Search results for Advanced Analytics

Found 740 documents, 5944 searched:

• Analyzing the Probability of Future Success with Intelligence Node’s Attributes Evolution Model

  The analytics team at Intelligence Node have been working on developing a Limited Memory model (which first started as a Reactive model) aka the 'The Probability of Future Success' model. This model explores a new market driven approach to identifying future trends and probability of success for specific product attributes based on a series of dynamic metrics and attributes. Read this article to know more.

  https://www.kdnuggets.com/2022/02/analyzing-probability-future-success-intelligence-node-attributes-evolution-model.html

• Advance your data science career to the next level

  SAS offers a wide range of hands-on courses for data science professionals to help you get ahead – and stay ahead – in your data science career.

  https://www.kdnuggets.com/2021/12/sas-advance-data-science-career-next-level.html

• Data Scientists: How to Sell Your Project and Yourself

  Follow this formula for the perfect elevator pitch.

  https://www.kdnuggets.com/2021/11/data-scientists-sell-project.html

• Transforming your business with SAS® Viya® on Microsoft Azure

  Faster, trusted decisions are in the cloud. See how you can use the flexibility, scalability and agility of modern technologies to advance your organization’s goals. Read our blog with 3-part video demo.

  https://www.kdnuggets.com/2021/10/sas-viya-microsoft-azure.html

• The Top Industries Hiring Data Scientists in 2021">The Top Industries Hiring Data Scientists in 2021

  People realize that effective uses of data can increase competitiveness, even in a challenging marketplace. Here are six industries hiring data scientists now that will likely continue doing so for the foreseeable future.

  https://www.kdnuggets.com/2021/08/top-industries-hiring-data-scientists-2021the-top-industries-hiring-data-scientists-in-2021.html

• Coding Ethics for AI & AIOps: Designing Responsible AI Systems

  AI ops has taken Human machine collaboration to the next level where humans and machines are not just coexisting but are collaborating and working together like team members.

  https://www.kdnuggets.com/2021/08/coding-ethics-ai-aiops-designing-responsible-ai-systems.html

• 5 Data Science Career Mistakes To Avoid

  Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.

  https://www.kdnuggets.com/2021/08/5-data-science-career-mistakes-avoid.html

• 5 Mistakes I Wish I Had Avoided in My Data Science Career

  Everyone makes mistakes, which can be a good thing when they lead to learning and improvements over time. But, we can also try to first learn from others to expedite our personal growth. To get started, consider these lessons learned the hard way, so you don’t have to.

  https://www.kdnuggets.com/2021/07/5-mistakes-data-science-career.html

• Building Tech Skills in 2021

  With all the workforce changes last year, it is not surprising that employees lack the skills to meet new demands. To be ready for today’s challenges, companies need sound methods to assess what skills their employees have, the ability to identify the gaps, and a plan to upskill them for success. You can read the survey results here, along with predicted learning and development trends, and insights for upskilling, cross-skilling and reskilling your workforce.

  https://www.kdnuggets.com/2021/07/sas-building-tech-skills.html

• Data Validation in Machine Learning is Imperative, Not Optional

  Before we reach model training in the pipeline, there are various components like data ingestion, data versioning, data validation, and data pre-processing that need to be executed. In this article, we will discuss data validation, why it is important, its challenges, and more.

  https://www.kdnuggets.com/2021/05/data-validation-machine-learning-imperative.html

• Data Validation and Data Verification – From Dictionary to Machine Learning

  In this article, we will understand the difference between data verification and data validation, two terms which are often used interchangeably when we talk about data quality. However, these two terms are distinct.

  https://www.kdnuggets.com/2021/03/data-validation-data-verification-dictionary-machine-learning.html

• What is Graph Theory, and Why Should You Care?

