Search results for How to Invest

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• 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

• The Top 5 KPIs to Consider When Measuring Your Campaign

  When it comes to measuring marketing campaign performance or analysing customers in any business, below top 5 Key Performance Indicators (KPIs) needs to be used to strategically drive the business.

  https://www.kdnuggets.com/2017/02/top-5-kpis-measuring-campaign.html

• 17 More Must-Know Data Science Interview Questions and Answers, Part 2

  The second part of 17 new must-know Data Science Interview questions and answers covers overfitting, ensemble methods, feature selection, ground truth in unsupervised learning, the curse of dimensionality, and parallel algorithms.

  https://www.kdnuggets.com/2017/02/17-data-science-interview-questions-answers-part-2.html

• The Origins of Big Data

  Big Data has truly come of age in 2013 when OED introduced the term “Big Data” for the first time. But when was the term Big Data first used and Why? Here are the results of our investigation.

  https://www.kdnuggets.com/2017/02/origins-big-data.html

• Introduction to Natural Language Processing, Part 1: Lexical Units

  This series explores core concepts of natural language processing, starting with an introduction to the field and explaining how to identify lexical units as a part of data preprocessing.

  https://www.kdnuggets.com/2017/02/datascience-introduction-natural-language-processing-part1.html

• Web Scraping for Dataset Curation, Part 1: Collecting Craft Beer Data

  This post is the first in a 2 part series on scraping and cleaning data from the web using Python. This first part is concerned with the scraping aspect, while the second part while focus on the cleaning. A concrete example is presented.

  https://www.kdnuggets.com/2017/02/web-scraping-dataset-curation-part-1.html

• Getting Real World Results From Agile Data Science Teams

  In this post, I’ll look at the practical ingredients of managing agile data science. By using agile data science methods, we help data teams do fast and directed work, and manage the inherent uncertainty of data science and application development.

  https://www.kdnuggets.com/2017/02/real-world-results-agile-data-science-teams.html

• Bad Data + Good Models = Bad Results

  No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
  is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.

  https://www.kdnuggets.com/2017/01/bad-data-good-models-bad-results.html

• Artificial Intelligence and Speech Recognition for Chatbots: A Primer

  Bot bots bots... Read this overview of how artificial intelligence and natural language processing are contributing to chatbot development, and where it all goes from here.

  https://www.kdnuggets.com/2017/01/artificial-intelligence-speech-recognition-chatbots-primer.html

• 6 areas of AI and Machine Learning to watch closely">6 areas of AI and Machine Learning to watch closely

  Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

  https://www.kdnuggets.com/2017/01/6-areas-ai-machine-learning.html

• Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms

  Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.

  https://www.kdnuggets.com/2017/01/great-collection-clean-machine-learning-algorithms.html

• Data Science of Sales Calls: 3 Actionable Findings

  How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.

  https://www.kdnuggets.com/2017/01/data-science-sales-calls-actionable-findings.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

• Time Series Analysis: A Primer">Time Series Analysis: A Primer

  Time series analysis is a complex subject but, in short, when we use our usual cross-sectional techniques such as regression on time series data, variables can appear "more significant" than they really are and we are not taking advantage of the information the serial correlation in the data provides.

  https://www.kdnuggets.com/2017/01/time-series-analysis-primer.html

• The Most Popular Language For Machine Learning and Data Science Is …">The Most Popular Language For Machine Learning and Data Science Is …

  When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.

  https://www.kdnuggets.com/2017/01/most-popular-language-machine-learning-data-science.html

• Social Media for Marketing and Healthcare: Focus on Adverse Side Effects

  Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.

  https://www.kdnuggets.com/2017/01/social-media-marketing-healthcare-focus-adverse-side-effects.html

• A Tasty approach to data science

  Data scientists at Foodpairing help brands cut down on the fuzzy front end of product development. The so-called Consumer Flavor Intelligence combines internet data and food science to create timely flavor line extensions.

  https://www.kdnuggets.com/2017/01/foodpairing-tasty-approach-data-science.html

• Laying the Foundation for a Data Team

  Admittedly, there is a lot more to building a successful data team, and we would be lying if we pretended we have it all figured out. But hopefully focusing on the elements in this post is a good start.

