Search results for Data Science

Found 4811 documents, 6021 searched:

• 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated)

  Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  https://www.kdnuggets.com/2017/01/blogs-analytics-big-data-mining-data-science-machine-learning.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

• 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

• Sound Data Science: Avoiding the Most Pernicious Prediction Pitfall

  Data science and predictive analytics can provide huge value, but they can mislead and backfire if not used with fail-safe measures. The author gives examples of such problems and provides guidelines to avoid them.

  https://www.kdnuggets.com/2017/01/siegel-data-science-avoiding-prediction-pitfall.html

• A Funny Look at Big Data and Data Science">A Funny Look at Big Data and Data Science

  A less than serious look at Big Data and Data Science. If you can laugh at all cartoons, then your Data Science skills are in good shape.

  https://www.kdnuggets.com/2016/12/funny-big-data-science.html

• Academic, Research Positions in Big Data, Data Mining, Data Science, Machine Learning

    To add here a short entry for an academic or research position related to AI, Big Data, Data Science, or Machine Learning, email 5 Read more »

  https://www.kdnuggets.com/academic/index.html

• KDnuggets Consulting – expert advice on Business Analytics, Data Mining, and Data Science

  Gregory Piatetsky-Shapiro Experience Education Presentations & Tutorials Recent Publications Contact: [my first name] at kdnuggets.com, with a brief description of the issues. Gregory Piatetsky-Shapiro, Ph.D., Read more »

  https://www.kdnuggets.com/data-mining-consulting.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

• Data Sources for Cool Data Science Projects

  One of the biggest obstacles to successful projects has been getting access to interesting data. Here are some more cool public data sources you can use for your next project.

  https://www.kdnuggets.com/2016/12/thedataincubator-data-sources-data-science.html

• The 5 Basic Types of Data Science Interview Questions

  Data science interviews are notoriously complex, but most of what they throw at you will fall into one of these categories.

  https://www.kdnuggets.com/2016/12/5-basic-types-data-science-interview-questions.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

• 50+ Data Science, Machine Learning Cheat Sheets, updated">50+ Data Science, Machine Learning Cheat Sheets, updated

  Gear up to speed and have concepts and commands handy in Data Science, Data Mining, and Machine learning algorithms with these cheat sheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark, Matlab, and Java.

  https://www.kdnuggets.com/2016/12/data-science-machine-learning-cheat-sheets-updated.html

• Data Science Basics: What Types of Patterns Can Be Mined From Data?

  Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.

  https://www.kdnuggets.com/2016/12/data-science-basics-types-patterns-mined-data.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

• Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail

  We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.

  https://www.kdnuggets.com/2016/12/top-reasons-big-data-science-analytics-fail.html

• 10 Tips to Improve your Data Science Interview

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

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

• Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning

  Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.

  https://www.kdnuggets.com/2016/11/top-facebook-groups-big-data-science-machine-learning.html

• Cartoon: Thanksgiving, Big Data, and Turkey Data Science.

  We revisit KDnuggets Thanksgiving cartoon, which examines the predicament of one group of fowl Data Scientists.

  https://www.kdnuggets.com/2016/11/cartoon-thanksgiving-turkey-data-science.html

• Predictive Science vs Data Science

  Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.

  https://www.kdnuggets.com/2016/11/predictive-science-vs-data-science.html

• Process Mining: Where Data Science and Process Science Meet

  A data scientist without Process Mining training is ill-equipped to uncover the organization’s real processes, analyze compliance, diagnose bottlenecks and improve processes, so improve your skills with a new version of the free Coursera course "Process Mining: Data Science in Action" will start on November 28, 2016.

  https://www.kdnuggets.com/2016/11/process-mining-coursera-course.html

• Data Science and Big Data, Explained">Data Science and Big Data, Explained

  This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.

  https://www.kdnuggets.com/2016/11/big-data-data-science-explained.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

• Data Science Basics: An Introduction to Ensemble Learners

  New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.

  https://www.kdnuggets.com/2016/11/data-science-basics-intro-ensemble-learners.html