  Go from graph theory to path optimization.

  https://www.kdnuggets.com/2021/01/graph-theory-why-care.html

• Building a Deep Learning Based Reverse Image Search">Building a Deep Learning Based Reverse Image Search

  Following the journey from unstructured data to content based image retrieval.

  https://www.kdnuggets.com/2021/01/deep-learning-based-reverse-image-search.html

• MLOps: Model Monitoring 101

  Model monitoring using a model metric stack is essential to put a feedback loop from a deployed ML model back to the model building stage so that ML models can constantly improve themselves under different scenarios.

  https://www.kdnuggets.com/2021/01/mlops-model-monitoring-101.html

• MLOps – “Why is it required?” and “What it is”?

  Creating an model that works well is only a small aspect of delivering real machine learning solutions. Learn about the motivation behind MLOps, the framework and its components that will help you get your ML model into production, and its relation to DevOps from the world of traditional software development.

  https://www.kdnuggets.com/2020/12/mlops-why-required-what-is.html

• Data science certification – why it is important and where to get it?

  Data science jobs are one of most sought after and in-demand jobs in the IT industry right now. In order to get into this field and get these data science jobs, certification is needed and that is widely discussed below.

  https://www.kdnuggets.com/2020/11/greatlearning-data-science-certification.html

• The Rise of the Machine Learning Engineer">The Rise of the Machine Learning Engineer

  The evolution of Big Data into machine learning applications ushered in an exciting era of new roles and skillsets that became necessary to implement these technologies. With the Machine Learning Engineer being such a crucial component today, where the evolution of this field will take us tomorrow should be fascinating.

  https://www.kdnuggets.com/2020/11/rise-machine-learning-engineer.html

• AI and Automation meets BI">AI and Automation meets BI

  Organizations use a variety of BI tools to analyze structured data. These tools are used for ad-hoc analysis, and for dashboards and reports that are essential for decision making. In this post, we describe a new set of BI tools that continue this trend.

  https://www.kdnuggets.com/2020/11/ai-automation-meets-bi.html

• Build Your Own AutoML Using PyCaret 2.0

  In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.

  https://www.kdnuggets.com/2020/08/build-automl-pycaret.html

• The List of Top 10 Lists in Data Science">The List of Top 10 Lists in Data Science

  The list of Top 10 lists that Data Scientists -- from enthusiasts to those who want to jump start a career -- must know to smoothly navigate a path through this field.

  https://www.kdnuggets.com/2020/08/top-10-lists-data-science.html

• Labelling Data Using Snorkel

  In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.

  https://www.kdnuggets.com/2020/07/labelling-data-using-snorkel.html

• How to make AI/Machine Learning models resilient during COVID-19 crisis

  COVID-19-driven concept shift has created concern over the usage of AI/ML to continue to drive business value following cases of inaccurate outputs and misleading results from a variety of fields. Data Science teams must invest effort in post-model tracking and management as well as deploy an agility in the AI/ML process to curb problems related to concept shift.

  https://www.kdnuggets.com/2020/06/ai-ml-models-resilient-covid-19-crisis.html

• Upcoming Webinars and Online Events in AI, Data Science, Machine Learning: June

  Here are some interesting upcoming webinar, online events and virtual conferences in in AI, Data Science, and Machine Learning.

  https://www.kdnuggets.com/2020/06/upcoming-webinars-online-events-june.html

• Machine Learning in Power BI using PyCaret

  Check out this step-by-step tutorial for implementing machine learning in Power BI within minutes.

  https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html

• How (not) to use Machine Learning for time series forecasting: The sequel">How (not) to use Machine Learning for time series forecasting: The sequel

  Developing machine learning predictive models from time series data is an important skill in Data Science. While the time element in the data provides valuable information for your model, it can also lead you down a path that could fool you into something that isn't real. Follow this example to learn how to spot trouble in time series data before it's too late.

  https://www.kdnuggets.com/2020/03/machine-learning-time-series-forecasting-sequel.html

• Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms">Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms

  The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.

  https://www.kdnuggets.com/2020/02/gartner-mq-2020-data-science-machine-learning.html

• Fidelity on How to Find a Tailor-Fit Unicorn Data Scientist

  Predictive Analytics World for Financial Services in Las Vegas, May 31-Jun 4 is honored to host an exceptional keynote by Fidelity Investments’ AI and Data Science Center of Excellence Leader, Victor Lo: "How to Find a Tailor-Fit 'Unicorn' Data Scientist for Financial Services". Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.