  https://www.kdnuggets.com/2016/12/laying-foundation-data-team.html

• The big data ecosystem for science: Climate Science and Climate Change

  Climate change is one of the most pressing challenges for human society in the 21st century. We review the Big Data ecosystem for studying the climate change.

  https://www.kdnuggets.com/2016/12/big-data-ecosystem-science-climate-change.html

• Privacy, Security and Ethics in Process Mining

  Data Privacy, Security and Ethics are hot yet complex topics in the business and data science world. This important article talks about and provide guidelines for privacy, security and ethics, specifically in the context of Process Mining.

  https://www.kdnuggets.com/2016/12/privacy-security-ethics-process-mining.html

• Data Science Basics: Power Laws and Distributions

  Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.

  https://www.kdnuggets.com/2016/12/data-science-basics-power-laws-distributions.html

• Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017">Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017

  As 2016 comes to a close and we prepare for a new year, check out the final instalment in our "Main Developments in 2016 and Key Trends in 2017" series, where experts weigh in with their opinions.

  https://www.kdnuggets.com/2016/12/machine-learning-artificial-intelligence-main-developments-2016-key-trends-2017.html

• Artificial Intelligence and Life in 2030

  Read this engaging overview of a report from the Stanford University 100 year study of Artificial Intelligence, “a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society.”

  https://www.kdnuggets.com/2016/12/artificial-intelligence-life-2030.html

• Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017">Data Science, Predictive Analytics Main Developments in 2016 and Key Trends for 2017

  Key themes included the polling failures in 2016 US Elections, Deep Learning, IoT, greater focus on value and ROI, and increasing adoption of predictive analytics by the "masses" of industry.
   
  

  https://www.kdnuggets.com/2016/12/data-science-predictive-analytics-main-developments-trends.html

• Data Analytics Models in Quantitative Finance and Risk Management

  We review how key data science algorithms, such as regression, feature selection, and Monte Carlo, are used in financial instrument pricing and risk management.

  https://www.kdnuggets.com/2016/12/data-analytics-models-quantitative-finance-risk-management.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

• Machine Learning vs Statistics">Machine Learning vs Statistics

  Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?

  https://www.kdnuggets.com/2016/11/machine-learning-vs-statistics.html

• Data Avengers… Assemble!

  The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.

  https://www.kdnuggets.com/2016/11/data-avengers-assemble.html

• Deep Learning Reading Group: Skip-Thought Vectors

  Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.

  https://www.kdnuggets.com/2016/11/deep-learning-group-skip-thought-vectors.html

• Practical Data Science: Building Minimum Viable Models

  Data Science for startups based on data: Minimum Valuable Model, a new concept to avoid a full scale 95% accurate data science model. Want to know more about MVM? Have a look at this interesting article.

  https://www.kdnuggets.com/2016/11/practical-data-science-building-minimum-viable-models.html

• An NLP Approach to Analyzing Twitter, Trump, and Profanity

  Who swears more? Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let’s find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets.

  https://www.kdnuggets.com/2016/11/nlp-approach-analyzing-twitter-trump-profanity.html

• Artificial Intelligence Classification Matrix

  There might be several different ways to think around machine intelligence startups; too narrow of a framework might be counterproductive given the flexibility of the sector and the facility of transitioning from one group to another. Check out this categorization matrix.

  https://www.kdnuggets.com/2016/11/artificial-intelligence-classification-matrix.html

• Do You Suffer From Analytic Personality Disorder (APD)?

  Read this lighthearted take on Analytics Personality Disorder, a (nonexistent) syndrome for those obsessed with analytics.

  https://www.kdnuggets.com/2016/11/analytic-personality-disorder.html

• Deep Learning Reading Group: Deep Residual Learning for Image Recognition

  Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.

  https://www.kdnuggets.com/2016/09/deep-learning-reading-group-deep-residual-learning-image-recognition.html

• Decision Trees: A Disastrous Tutorial

  Get a concise overview of decision trees here, one of the most used KDnuggets reader algorithms as measured in a recent poll.

  https://www.kdnuggets.com/2016/09/decision-trees-disastrous-overview.html

• Deep Learning Reading Group: Deep Networks with Stochastic Depth

  An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.