• Data Science 101: How to get good at R

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

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

• Learn Data Science in 8 (Easy) Steps

  Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!

  https://www.kdnuggets.com/2016/10/learn-data-science-8-steps.html

• Big Data Science: Expectation vs. Reality">Big Data Science: Expectation vs. Reality

  The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.

  https://www.kdnuggets.com/2016/10/big-data-science-expectation-reality.html

• Jupyter Notebook Best Practices for Data Science

  Check out this overview of Jupyter notebook best practices as pertains to data science. Novice or expert, you may find something of use here.

  https://www.kdnuggets.com/2016/10/jupyter-notebook-best-practices-data-science.html

• Top 10 Data Science Videos on Youtube">Top 10 Data Science Videos on Youtube

  Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?

  https://www.kdnuggets.com/2016/10/top-10-data-science-videos-youtube.html

• EDISON Data Science Framework to define the Data Science Profession

  EDISON Data Science Framework provides conceptual, instructional and policy components required to establish the Data Science profession.

  https://www.kdnuggets.com/2016/10/edison-data-science-framework.html

• KDnuggets™ News 16:n36, Oct 12: Battle of the Data Science Venn Diagrams; 9 Bizarre and Surprising Insights; ROI in Big Data Analytics

  Battle of the Data Science Venn Diagrams; Top September Stories in KDnuggets; Open Images Dataset; Still Searching for ROI in Big Data Analytics?

  https://www.kdnuggets.com/2016/n36.html

• Battle of the Data Science Venn Diagrams">Battle of the Data Science Venn Diagrams

  First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.

  https://www.kdnuggets.com/2016/10/battle-data-science-venn-diagrams.html

• Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team">Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team

  This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.

  https://www.kdnuggets.com/2016/10/interview-auto-sklearn-automated-data-science-machine-learning-team.html

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

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

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

• Top Data Scientist Claudia Perlich on Biggest Issues in Data Science">Top Data Scientist Claudia Perlich on Biggest Issues in Data Science

  Find out what top data scientist Claudia Perlich believes are - and are not - the biggest issues in data science today, and why spending 80% of their time with data preparation is not a problem.

  https://www.kdnuggets.com/2016/09/perlich-biggest-issues-data-science.html

• Data Science Basics: Data Mining vs. Statistics

  As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.

  https://www.kdnuggets.com/2016/09/data-science-basics-data-mining-statistics.html

• Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science">Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science

  The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.

  https://www.kdnuggets.com/2016/09/data-science-iot-10-differences.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

• Data Science Basics: 3 Insights for Beginners

  For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.

  https://www.kdnuggets.com/2016/09/data-science-basics-3-insights-beginners.html

• Doing the Data Science That Drives Predictive Personalization

  Agile collaboration within data science teams is essential to the vision of customer analytics and personalization. Attend IBM DataFirst Launch Event on Sep 27 in New York City to engage with open-source community leaders and practitioners.

  https://www.kdnuggets.com/2016/09/ibm-data-science-predictive-personalization.html

• Data Science vs Crime: Detecting Pickpocket Suspects from Transit Records

  A team of US and Chinese researchers has creatively used massive data collected by automated fare collectors for identifying thieves in the public transit systems. The system was tested in Beijing and was able to identify 93% of known pickpockets.

  https://www.kdnuggets.com/2016/09/kdd16-detecting-pickpocket-suspects-transit-records.html

• Data Science of Reviews: ReviewMeta tool Automatically Detects Unnatural Reviews on Amazon

  ReviewMeta is a tool that analyzes millions of reviews and helps customers decide which ones to trust. As the dataset grows, so do the insights on unbiased reviews.

  https://www.kdnuggets.com/2016/08/data-science-reviews-reviewmeta-detects-unnatural-amazon-reviews.html

• Central Limit Theorem for Data Science – Part 2

  This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.

  https://www.kdnuggets.com/2016/08/central-limit-theorem-data-science-part-2.html

• Central Limit Theorem for Data Science

  This post is an introductory explanation of the Central Limit Theorem, and why it is (or should be) of importance to data scientists.