  https://www.kdnuggets.com/2020/02/paw-find-tailor-fit-unicorn-data-scientist.html

• The Rise of User-Generated Data Labeling

  Let’s say your project is humongous and needs data labeling to be done continuously - while you’re on-the-go, sleeping, or eating. I’m sure you’d appreciate User-generated Data Labeling. I’ve got 6 interesting examples to help you understand this, let’s dive right in!

  https://www.kdnuggets.com/2019/12/rise-user-generated-data-labeling.html

• Data Preparation for Machine learning 101: Why it’s important and how to do it

  As data scientists who are the brains behind the AI-based innovations, you need to understand the significance of data preparation to achieve the desired level of cognitive capability for your models. Let’s begin.

  https://www.kdnuggets.com/2019/10/data-preparation-machine-learning-101.html

• The Hidden Risk of AI and Big Data

  With recent advances in AI being enabled through access to so much “Big Data” and cheap computing power, there is incredible momentum in the field. Can big data really deliver on all this hype, and what can go wrong?

  https://www.kdnuggets.com/2019/09/risk-ai-big-data.html

• XGBoost and Random Forest® with Bayesian Optimisation

  This article will explain how to use XGBoost and Random Forest with Bayesian Optimisation, and will discuss the main pros and cons of these methods.

  https://www.kdnuggets.com/2019/07/xgboost-random-forest-bayesian-optimisation.html

• Understanding Cloud Data Services">Understanding Cloud Data Services

  Ready to move your systems to a cloud vendor or just learning more about big data services? This overview will help you understand big data system architectures, components, and offerings with an end-to-end taxonomy of what is available from the big three cloud providers.

  https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

• Overview of Different Approaches to Deploying Machine Learning Models in Production

  Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.html

• How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls">How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls

  We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.

  https://www.kdnuggets.com/2019/05/machine-learning-time-series-forecasting.html

• 5 Things to Review Before Accepting That Data Scientist Job Offer

  Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.

  https://www.kdnuggets.com/2019/05/5-things-review-before-accepting-data-scientist-job-offer.html

• [White Paper] Unlocking the Power of Data Science & Machine Learning with Python

  This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.

  https://www.kdnuggets.com/2019/05/activestate-whitepaper-data-science-machine-learning-python.html

• 7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them">7 Qualities Your Big Data Visualization Tools Absolutely Must Have and 10 Tools That Have Them

  Without the right visualization tools, raw data is of little use. Data visualization helps present the data in an interactive visual format. Here are the qualities to look for in a data visualization tool.

  https://www.kdnuggets.com/2019/04/7-qualities-big-data-visualization-tools.html

• The Difference Between Data Scientists and Data Engineers

  ODSC East 2019 has multiple tracks for both Data Scientists and Data Engineers, including workshops, talks, and training sessions. Save 45% with code KDN45.

  https://www.kdnuggets.com/2019/03/odsc-difference-data-scientists-data-engineers.html

• Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms">Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms

  We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.

  https://www.kdnuggets.com/2019/02/gartner-2019-mq-data-science-machine-learning-changes.html

• Understanding Gradient Boosting Machines">Understanding Gradient Boosting Machines

  However despite its massive popularity, many professionals still use this algorithm as a black box. As such, the purpose of this article is to lay an intuitive framework for this powerful machine learning technique.

  https://www.kdnuggets.com/2019/02/understanding-gradient-boosting-machines.html

• The Essential Data Science Venn Diagram">The Essential Data Science Venn Diagram

  A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.

  https://www.kdnuggets.com/2019/02/essential-data-science-venn-diagram.html

• ELMo: Contextual Language Embedding

  Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.

  https://www.kdnuggets.com/2019/01/elmo-contextual-language-embedding.html

• Practical Apache Spark in 10 Minutes

  Check out this series of articles on Apache Spark. Each part is a 10 minute tutorial on a particular Apache Spark topic. Read on to get up to speed using Spark.

