Innovation

    We discuss the Big Data architecture at Toyota, executives’ perception of Analytics, Toyota Innovation Fair, advice, trends, and more.

  • You’re invited to join Dean Abbott & other industry thought leaders

    Dean Abbott is a "rock star" hands-on practitioner - attend his keynote, hands-on methods workshop, and hands-on ensembles workshop at PAW Business, Boston, Sep 27 - Oct 1. Use KDN150 for KDnuggets discount.

  • Digital Catapult Centre: Lead Technologist – Data (Permanent or Contract)

    Have deep expertise in data technologies, help catapult UK best ideas from startups and university research to grow into large-scale companies.

  • Macy’s: VP, Marketing Data Science and Analytics

    Informing business and marketing strategy, guiding tactical execution and identifying new opportunities to drive and grow sales across Macy's.

  • Top KDnuggets tweets, Jun 30 – Jul 06: Click Testing Proved that Beards Are Still A Thing; 16 Free #DataScience Books

    How Screenshot Click Testing Proved that Beards Are Still A Thing; 16 Free #DataScience Books; How to avoid #Overfitting using #Regularization; #DataScience must read: quick puzzle tests your problem solving.

  • 10 Key Tips for Entry-Level Analytics Professionals

    With so many companies hunting down the data scientist. There are few things which aspirants can do to increase their chances of being getting selected into the best ones.

  • Doubt and Verify: Data Science Power Tools

    In the end, there is no truth, no ultimate ground truth, no lie-free utterances, as everything is contextual based on incomplete facts and knowledge. All world models are flawed, but Data Science has 2 power tools.

  • Civis Analytics: Data Scientist – Statistics

    Apply state-of-the-art statistical methods to our research and develop entirely new methods to use our data in new ways; be a critical voice on determining which statistical approaches are appropriate for which problems.

  • Introduction to Big Data with Apache Spark

    Apache spark, has been one of the exciting technologies in recent years for the big data development. Here, you can find why spark is better than its predecessors and what are its major pillars.

  • Top KDnuggets tweets, Jun 09-15: Which Big Data, Data Mining, Data Science Tools go together? Good Comparison of ML classifiers

    Which #BigData, #DataMining and #DataScience Tools go together? Good Comparison of ML classifiers: Decision Trees, Regression, SVM, #NeuralNets; In #machinelearning, what is better more #data or better algorithms? Need both.

  • 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.

  • Virginia Tech: Data Engineer

    The Discovery Analytics Center applies cutting edge machine learning to forecast worldwide events using Twitter, Facebook, Google News, and blogs. Data engineer will help harness and curate the ever-growing influx of data.

  • Is Analytics Career Right for You?

    An analytical way to decide whether you should pursue a career in analytics. We shared some economic ways to getting started and mind-set required for entering into this exciting field.

  • Interview: Beth Smith, General Manager of the IBM Analytics Platform business, on Analytics, Hadoop, Spark

    We discuss coming Analytics surprises, what has changed, Open Source, Hadoop, Apache Spark, Open Data Platform, new analytics roles, IBM resources for analytics educations, and more.

  • May 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    May 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: Elon Musk, Google buys Timeful, Lexalytics Wizard GUI, Banjo $100M, Unicorn myth and reality.

  • Interview: Ranjan Sinha, eBay on Winner Insights from International Sorting Competitions

    We discuss advancements in the field of Personalization, lessons from winning sorting competitions, Data Science trends, career advice, and more.

  • TDWI Boston, July 26-31, 2015 – The Analytics Experience

    Join us at The Analytics Experience to explore the art and science of realizing business value from data. We are bringing together industry experts, solution providers, and practitioners to dig deep into analytics competencies, practices, and technologies. Special 10% Discount code before June 26.

  • Top KDnuggets tweets, May 26 – Jun 1: Step by Step Guide to Learn #DataScience on R

    Creator of @ApacheSpark @Matei_Zaharia wins @ACM Doctoral Dissertation Award; LeaRning Path: Step by Step Guide to Learn #DataScience with R; Top 20 #Python #MachineLearning #OpenSource Projects; R vs #Python for #DataScience.

  • 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.

  • 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.

  • 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.

  • Quantcast: Senior Principal Scientist

    A seasoned leader to guide our data scientists, software engineers, and company in applying innovative machine learning, data mining, statistics, and software development techniques to solve business problems.

  • Gaming Analytics Summit 2015, San Francisco – Day 1 Highlights

    Highlights from the presentations by Gaming Analytics leaders from Facebook, Turbine/Warner Bros Games, and Sega on day 1 of Gaming Analytics Innovation Summit 2015 in San Francisco.

  • Top KDnuggets tweets, Apr 27 – May 3: Attack of the #BigData Startups; Data Science from Scratch: First Principles with Python

    DataScience from Scratch: First Principles with Python; Attack of the #BigData Startups - @cbinsights maps the industries; Not accurate, but #fun ! See how old Microsoft thinks you are; US Chief Data Scientist @dpatil on #DataScience #BigData past and future.

  • 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.

  • Top stories for Apr 26 – May 2: The Myth of Model Interpretability; How To Become a Data Scientist and Get Hired

    The Myth of Model Interpretability; How To Become a Data Scientist And Get Hired; Top LinkedIn Groups for Analytics, Big Data, Data Mining; Emmanuel Letouze, Data-Pop Alliance on Big Data for Development and Future Prospects.

  • Macys: VP, Advanced Analytics

    Responsible for informing business and customer strategy, enabling and optimizing dynamic marketing and site capabilities, and for driving key customer, sales and efficiency KPIs.