  https://www.kdnuggets.com/2016/08/central-limit-theorem-data-science.html

• 5 EBooks to Read Before Getting into A Data Science or Big Data Career

  A short, carefully-curated list of 5 free ebooks to help you better understand what Data Science is all about and how you can best prepare for a career in data science, big data, and data analysis.

  https://www.kdnuggets.com/2016/08/simplilearn-5-free-ebooks-data-science-big-data.html

• Cartoon: Facebook data science experiments and Cats

  In honor of International Cat Day, we revisit KDnuggets cartoon that looks at the Facebook data science experiment on emotion manipulation and the importance of happy kittens.

  https://www.kdnuggets.com/2016/08/cartoon-cats-facebook-data-science.html

• Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising

  This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.

  https://www.kdnuggets.com/2016/08/automated-data-science-digital-advertising.html

• Contest 2nd Place: Automating Data Science

  This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.

  https://www.kdnuggets.com/2016/08/automating-data-science.html

• What Statistics Topics are Needed for Excelling at Data Science?

  Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.

  https://www.kdnuggets.com/2016/08/statistics-topics-needed-excelling-data-science.html

• The Core of Data Science

  This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.

  https://www.kdnuggets.com/2016/08/core-data-science.html

• Data Science of Visiting Famous Movie Locations in San Francisco

  Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.

  https://www.kdnuggets.com/2016/07/visiting-famous-movie-locations-san-francisco.html

• Theoretical Data Discovery: Using Physics to Understand Data Science

  Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.

  https://www.kdnuggets.com/2016/07/theoretical-data-discovery-physics-data-science.html

• Data Science Statistics 101

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

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

• Data Science for Beginners 1: The 5 questions data science answers

  A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.

  https://www.kdnuggets.com/2016/07/brohrer-data-science-beginners-1-5-questions.html

• Building a Data Science Portfolio: Machine Learning Project Part 1

  Dataquest's founder has put together a fantastic resource on building a data science portfolio. This first of three parts lays the groundwork, with subsequent posts over the following 2 days. Very comprehensive!

  https://www.kdnuggets.com/2016/07/building-data-science-portfolio-machine-learning-project-part-1.html

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

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

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

• TalkingData Data Science Competition: understand mobile users

  Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.

  https://www.kdnuggets.com/2016/07/talkingdata-data-science-competition-mobile-users.html

• Storytelling: The Power to Influence in Data Science

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

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

• 3 Key Ethics Principles for Big Data and Data Science

  If ethics in general are important, should ethics training be a crucial element of the data science field?

  https://www.kdnuggets.com/2016/07/ethics-principles-big-data-science.html

• Political Data Science: Analyzing Trump, Clinton, and Sanders Tweets and Sentiment

  This post shares some results of political text analytics performed on Twitter data. How negative are the US Presidential candidate tweets? How does the media mention the candidates in tweets? Read on to find out!

  https://www.kdnuggets.com/2016/06/politics-analytics-trump-clinton-sanders-twitter-sentiment.html

• 7 Steps to Mastering SQL for Data Science

  Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.

  https://www.kdnuggets.com/2016/06/seven-steps-mastering-sql-data-science.html

• Bootcamps in Analytics, Big Data, Data Science, Machine Learning

  BaseCamp, an innovative data science bootcamp from Knoyd. The first cohort will start in Vienna, Austria in January 2017. Data Science Dojo, an in-person or Read more »

  https://www.kdnuggets.com/education/bootcamps.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

• R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

  R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

  https://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

• 10 Must Have Data Science Skills, Updated

  An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.

  https://www.kdnuggets.com/2016/05/10-must-have-skills-data-scientist.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

• A Data Science Approach to Writing a Good GitHub README

  Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.

  https://www.kdnuggets.com/2016/05/algorithmia-data-science-approach-good-github-readme.html

• How to Network and Build a Personal Brand in Data Science

  SpringBoard shares some ideas on how to network and build a data career, as taken from a new guide they have put together on the topic.

  https://www.kdnuggets.com/2016/05/how-network-build-personal-brand-data-science.html