  https://www.kdnuggets.com/2016/09/deep-learning-reading-group-stochastic-depth-networks.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

• The Evolution of IoT Edge Analytics: Strategies of Leading Players

  This article explores the significance and evolution of IoT edge analytics. Since the author believes that hardware capabilities will converge for large vendors, IoT analytics will be the key differentiator.

  https://www.kdnuggets.com/2016/09/evolution-iot-edge-analytics.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

• Big Data Key Terms, Explained

  Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.

  https://www.kdnuggets.com/2016/08/big-data-key-terms-explained.html

• Build vs Buy – Analytics Dashboards

  Read this post on choosing between available analytics dashboard options, and designing your own. Get an informed opinion.

  https://www.kdnuggets.com/2016/07/build-buy-analytics-dashboards.html

• 7 Steps to Understanding NoSQL Databases

  Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.

  https://www.kdnuggets.com/2016/07/seven-steps-understanding-nosql-databases.html

• In Deep Learning, Architecture Engineering is the New Feature Engineering

  A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.

  https://www.kdnuggets.com/2016/07/deep-learning-architecture-engineering-feature-engineering.html

• Why Big Data is in Trouble: They Forgot About Applied Statistics">Why Big Data is in Trouble: They Forgot About Applied Statistics

  This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago.

  https://www.kdnuggets.com/2016/07/big-data-trouble-forgot-applied-statistics.html

• Predictive Analytics Introductory Key Terms, Explained

  Here is a collection of introductory predictive analytics terms and concepts, presented for the newcomer in a straight-forward, no frills definition style.

  https://www.kdnuggets.com/2016/07/siegel-predictive-analytics-key-terms-explained.html

• Bayesian Machine Learning, Explained">Bayesian Machine Learning, Explained

  Want to know about Bayesian machine learning? Sure you do! Get a great introductory explanation here, as well as suggestions where to go for further study.
   
   

  https://www.kdnuggets.com/2016/07/bayesian-machine-learning-explained.html

• Big Data, Bible Codes, and Bonferroni

  This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.

  https://www.kdnuggets.com/2016/07/big-data-bible-codes-bonferroni.html

• The Big Data Ecosystem is Too Damn Big">The Big Data Ecosystem is Too Damn Big

  The Big Data ecosystem is just too damn big! It's complex, redundant, and confusing. There are too many layers in the technology stack, too many standards, and too many engines. Vendors? Too many. What is the user to do?

  https://www.kdnuggets.com/2016/06/big-data-ecosystem-too-damn-big.html

• Ten Simple Rules for Effective Statistical Practice: An Overview

  An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.

  https://www.kdnuggets.com/2016/06/ten-simple-rules-effective-statistical-practice-overview.html

• Machine Learning Trends and the Future of Artificial Intelligence

  The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.

  https://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html

• What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data

  The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.

  https://www.kdnuggets.com/2016/06/walker-linkedin-microsoft-value-user-data.html

• Thinking About Analytics Readiness

  This article touches upon an important but under-discussed topic of analytics readiness, including whether and when organizations should engage in analytics.

  https://www.kdnuggets.com/2016/06/thinking-domain-readiness.html

• 10 Data Acquisition Strategies for Startups

  An interesting discussion of the myriad methods in which startups may choose to acquire data, often the most overlooked and important aspect of a startup's success (or failure).

  https://www.kdnuggets.com/2016/06/10-data-acquisition-strategies-startups.html

• Cloud Computing Key Terms, Explained

  A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.

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

• 5 Best Practices for Big Data Security

  Lack of data security can not only result in financial losses, but may also damage the reputation of organizations. Take a look at some of the most important data security best practices that can reduce the risks associated with analyzing a massive amount of data.

  https://www.kdnuggets.com/2016/06/5-best-practices-big-data-security.html

• Where are the Opportunities for Machine Learning Startups?

  Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.

  https://www.kdnuggets.com/2016/06/opportunites-machine-learning-startups.html

• Top 10 Open Dataset Resources on Github

  The top open dataset repositories on Github include a variety of data, freely available for use by researchers, practitioners, and students alike.

  https://www.kdnuggets.com/2016/05/top-10-datasets-github.html

• Predicting Popularity of Online Content

  A look at predicting what makes online content popular, with a particular focus on images, especially selfies.

  https://www.kdnuggets.com/2016/05/predicting-popularity-online-content.html

• Free eBook: Healthcare Social Media Analytics and Marketing

  Get your free copy of a new ebook outlining social media marketing and analytics strategies (including code) for healthcare professionals.

  https://www.kdnuggets.com/2016/05/healthcare-social-media-analytics-marketing-ebook.html

• 5 Ways in Which Big Data Can Help Leverage Customer Data

  Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.

  https://www.kdnuggets.com/2016/05/5-ways-big-data-leverage-customer-data.html

• 5 Machine Learning Projects You Can No Longer Overlook

  We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.

  https://www.kdnuggets.com/2016/05/five-machine-learning-projects-cant-overlook.html

• Practical skills that practical data scientists need

  The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.

  https://www.kdnuggets.com/2016/05/practical-skills-practical-data-scientists-need.html

• Meet the 11 Big Data & Data Science Leaders on LinkedIn

  In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!

  https://www.kdnuggets.com/2016/05/10-big-data-data-science-leaders-linkedin.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

• Microsoft is Becoming M(ai)crosoft

  This post is an overview and discussion of Microsoft's increasing investment in, and approach to, artificial intelligence, and how their philosophy differs from their competitors.

  https://www.kdnuggets.com/2016/04/microsoft-becoming-m-ai-crosoft.html

• Recommender Systems: New Comprehensive Textbook by Charu Aggarwal

  Covers recommender systems comprehensively, both fundamentals and advanced topics, organized into: Algorithms and evaluation, recommendations in specific domains and contexts, and advanced topics and applications.

  https://www.kdnuggets.com/2016/04/recommender-systems-textbook.html

• What Developers Actually Need to Know About Machine Learning

  Some guidance on what, exactly, it is that developers need to know to get up to speed with machine learning.

  https://www.kdnuggets.com/2016/04/developers-need-know-about-machine-learning.html

• 10 Signs Of A Bad Data Scientist

  With the number of people claiming to be a data scientist growing, the “true” data scientists are becoming hard to find. Here your guide identify the clues to catch a bad data scientists.

  https://www.kdnuggets.com/2016/04/10-signs-bad-data-scientist.html

• Don’t Buy Machine Learning

  In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a future phase after building the application first.

  https://www.kdnuggets.com/2016/03/dont-buy-machine-learning.html

• How to combat financial fraud by using big data?

  Financial fraud methods are becoming more sophisticated and the techniques to combat such attacks also need to evolve. Big data has brought with it novel fraud detection and prevention techniques such as behavioral analysis and real-time detection to give fraud fighting techniques a new perspective.

  https://www.kdnuggets.com/2016/03/combat-financial-fraud-using-big-data.html

• R Learning Path: From beginner to expert in R in 7 steps

  This learning path is mainly for novice R users that are just getting started but it will also cover some of the latest changes in the language that might appeal to more advanced R users.

  https://www.kdnuggets.com/2016/03/datacamp-r-learning-path-7-steps.html

• Netflix Prize Analyzed: Movie Ratings and Recommender Systems

  A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems.

  https://www.kdnuggets.com/2016/03/netflix-prize-analyzed-movie-ratings-recommender-systems.html

• The Data Science Puzzle, Explained

  The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.

  https://www.kdnuggets.com/2016/03/data-science-puzzle-explained.html

• The Data Science Process, Rediscovered

  The Data Science Process is a relatively new framework for doing data science. It is compared to previous similar frameworks, and a discussion on process innovation versus repetition is then undertaken.

  https://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html

• The Data Science Process

  What does a day in the data science life look like? Here is a very helpful framework that is both a way to understand what data scientists do, and a cheat sheet to break down any data science problem.

  https://www.kdnuggets.com/2016/03/data-science-process.html

• A comparison between PCA and hierarchical clustering

  Graphical representations of high-dimensional data sets are the backbone of exploratory data analysis. We examine 2 of the most commonly used methods: heatmaps combined with hierarchical clustering and principal component analysis (PCA).

  https://www.kdnuggets.com/2016/02/qlucore-comparison-pca-hierarchical-clustering.html

• How Small is the World, Really?