  https://www.kdnuggets.com/2019/01/practical-apache-spark-10-minutes.html

• The Role of the Data Engineer is Changing

  The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?

  https://www.kdnuggets.com/2019/01/role-data-engineer-changing.html

• Common mistakes when carrying out machine learning and data science">Common mistakes when carrying out machine learning and data science

  We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

  https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html

• Interpretability is crucial for trusting AI and machine learning

  We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.

  https://www.kdnuggets.com/2018/11/interpretability-trust-ai-machine-learning.html

• The Big Data Game Board™">The Big Data Game Board™

  Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  https://www.kdnuggets.com/2018/11/big-data-game-board.html

• Best Practices for Using Notebooks for Data Science

  Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.

  https://www.kdnuggets.com/2018/11/best-practices-notebooks-data-science.html

• Evaluating the Business Value of Predictive Models in Python and R

  In these blogs for R and python we explain four valuable evaluation plots to assess the business value of a predictive model. We show how you can easily create these plots and help you to explain your predictive model to non-techies.

  https://www.kdnuggets.com/2018/10/evaluating-business-value-predictive-models-modelplotpy.html

• Big Data a $4.7 Billion opportunity in the healthcare and pharmaceutical industry

  This post contains some of the key findings from the SNS Telecom & IT's latest report, which indicates that Big Data investments in the healthcare and pharmaceutical industry are expected to reach nearly $4.7 Billion by the end of 2018.

  https://www.kdnuggets.com/2018/07/snstelecom-big-data-healthcare-pharm.html

• The Executive Guide to Data Science and Machine Learning

  This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.

  https://www.kdnuggets.com/2018/05/executive-guide-data-science-machine-learning.html

• Time Series for Dummies – The 3 Step Process">Time Series for Dummies – The 3 Step Process

  Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.

  https://www.kdnuggets.com/2018/03/time-series-dummies-3-step-process.html

• Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms">Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms

  We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.

  https://www.kdnuggets.com/2018/02/gartner-2018-mq-data-science-machine-learning-changes.html

• Deep Feature Synthesis: How Automated Feature Engineering Works

  Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.

  https://www.kdnuggets.com/2018/02/deep-feature-synthesis-automated-feature-engineering.html

• Generalists Dominate Data Science

  An interesting insight into why small teams generalists outperform large teams of specialists.

  https://www.kdnuggets.com/2018/02/generalists-dominate-data-science.html

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018">Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018

  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  https://www.kdnuggets.com/2017/12/data-science-machine-learning-main-developments-trends.html

• 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

• Big Data: Main Developments in 2017 and Key Trends in 2018">Big Data: Main Developments in 2017 and Key Trends in 2018

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  https://www.kdnuggets.com/2017/12/big-data-main-developments-2017-key-trends-2018.html

• Using Deep Learning to Solve Real World Problems">Using Deep Learning to Solve Real World Problems

  Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.

  https://www.kdnuggets.com/2017/12/using-deep-learning-solve-real-world-problems.html

• How Bayesian Networks Are Superior in Understanding Effects of Variables

  Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.

  https://www.kdnuggets.com/2017/11/bayesian-networks-understanding-effects-variables.html

• Process Mining with R: Introduction

  In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.

  https://www.kdnuggets.com/2017/11/process-mining-r-introduction.html

• Understanding Machine Learning Algorithms">Understanding Machine Learning Algorithms

  Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.

  https://www.kdnuggets.com/2017/10/understanding-machine-learning-algorithms.html

• New-Age Machine Learning Algorithms in Retail Lending">New-Age Machine Learning Algorithms in Retail Lending

  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  https://www.kdnuggets.com/2017/09/machine-learning-algorithms-lending.html

• Are Data Lakes Fake News?">Are Data Lakes Fake News?