• Angoss 9.6 Data Science Software Suite

  Angoss software provides users with comprehensive scorecard building functionality that is fast, reliable, accurate, and business centric.

  https://www.kdnuggets.com/2016/04/angoss-9-6-data-science-software-suite.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

• Advantages of a Career in Data Science

  As the rampant growth of data science continues across industries, the opportunities are plenty for both aspiring and expert data scientists. Here is an overview of data science industries, opportunities and work locations.

  https://www.kdnuggets.com/2016/04/advantages-career-data-science.html

• Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

  A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.

  https://www.kdnuggets.com/2016/04/top-10-ipython-nb-tutorials.html

• Comprehensive Guide to Learning Python for Data Analysis and Data Science

  Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.

  https://www.kdnuggets.com/2016/04/datacamp-learning-python-data-analysis-data-science.html

• 12 Inspiring Women In Data Science, Big Data

  It’s been well documented that women don’t come close to parity in STEM fields with their counterparts. Could the rise of big data and data science offer women a clearer path to success in technology? Here’s a list of 12 inspiring women who work in big data and data

  https://www.kdnuggets.com/2016/04/12-inspiring-women-in-data-science-big-data.html

• CrowdFlower 2016 Data Science Report

  A new data science report with survey results related to the success and challenges of data scientists, and where data science is going as a discipline.

  https://www.kdnuggets.com/2016/04/crowdflower-2016-data-science-repost.html

• The Secret to a Perfect Data Science Interview

  How to interview a Data Scientist, in 5 steps. The secret to answering every question perfectly :).

  https://www.kdnuggets.com/2016/04/cartoon-interview-data-scientist.html

• 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning

  Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

  https://www.kdnuggets.com/2016/03/100-active-blogs-analytics-big-data-science-machine-learning.html

• Top 10 Data Science Resources on Github

  The top 10 data science projects on Github are chiefly composed of a number of tutorials and educational resources for learning and doing data science. Have a look at the resources others are using and learning from.

  https://www.kdnuggets.com/2016/03/top-10-data-science-github.html

• Doing Data Science: A Kaggle Walkthrough – Cleaning Data

  Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.

  https://www.kdnuggets.com/2016/03/doing-data-science-kaggle-walkthrough-cleaning-data.html

• The Data Science Game – Student Competition

  The Data Science Game returns this year, with university students competing for dominance. Details for this iteration and further information is provided here.

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

• New KDnuggets Tutorials Page: Learn R, Python, Data Visualization, Data Science, and more

  Introducing new KDnuggets Tutorials page with useful resources for learning about Business Analytics, Big Data, Data Science, Data Mining, R, Python, Data Visualization, Spark, Deep Learning and more.

  https://www.kdnuggets.com/2016/03/new-tutorials-section-r-python-data-visualization-data-science.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

• Top February stories: 21 Must-Know Data Science Interview Q&A; Gartner 2016 MQ for Advanced Analytics: gainers and losers

  21 Must-Know Data Science Interview Questions and Answers; Top 10 TED Talks for the Data Scientists; Gartner 2016 Magic Quadrant for Advanced Analytics Platforms: gainers and losers.

  https://www.kdnuggets.com/2016/03/top-news-2016-feb.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

• AutoML: Automated Data Science and Machine Learning

  For recent posts and more recent lists of AutoML and Automated Data Science, see Tag: AutoML. ABM: Automatic Business Modeler, automatically builds accurate and interpretable Read more »

  https://www.kdnuggets.com/software/automated-data-science.html

• Data Science and Disability

  Data Science and Artificial Intelligence has come to the forefront of technology in the last few years. Learn, how practitioners are taking a more philanthropic outlook on life, supporting people suffering with both physical and mental disabilities.