  Social network analysis is back in the news again, with a recent Facebook project which determined that there are an average of 3.5 intermediaries between any 2 Facebook users. But this is different than "6 degrees of separation." Read on to find out why, and how.

  https://www.kdnuggets.com/2016/02/how-small-is-world-really.html

• How Data Science is Fighting Disease

  Many organisations are starting to use Data Science as a method of tracking, diagnosing and curing some of the world’s most widespread diseases. We look at 3 common diseases, and how Data Science is used to save lives.

  https://www.kdnuggets.com/2016/02/how-data-science-fighting-disease.html

• Big Data Is Driving Your Car

  Never mind driverless cars! Big Data is already hard at work in every aspect of the automotive industry, including safety, design, marketing and more. We look at where Big Data is having an impact on the cars that we are driving.

  https://www.kdnuggets.com/2016/02/big-data-driving-your-car.html

• How IBM Watson is Taking on The World

  We have made tremendous progress in the field of data analysis and on the other, our technology is getting smart. IBM has taken a solid stride in the direction of Artificial Intelligence by unveiling its supercomputer IBM Watson, learn what it can do, its adopters and what it holds for the future.

  https://www.kdnuggets.com/2016/02/dezyre-ibm-watson-taking-world.html

• Amazon Machine Learning: Nice and Easy or Overly Simple?

  Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service is fast, offers a simple workflow but lacks model selection features and has slow execution times.

  https://www.kdnuggets.com/2016/02/amazon-machine-learning-nice-easy-simple.html

• The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing

  Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.

  https://www.kdnuggets.com/2016/02/iclr-deep-learning-scientific-publishing-experiment.html

• 21 Must-Know Data Science Interview Questions and Answers">21 Must-Know Data Science Interview Questions and Answers

  KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.

  https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers.html

• AI Supercomputers: Microsoft Oxford, IBM Watson, Google DeepMind, Baidu Minwa

  In the world of AI, this is the equivalent of the US and USSR competing to put their guy on the moon first. Here is a profile of some of the giants locked into the AI space race.

  https://www.kdnuggets.com/2016/02/ai-supercomputers-microsoft-ibm-watson-google-deepmind-baidu.html

• Deep Learning with Spark and TensorFlow

  The integration of TensorFlow with Spark leverages the distributed framework for hyperparameter tuning and model deployment at scale. Both time savings and improved error rates are demonstrated.

  https://www.kdnuggets.com/2016/01/deep-learning-spark-tensorflow.html

• Businesses Will Need One Million Data Scientists by 2018

  Deepening shortage of Data Science talent and cybersecurity challenges are trends shaping business in 2016.

  https://www.kdnuggets.com/2016/01/businesses-need-one-million-data-scientists-2018.html

• How to Tackle a Lottery with Mathematics

  With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.

  https://www.kdnuggets.com/2016/01/how-tackle-lottery-mathematics.html

• Tour of Real-World Machine Learning Problems

  The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.

  https://www.kdnuggets.com/2015/12/tour-real-world-machine-learning-problems.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

• Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

  This year, Florida has experienced its 10th consecutive year without a hurricane, which is longest period without a hurricane strike in modern times. Exploring this is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization.

  https://www.kdnuggets.com/2015/12/walker-hurricanes-big-data-analytics-risk-visualization.html

• What is the importance of Dark Data in Big Data world?

  Dark data is a subset of big data, but it constitutes the biggest portion of the total volume of big data collected by organizations in a year. We will discuss about what opportunities this holds for an organization.

  https://www.kdnuggets.com/2015/11/importance-dark-data-big-data-world.html

• 7 Steps to Mastering Machine Learning With Python

  There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!

  https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html

• Understanding Convolutional Neural Networks for NLP

  Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.

  https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

• 5 Warning Signs that Turn Off Data Science Hiring Managers

  Here are some warning signs that will prevent managers from hiring you for a Data Science position. If your resume has one or more of them, make an effort to remove the risk factors.

  https://www.kdnuggets.com/2015/11/warning-signs-data-science-hiring-managers.html

• How Big Data is used in Recommendation Systems to change our lives

  A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.

  https://www.kdnuggets.com/2015/10/big-data-recommendation-systems-change-lives.html

• How big data can help in home health care?