  • KDnuggets Free Pass to Strata Hadoop World London, 5-7 May, 2015

    Strata + Hadoop World has been called "mind-blowing", "an amazing event", "the most interesting and informative conference". Win free registration via KDnuggets.

  • Interview: Xia Wang, AstraZeneca on Big Data and the Promise of Effective Healthcare

    We discuss challenges in analyzing text data, Big Data impact on translational bioinformatics, advice, desired skills in data scientists, and more.

  • March 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    March 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: Apple buys Acunu, Algorithmia Launches, Dataminr raises $130M, PatentVector, Looker, and more.

  • Algorithmia – How Marketplaces are Fostering Innovation?

    We have a marketplace for almost everything – mobile apps, cabs, hotels, and what not. But, not for algorithms. Algorithmia takes up that challenge.

  • Interview: Beth Diaz, Washington Post on How Dark Social is Shadowing Modern Analytics

    We discuss recent events at Washington Post, growth initiatives, the growing pain of Dark Social, how to deal with it, audience analytics, advice and more.

  • A Data Scientist Advice to Business Schools

    To remain relevant business school graduates must learn to speak to Data Scientists, whose domain expertise is playing a vital role in an organization's ability to compete in today's market.

  • The Grammar of Data Science: Python vs R

    In this post, I will elaborate on my experience switching teams by comparing and contrasting R and Python solutions to some simple data exploration exercises.

  • Top KDnuggets tweets, Mar 23-25: 24 free resources on Data Mining, Data Science; More Training Data or More Complex Models?

    24 free resources and online books on #DataMining, #DataScience, #MachineLearning; New R Online Tool for Seasonal Adjustment of time series; Key #DataScience question: More Training Data or More Complex Models?; Twitter #DataMining finds origins of ISIS support.

  • NPD: Head of Global Data Classification (Data Scientist)

    Run our Global Data Classification organization with an initial focus on standardizing processes and developing productivity measurements.

  • 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.

  • Top KDnuggets tweets, Mar 12-15: Cartoon: the most difficult challenge facing the 1st US Chief Data Scientist @dpatil

    Cartoon: top challenge for US Chief Data Scientist DJ Patil; In-depth intro to #MachineLearning, #Statistics, R: 15 hours of videos; Amazing! Forget coding word meaning, grammar, syntax - now #DeepLearning can learn everything.

  • Strata + Hadoop World London, 5-7 May 2015

    Strata + Hadoop World has been called "mind-blowing", "an amazing event", "the most interesting and informative conference". See for yourself in London and get a special KDnuggets discount.

  • Feb 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    Feb 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: @Kaggle cuts 1/3 of staff, Infosys buys Panaya, RapidMiner gets $15M, Palantir buys Fancy That, Hitachi buys Pentaho, and more.

  • Strata + Hadoop World 2015 San Jose – Day 2 Highlights

    Strata + Hadoop World 2015 was a great conference, and here are key insights from some of the best sessions on day 2.

  • PeerIQ: Credit Quant

    Developing statistical and quantitative models to support prepayment, default and loss forecasting; Basel and CCAR; pricing and valuation; and other economic calculations.

  • HomeUnion: Sr. Data Scientist

    Be fearless: include heuristics into a solution; be skeptic: self-scrutinize, able to pivot quickly; be creative: experiment with modeling techniques, invent and test features.

  • Top KDnuggets tweets, Feb 26 – Mar 1: Bayes Theorem explained with Lego; 10 Cool #BigData Cartoons

    Cute and Educational: Bayes Theorem explained with Lego; 10 Cool #BigData Cartoons #TGIF; #DataMining Indian Recipes finds spices make negative food pairing more powerful; Key Take-Aways from Gartner 2015 MQ for #BI & Analytics Platforms.

  • Strata + Hadoop World 2015 San Jose – Day 1 Highlights

    Here are the quick takeaways and valuable insights from selected talks at one of the most reputed conferences in Big Data – Strata + Hadoop World 2015, San Jose.

  • Top KDnuggets tweets, Feb 18-19: New Face Detection Algorithm to revolutionize search; How to transition from Excel to R

    Practical #DataScience in #Python #MachineLearning - nice intro; New Face Detection Algorithm to revolutionize search; Well written: How to Transition from Excel to R; Microsoft launches #Azure #MachineLearning Platform for #BigData, adds Python.

  • Syracuse University: Interdisciplinary Faculty

    The school hosts 5 research centers, including a new created Center for Computational and Data Sciences, which advances scholarships on computational data analytics.

  • courses on RESTful APIs

    Applying analytics to big data requires a mechanism to rapidly get and share data and RESTful APIs is the standard way doing it. Learn how to write Python code to ingest data, communicate with, and create RESTful APIs with online courses from

  • Active Data Mining, Data Science blogs

    Here are 85 or so active (recently updated) data mining, data science, and machine learning blogs.

  • Top stories for Feb 8-14: 10 things statistics taught us about Big Data; Data Science Most Confused Jargon

    10 things statistics taught us about big data analysis; Data Science's Most Used, Confused, and Abused Jargon; Top 30 people in Big Data and Analytics; Cartoon: Data Scientist 3 wishes for Valentine Day.

  • Interview: M.C. Srivas, CTO, MapR on Data Agility – The Next Frontier of Big Data

    We discuss the competitive differentiation of MapR, challenges in consumerizing Big Data, trends, strategy recommendations, desired skills and more.

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