  The quick answer is yes, and the biggest problem is that the term “Data Lakes” has been overloaded by vendors and analysts with different meanings, resulting in an ill-defined and blurry concept.

  https://www.kdnuggets.com/2017/09/data-lakes-fake-news.html

• Data science platforms are on the rise and IBM is leading the way

  Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.

  https://www.kdnuggets.com/2017/05/ibm-data-science-platforms-gartner.html

• Difference Between Big Data and Internet of Things

  If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion. So what is required to support these IOT data management and analytic requirements?

  https://www.kdnuggets.com/2017/04/difference-big-data-internet-of-things.html

• What is Structural Equation Modeling?">What is Structural Equation Modeling?

  Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?

  https://www.kdnuggets.com/2017/03/structural-equation-modeling.html

• The Most Underutilized Function in SQL

  Find out why md5() is an SQL function that's used surprisingly often, and find out how -- and why -- you can use it yourself.

  https://www.kdnuggets.com/2017/03/most-underutilized-function-sql.html

• Gartner Data Science Platforms – A Deeper Look

  Thomas Dinsmore critical examination of Gartner 2017 MQ of Data Science Platforms, including vendors who out, in, have big changes, Hadoop and Spark integration, open source software, and what Data Scientists actually use.

  https://www.kdnuggets.com/2017/03/thomaswdinsmore-gartner-data-science-platforms.html

• Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers">Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers

  We compare Gartner 2017 Magic Quadrant for Data Science Platforms vs its 2016 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, MathWorks, Microsoft, and Quest.

  https://www.kdnuggets.com/2017/02/gartner-2017-mq-data-science-platforms-gainers-losers.html

• Four Problems in Using CRISP-DM and How To Fix Them

  CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key elements of the approach.

  https://www.kdnuggets.com/2017/01/four-problems-crisp-dm-fix.html

• Top 2016 KDnuggets Stories: Must-Know Data Science Interview Q&A, 10 Algorithms Machine Learning Engineers Need to Know

  Also 20 Questions to Detect Fake Data Scientists; Software used for Analytics, Data Science, Machine Learning projects; Top Algorithms and Methods Used by Data Scientists

  https://www.kdnuggets.com/2016/12/top-2016-kdnuggets-stories.html

• How Can Lean Six Sigma Help Machine Learning?

  The data cleansing phase alone is not sufficient to ensure the accuracy of the machine learning, when noise / bias exists in input data. The lean six sigma variance reduction can improve the accuracy of machine learning results.

  https://www.kdnuggets.com/2016/11/lean-sigma-six-help-machine-learning.html

• Top 16 Active Big Data, Data Science Leaders on LinkedIn

  Who are the most active Big Data, Data Science Influencers and Leaders on LinkedIn? We analyze the data and bring you the list of key people to follow.

  https://www.kdnuggets.com/2016/09/top-big-data-science-leaders-linkedin.html

• How to Become a (Type A) Data Scientist">How to Become a (Type A) Data Scientist

  This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A.

  https://www.kdnuggets.com/2016/08/become-type-a-data-scientist.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

• Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists

  Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.

  https://www.kdnuggets.com/2016/07/interview-florian-douetteau-dataiku-empowering-data-scientists.html

• Data Science of Variable Selection: A Review

  There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.

  https://www.kdnuggets.com/2016/06/data-science-variable-selection-review.html

• Data Scientist Survey: What Is An Interesting Result?

  A survey requesting feedback from data scientists on their opinion of what an interesting result is. The survey is anonymous, has only a single mandatory question, and takes only 5 minutes.

  https://www.kdnuggets.com/2016/04/irisa-data-scientist-survey-interesting-result.html

• Deriving Better Insights from Time Series Data with Cycle Plots

  Visualization plays key role in analysis of time series data, to understand underlying trends. Here we are demonstrating the cycle plot which shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect.

  https://www.kdnuggets.com/2016/03/better-insights-time-series-cycle-plots.html

• Data Lake Plumbers: Operationalizing the Data Lake

  Gain insight into data lakes, their benefits, when they are appropriate, and how to operationalize them. How do they compare to the data warehouse?

  https://www.kdnuggets.com/2016/02/data-lakes-plumbers-operationalizing.html

• Research Leaders on Data Mining, Data Science and Big Data key advances, top trends