  Proper home care services can reduce both the chances and the cost of hospitalization and manage illness. Understand what big data promises for the healthcare sector and what are practical hurdles standing between the current solutions.

  https://www.kdnuggets.com/2015/10/big-data-home-healthcare.html

• The Master Algorithm – new book by top Machine Learning researcher Pedro Domingos

  Wonderfully erudite, humorous, and easy to read, the Master Algorithm by top Machine Learning researcher Pedro Domingos takes you on a journey to visit the 5 tribes of Machine Learning experts and helps you understand what the Master Algorithm can be.

  https://www.kdnuggets.com/2015/09/book-master-algorithm-pedro-domingos.html

• Top 10 Quora Data Science Writers and Their Best Advice

  Top Quora data science writers give their advice on pursuing a career in the field, approaching interviews, and selecting appropriate technologies.

  https://www.kdnuggets.com/2015/09/top-data-science-writers-quora.html

• Big Data Monetization Lessons from Zillow

  In the current tsunami of “Big Data” every business wants to get value out of the data. Here, we are sharing lessons learned by the new real estate websites who have brought together Big Data sets, home buyers, and home sellers.

  https://www.kdnuggets.com/2015/09/big-data-monetization-lessons-from-zillow.html+

• Data Science Data Architecture

  Data scientists are kind of a rare breed, who juggles between data science, business and IT. But, they do understand less IT than an IT person and understands less business than a business person. Which demands a specific workflow and data architecture.

  https://www.kdnuggets.com/2015/09/data-science-data-architecture.html

• How to Balance the Five Analytic Dimensions

  When developing a solution one has to consider data complexity, speed, analytic complexity, accuracy & precision, and data size. It is not possible to best in all categories, but it is necessary to understand the trade-offs.

  https://www.kdnuggets.com/2015/09/how-balance-five-analytic-dimensions.html

• Big Data Influence on Data Driven Advertising

  More and more companies relying on big data for their data driven initiatives. In a survey conducted by BlueKai, we are trying to capture what its impact on advertising strategies.

  https://www.kdnuggets.com/2015/08/big-data-influencing-data-driven-advertising.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

• Recycling Deep Learning Models with Transfer Learning

  Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.

  https://www.kdnuggets.com/2015/08/recycling-deep-learning-representations-transfer-ml.html

• 3D Data Sculptures: a New Way to Visualize Data

  3D printing can go beyond printing products like iPod cases, or butterfly earrings, and can offer a sustainable way to understand strategic DATA by printing decision support landscapes.

  https://www.kdnuggets.com/2015/08/3d-data-sculptures-visualize-data.html

• How Long Should You Stay at Your Analytics Job?

  Considering the huge demand for the data scientists many are pondering to switch for a better profile and salary. But, there some things to be pondered about like what should be the interval between two switches, acquiring new skills and your loyalty.

  https://www.kdnuggets.com/2015/08/how-long-stay-analytics-job.html

• Data is Ugly – Tales of Data Cleaning

  Whether you want to do business analytics or build the deep learning models, getting correct data and cleansing it appropriately remains the major task. Find out experts opinions on how you can make efficient data cleansing and collection efforts.

  https://www.kdnuggets.com/2015/08/data-ugly-tales-data-cleaning.html

• Emacs for Data Science

  Data science nowadays demands a polyglot developer and, choosing a correct code editor would definitely be a worthy investment. Here we provide, important features of Emacs and its advantages over other editors.

  https://www.kdnuggets.com/2015/07/emacs-data-science.html

• Deep Learning and the Triumph of Empiricism

  Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?

  https://www.kdnuggets.com/2015/07/deep-learning-triumph-empiricism-over-theoretical-mathematical-guarantees.html

• Data Science and Big Data: Two very Different Beasts

  Creating artifact from the ore requires the tools, craftmanship and science. Same is the case of big data and data science, here we present the distinguishing factors between the ore and the artifact.

  https://www.kdnuggets.com/2015/07/data-science-big-data-different-beasts.html

• Using Ensembles in Kaggle Data Science Competitions- Part 3

  Earlier, we showed how to create stacked ensembles with stacked generalization and out-of-fold predictions. Now we'll learn how to implement various stacking techniques.