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

• 21 Must-Know Data Science Interview Questions and Answers, part 2

  Second part of the answers to 20 Questions to Detect Fake Data Scientists, including controlling overfitting, experimental design, tall and wide data, understanding the validity of statistics in the media, and more.

  https://www.kdnuggets.com/2016/02/21-data-science-interview-questions-answers-part2.html

• Data Science Skills for 2016

  As demand for the hottest job is getting hotter in new year, the skill set required for them is getting larger. Here, we are discussing the skills which will be in high demand for data scientist which include data visualization, Apache Spark, R, python and many more.

  https://www.kdnuggets.com/2016/02/data-science-skills-2016.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

• Python Data Science with Pandas vs Spark DataFrame: Key Differences

  A post describing the key differences between Pandas and Spark's DataFrame format, including specifics on important regular processing features, with code samples.

  https://www.kdnuggets.com/2016/01/python-data-science-pandas-spark-dataframe-differences.html

• Useful Data Science: Feature Hashing

  Feature engineering plays major role while solving the data science problems. Here, we will learn Feature Hashing, or the hashing trick which is a method for turning arbitrary features into a sparse binary vector.

  https://www.kdnuggets.com/2016/01/useful-data-science-feature-hashing.html

• Top 2015 KDnuggets Stories on Analytics, Big Data, Data Science, Data Mining, Machine Learning, updated

  R vs Python for Data Science: The Winner is ...; 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning; Top 20 Python Machine Learning Open Source Projects; 50+ Data Science and Machine Learning Cheat Sheets.

  https://www.kdnuggets.com/2016/01/top-2015-kdnuggets-stories-updated.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

• Data Science Humor: Google Analytics, if Applied in Real Life

  From the lighter side: how Google Analytics would look if applied in real life situations.

  https://www.kdnuggets.com/2016/01/data-science-humor-google-analytics-real-life.html

• Podcasts on AI, Analytics, Big Data, Data Science, Machine Learning

  Becoming a Data Scientist Podcast, started Dec 2015. Behind Data Science Podcast, by Big Cloud. Started April 2020. Brave New World Podcast, a look into Read more »

  https://www.kdnuggets.com/websites/podcasts.html

• Data Science Resume Tips and Guidelines

  A well-built resume is key to get through the first door – in the process of getting hired as a Data Scientist. Learn more, about how to present yourself as a true DS and which pitfalls to avoid.

  https://www.kdnuggets.com/2016/01/data-science-resume-tips-guidelines.html

• What questions can data science answer?

  There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.

  https://www.kdnuggets.com/2016/01/questions-data-science-answer.html

• The Art of Data Science: The Skills You Need and How to Get Them

  Learn, how to turn the deluge of data into the gold by algorithms, feature engineering, reasoning out business value and ultimately building a data driven organization.

  https://www.kdnuggets.com/2015/12/art-data-science-skills.html

• More Data Science Humor and Cartoons

  More humor and cartoons from Andrii aka San Sanych, #HappyDataScientist.

  https://www.kdnuggets.com/2015/12/more-data-science-humor-cartoons.html

• 5 Criteria To Determine If Your Data Is Ready For Serious Data Science

  If your data is a large, relevant, accurate, connected, and you also have a sharp question, you ready to do some serious data science. If you’re weak on 1-2 points, don’t worry. But if most criteria are not true, you need to do more preparation.

  https://www.kdnuggets.com/2015/12/5-criteria-data-ready-data-science.html

• Top stories for Dec 13-19: Top 10 Machine Learning Projects on Github; Importance of Data Science for IoT business

  Top 10 Machine Learning Projects on Github; Using Python and R together: main approaches; Importance of Data Science for IoT business; Top 10 Deep Learning Tips, Tricks.

  https://www.kdnuggets.com/2015/12/top-news-week-dec-13.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

• The hardest parts of data science

  The hardest part of data science is not building an accurate model or obtaining good, clean data, but defining feasible problems and coming up with reasonable ways of measuring solutions.

  https://www.kdnuggets.com/2015/11/hardest-parts-data-science.html

• The different data science roles in the industry

  Data science roles and responsibilities are diverse and skills required for them vary considerably. Here, we have described the different data science roles along with the skill set, technical knowledge and mindset required to carry it.

  https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

• Data Science of IoT: Sensor fusion and Kalman filters, Part 2

  The second part of this tutorial examines use of Kalman filters to determine context for IoT systems, which helps to combine uncertain measurements in a multi-sensor system to accurately and dynamically understand the physical world.