  Research Leaders in Data Science and Big Data reflect on the most important research advances in 2015 and the key trends expected to dominate throughout 2016.

  https://www.kdnuggets.com/2016/01/research-leaders-data-science-big-data-top-trends.html

• Understanding Rare Events and Anomalies: Why streaks patterns change

  We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!

  https://www.kdnuggets.com/2016/01/understanding-rare-events-anomalies-patterns-change.html

• Big Data and Data Science for Security and Fraud Detection

  We review big data analytics tools and technologies that combine text mining, machine learning and network analysis for security threat prediction, detection and prevention at an early stage.

  https://www.kdnuggets.com/2015/12/big-data-science-security-fraud-detection.html

• Anomaly Detection in Predictive Maintenance with Time Series Analysis

  How can we predict something we have never seen, an event that is not in the historical data? This requires a shift in the analytics perspective! Understand how to standardization the time and perform time series analysis on sensory data.

  https://www.kdnuggets.com/2015/12/anomaly-detection-predictive-maintenance-time-series-analysis.html

• How Data Science increased the profitability of the e-commerce industry?

  Data Science helps businesses provide a richer understanding of the customers by capturing and integrating the information on customers web behaviour, their life events, what led to the purchase of a product or service, how customers interact with different channels, and more.

  https://www.kdnuggets.com/2015/11/how-data-science-increased-profitability-e-commerce-industry.html

• Amazon Top 20 Books in Data Mining

  These are the most popular data mining books on Amazon. As you look to increase your knowledge, is there something listed here that is missing from your collection?

  https://www.kdnuggets.com/2015/10/amazon-top-20-books-data-mining.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

• Gartner 2015 Hype Cycle: Big Data is Out, Machine Learning is in

  Which are the most hyped technologies today? Check out Gartner's latest 2015 Hype Cycle Report. Autonomous cars & IoT stay at the peak while big data is losing its prominence. Smart Dust is a new cool technology for the next decade!

  https://www.kdnuggets.com/2015/08/gartner-2015-hype-cycle-big-data-is-out-machine-learning-is-in.html

• Top stories for Jun 28 – Jul 4: Top 20 R packages by popularity; Nine Laws of Data Mining

  Top 20 R packages by popularity; Top 20 R Machine Learning and Data Science packages; Nine Laws of Data Mining; The missing D in Data Science.

  https://www.kdnuggets.com/2015/07/top-news-week-jun-28.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

• Data Science for Workforce Optimization: Reducing Employee Attrition

  Predictive analytics is growing its reach, see how it is affecting workforce analytics domain. In this presentation Pasha Roberts explains what is in it for students, managers and practitioners.

  https://www.kdnuggets.com/2015/05/data-science-workforce-optimization-reducing-employee-attrition.html

• Seven Techniques for Data Dimensionality Reduction

  Performing data mining with high dimensional data sets. Comparative study of different feature selection techniques like Missing Values Ratio, Low Variance Filter, PCA, Random Forests / Ensemble Trees etc.

  https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html

• Top SlideShare Presentations on Big Data, updated

  REST APIs and crawling offer two different ways to gather big data presentations from SlideShare, but they provide different results and lead to a very different view of the data. We examine why and find a useful data science lesson.

  https://www.kdnuggets.com/2015/01/top-slideshare-presentations-big-data-updated.html

• KDnuggets™ News 14:n34, Dec 17

  Features | Software | Opinions | Interviews | Reports | News | Webcasts | Jobs | Academic | Tweets | CFP | Quote Features New Read more »

  https://www.kdnuggets.com/2014/n34.html

• Interview: Daqing Zhao, Macys.com on Building Effective Data Models for Marketing

  We discuss the challenges in identifying the fair price of ad media, recommendations for building effective models for online marketing, unique challenges of Mobile channel, selection of Big Data tools, and more.

  https://www.kdnuggets.com/2014/12/interview-daqing-zhao-macys-data-models-marketing.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

• TweetNLP: Twitter Natural Language Processing

  A short overview of Natural Language Processing tools and utilities developed by Prof. Noah Smith, CMU and his team to analyze Twitter data.