  https://www.kdnuggets.com/2015/06/ensembles-kaggle-data-science-competition-p3.html

• Excellent Tutorial on Sequence Learning using Recurrent Neural Networks

  Excellent tutorial explaining Recurrent Neural Networks (RNNs) which hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation.

  https://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html

• In Machine Learning, What is Better: More Data or better Algorithms

  Gross over-generalization of “more data gives better results” is misguiding. Here we explain, in which scenario more data or more features are helpful and which are not. Also, how the choice of the algorithm affects the end result.

  https://www.kdnuggets.com/2015/06/machine-learning-more-data-better-algorithms.html

• Interview: Joseph Babcock, Netflix on Discovery and Personalization from Big Data

  We discuss the steps involved in Discovery process at Netflix, impact due to multitude of devices, system generated logs, and surprising insights.

  https://www.kdnuggets.com/2015/06/interview-joseph-babcock-netflix-discovery-personalization.html

• Top 30 Social Network Analysis and Visualization Tools

  We review major tools and packages for Social Network Analysis and visualization, which have wide applications including biology, finance, sociology, network theory, and many other domains.

  https://www.kdnuggets.com/2015/06/top-30-social-network-analysis-visualization-tools.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

• Surprising Random Correlations

  An interesting demo showing how easy it is to find surprising correlations in real data. Is German unemployment rate related to Apple Stock? Is 10-year Treasury rate related to price of Red Winter Wheat? You will be surprised.

  https://www.kdnuggets.com/2015/05/surprising-random-correlations.html

• Plotly: Online Dashboards That Update Your Data and Graphs

  New online visualization option from Plot.ly allows you to have data visualizations and graphs that update dynamically.

  https://www.kdnuggets.com/2015/05/plotly-online-dashboards-update-data-graphs.html

• Interview: Bill Moreau, USOC on Evidence-based Medicine to Reduce Sports Injuries

  We discuss the success of Analytics in predicting sports injuries, recent progress in concussion management and the trends in data-driven evidence-based sports medicine.

  https://www.kdnuggets.com/2015/03/interview-bill-moreau-usoc-sports-medicine.html

• Data Science’s Most Used, Confused, and Abused Jargon

  As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.

  https://www.kdnuggets.com/2015/02/data-science-confusing-jargon-abused.html

• Top 30 people in Big Data and Analytics

  Innovation Enterprise has compiled a top 30 list for individuals in big data that have had a large impact on the development or popularity of the industry.

  https://www.kdnuggets.com/2015/02/top-30-people-big-data-analytics.html

• Interview: Eli Collins, Cloudera on Evolution and Future of Big Data Ecosystem

  We discuss the change in Big Data priorities, risks, Big Data ecosystem, rise of data culture in organizations, challenges, advice and more.

  https://www.kdnuggets.com/2015/02/interview-eli-collins-cloudera-big-data-ecosystem.html

• Predictions: 2015 Analytics and Data Science Hiring Market

  Thanks to Big Data, analytics have become inescapable. Forget the C-Suite if you’re not a Data Geek, recruiting for startups gets harder, analytics salary bands get a lift, and more 2015 predictions.

  https://www.kdnuggets.com/2015/01/predictions-2015-analytics-data-science-hiring-market.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

• Why Azure ML is the Next Big Thing for Machine Learning?

  With advanced capabilities, free access, strong support for R, cloud hosting benefits, drag-and-drop development and many more features, Azure ML is ready to take the consumerization of ML to the next level.

  https://www.kdnuggets.com/2014/11/microsoft-azure-machine-learning.html

• R and Hadoop make Machine Learning Possible for Everyone

  R and Hadoop make machine learning approachable enough for inexperienced users to begin analyzing and visualizing interesting data to start down the path in this lucrative field.

  https://www.kdnuggets.com/2014/11/r-hadoop-make-machine-learning-possible-everyone.html

• KDnuggets™ News 14:n29, Nov 5

  Features | Software | Opinions | News | Webcasts | Courses | Meetings | Jobs | Publications | Tweets | CFP | Quote Features Big Read more »

  https://www.kdnuggets.com/2014/n29.html

• KDnuggets™ News 14:n27, Oct 22

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

  https://www.kdnuggets.com/2014/n27.html