  https://www.kdnuggets.com/2015/11/data-science-iot-sensor-fusion-kalman-filters-part2.html

• 5 Best Machine Learning APIs for Data Science

  Machine Learning APIs make it easy for developers to develop predictive applications. Here we review 5 important Machine Learning APIs: IBM Watson, Microsoft Azure Machine Learning, Google Prediction API, Amazon Machine Learning API, and BigML.

  https://www.kdnuggets.com/2015/11/machine-learning-apis-data-science.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

• 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

• Data Science of IoT: Sensor fusion and Kalman filters, Part 1

  The Kalman filter has numerous applications, including IoT and Sensor fusion, which helps to determine the State of an IoT based computing system based on sensor input.

  https://www.kdnuggets.com/2015/10/data-science-iot-sensor-fusion-kalman-filters-part1.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

• Which Movie Sequels Are Really Better? A Data Science Answer

  The internet is filled with polls and lists of sequels that are better or worse movie in the series. Yet such rankings are often based on personal judgement and rarely on data and statistics. Here is our solution to analyze and visualize the movie series.

  https://www.kdnuggets.com/2015/10/movie-sequels-better-data-science.html

• Crushed it! Landing a data science job

  Data scientist interviews depend on the company and the team, it might look like a software developer’s interview, or statistician’s interview. Here we collected some hot tips to pass along if you’re thinking about a move soon.

  https://www.kdnuggets.com/2015/10/erin-shellman-landing-data-science-job.html

• What Types of Questions Can Data Science Answer

  Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques

  https://www.kdnuggets.com/2015/09/questions-data-science-can-answer.html

• 15 Mathematics MOOCs for Data Science

  The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.

  https://www.kdnuggets.com/2015/09/15-math-mooc-data-science.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

• The 123 Most Influential People in Data Science

  We used LittleBird algorithm to build a true Data Science influencer network by measuring how often influencers retweet other influencers. Top influencers include @hmason, @kdnuggets, @kaggle, @peteskomoroch, @mrogati, and @KirkDBorne.

  https://www.kdnuggets.com/2015/09/123-influential-people-data-science.html

• A Great way to learn Data Science by simply doing it

  There are tons of great online resources out there we can pick up and learn them to become a master in data science. Here is a comprehensive list of data science course providers along with links to the data science courses.

  https://www.kdnuggets.com/2015/09/learn-data-science-by-doing.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

• Salaries by Roles in Data Science and Business Intelligence

  Data Scientist is the hottest role. What's next? We present national average salaries, job title progression in career, job trends and skills for popular job titles in Data Science & Business Intelligence. Check out the salaries of related roles.

  https://www.kdnuggets.com/2015/09/salaries-roles-data-science-business-intelligence.html

• 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more

  Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data Science Tools, and Programming Languages for Data Science.

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

• Paradoxes of Data Science

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

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

• Three Essential Components of a Successful Data Science Team

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

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

• 50+ Data Science and Machine Learning Cheat Sheets

  Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms.

  https://www.kdnuggets.com/2015/07/good-data-science-machine-learning-cheat-sheets.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

• Dataiku Data Science Studio – intuitive solution for data professionals

  Data Science Studio (DSS) from Dataiku is an intuitive software solution that let data professionals harness the power of big data. The latest version DSS 2.0 brings predictive analytics to a whole new level in terms of collaboration and usability.

  https://www.kdnuggets.com/2015/07/dataiku-data-science-studio.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

• Using Ensembles in Kaggle Data Science Competitions – Part 2

  Aspiring to be a Top Kaggler? Learn more methods like Stacking & Blending. In the previous post we discussed about ensembling models by ways of weighing, averaging and ranks. There is much more to explore in Part-2!

  https://www.kdnuggets.com/2015/06/ensembles-kaggle-data-science-competition-p2.html

• Top 20 R Machine Learning and Data Science packages

  We list out the top 20 popular Machine Learning R packages by analysing the most downloaded R packages from Jan-May 2015.

  https://www.kdnuggets.com/2015/06/top-20-r-machine-learning-packages.html