  https://www.kdnuggets.com/2014/10/tweetnlp-twitter-natural-language-processing.html

• KDnuggets™ News 14:n25, Sep 24

  Features | Software | News | Opinions | Webcasts | Courses | Jobs | Academic | Publications | Tweets | CFP | Quote Features Data Read more »

  https://www.kdnuggets.com/2014/n25.html

• KDnuggets™ News 14:n24, Sep 18

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

  https://www.kdnuggets.com/2014/n24.html

• Interview: Michael Berthold, President and Founder of KNIME, on Data Mining, Startups, and Visual Workflow

  We discuss KNIME key features and how it compares to competition, KNIME business model, Pharma, planned development, and transition from an academic project to a company.

  https://www.kdnuggets.com/2014/08/interview-michael-berthold-knime-data-mining-startup-visual-workflow-part1.html

• KDnuggets™ News 14:n19, Jul 30

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

  https://www.kdnuggets.com/2014/n19.html

• XLMiner solves Big Data Problems in Excel

  XLMiner, a part of Analytic Solver Platform integrated software for predictive and prescriptive analytics - forecasting, data mining, optimization and simulation, lets you solve small or Big Data problems in Excel.

  https://www.kdnuggets.com/2014/06/xlminer-solve-big-data-problems-excel.html

• KDnuggets™ News 14:n17, Jul 2

  Features (4) | Software (2) | Opinions (7) | News (6) | Webcasts (1) | Courses (1) | Meetings and Reports (2) | Jobs (4) Read more »

  https://www.kdnuggets.com/2014/n17.html

• KDnuggets™ News 14:n16, Jun 25

  Features (5) | Software (1) | Opinions (2) | News (2) | Webcasts (1) | Courses (1) | Meetings and Reports (6) | Jobs (7) Read more »

  https://www.kdnuggets.com/2014/n16.html

• KDnuggets™ News 14:n15, Jun 18

  Features (6) | Software (3) | Opinions (6) | News (2) | Webcasts (1) | Courses (1) | Jobs (7) | Academic (1) | Publications Read more »

  https://www.kdnuggets.com/2014/n15.html

• KDnuggets™ News 14:n13, May 28

  Features (5) | Software (3) | Opinions (5) | News (1) | Webcasts (1) | Courses (1) | Meetings and Reports (3) | Jobs (5) Read more »

  https://www.kdnuggets.com/2014/n13.html

• Predict Soccer World Cup 2014 Winner, Get Prizes from RapidMiner

  Use a free edition of RapidMiner to have fun and bring sports predictions to another level by making a prediction of Soccer (Futbol) World Cup 2014, which starts on June 12 in Brazil.

  https://www.kdnuggets.com/2014/05/predict-world-cup-2014-winner-rapidminer-contest.html

• KDnuggets™ News 14:n12, May 21

  Features (11) | Software (4) | Opinions (9) | News (7) | Webcasts (1) | Courses (3) | Meetings and Reports (10) | Jobs (14) Read more »

  https://www.kdnuggets.com/2014/n12.html

• KDnuggets™ News 14:n07, Mar 26

  Features (3) | Opinions (3) | Software (2) | News (3) | Webcasts (2) | Courses (1) | Meetings (3) | Jobs (5) | Academic Read more »

  https://www.kdnuggets.com/2014/n07.html

• KDnuggets™ News 14:n05, Mar 5

  Features (10) | News (6) | Software (5) | Webcasts (1) | Courses (6) | Meetings (5) | Jobs (7) | Academic (1) | Publications Read more »

  https://www.kdnuggets.com/2014/n05.html

• KDnuggets™ News 14:n04, Feb 19

  Features (7) | News (10) | Software (2) | Webcasts (2) | Courses (2) | Meetings (2) | Jobs (10) | Academic (4) | Publications Read more »

  https://www.kdnuggets.com/2014/n04.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

• KDnuggets™ News 14:n02, Jan 22

  Features (10) | Software (3) | Webcasts (2) | Courses, Events (5) | Meetings (3) | Jobs (11) | Academic (3) | Competitions (1) | Publications Read more »

  https://www.kdnuggets.com/2014/n02.html

• KDnuggets™ News 13:n31, Dec 19

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

  https://www.kdnuggets.com/2013/n31.html

• Fraud Detection Solutions

  AccessPay fraud detection software alerts businesses to fraudulent activity before payments are sent. Alaric Systems "Fractals" card fraud detection and prevention systems using proprietary inference Read more »

  https://www.kdnuggets.com/solutions/fraud-detection.html

• KDnuggets™ News 13:n29, Nov 26

  Features (6) | Software (1) | Webcasts (2) | Meetings (2) | Jobs (7) | Academic (1) | Publications (3) | Tweets (3) | CFP Read more »

  https://www.kdnuggets.com/2013/n29.html

• KDnuggets™ News 13:n24, Oct 8

  Features (10) | Software (3) | Webcasts (4) | Courses, Events (4) | Meetings (2) | Jobs (7) | Academic (4) | Competitions (1) | Publications Read more »

  https://www.kdnuggets.com/2013/n24.html

• KDnuggets™ News 13:n21, Aug 28

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

  https://www.kdnuggets.com/2013/n21.html

• KDnuggets™ News 13:n18, Jul 31

  Features (8) | Software (4) | Webcasts (2) | Courses, Events (5) | Meetings (1) | Jobs (14) | Academic (2) | Competitions (2) | Publications Read more »

  https://www.kdnuggets.com/2013/n18.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

• KDnuggets™ News 13:n15, Jun 19

  Features (8) | Software (5) | Webcasts (3) | Courses, Events (3) | Meetings (1) | Jobs (8) | Academic (2) | Competitions (3) | Publications Read more »

  https://www.kdnuggets.com/2013/n15.html

• Text Analysis, Text Mining, and Information Retrieval Software

  http likes 47 Commercial | online | free On-line Text Mining / Text Analytics Tools Ranks.nl, keyword analysis and webmaster tools. Text Sentiment Visualizer (online), Read more »

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

• KDnuggets™ News 13:n07, Mar 14

  Features (7) | Software (5) | Webcasts (3) | Courses, Events (2) | Meetings (1) | Jobs (4) | Academic (1) | Publications (5) | Tweets Read more »

  https://www.kdnuggets.com/2013/n07.html

• KDnuggets™ News 13:n06, Mar 7

  Features (7) | Software (1) | Webcasts (2) | Courses, Events (2) | Meetings (2) | Jobs (5) | Academic (2) | Competitions (3) | Publications Read more »

  https://www.kdnuggets.com/2013/n06.html

• KDnuggets™ News 13:n01, Jan 15

  Features (11) | Software (2) | Courses, Events (2) | Webcasts (3) | Jobs (11) | Competitions (3) | Publications (10) | NewsBriefs (4) | CFP Read more »

  https://www.kdnuggets.com/2013/n01.html

• Ultimate Collection of 50 Free Courses for Mastering Data Science

  The collection includes free courses on Python, SQL, Data Analytics, Business Intelligence, Data Engineering, Machine Learning, Deep Learning, Generative AI, and MLOps.

  https://www.kdnuggets.com/ultimate-collection-of-50-free-courses-for-mastering-data-science

• 7 Steps to Mastering Data Engineering

  The only data engineering roadmap you need for an introduction to concepts, tools, and techniques to collect, store, transform, analyze, and model data.

  https://www.kdnuggets.com/7-steps-to-mastering-data-engineering

• Exploring the OpenAI API with Python

  Let’s learn all the useful services from the OpenAI.

  https://www.kdnuggets.com/exploring-the-openai-api-with-python

• A Collection Of Free Data Science Courses From Harvard, Stanford, MIT, Cornell, and Berkeley

  Learn everything about data science by exploring our curated collection of free courses from top universities, covering essential topics from math and programming to machine learning, and mastering the nine steps to become a job-ready data scientist.

  https://www.kdnuggets.com/a-collection-of-free-data-science-courses-from-harvard-stanford-mit-cornell-and-berkeley