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For articles published after October 2015 or prior to February 2015, click here..

The following is a selection of featured articles that were posted in our previous weekly digests, in short, the best of the best on DSC. Single-starred articles are written by external/guest bloggers. Older popular articles are being added regularly, so please check out this page once a week!

Our upcoming book on data science 2.0 (or data science automation or data science handbook or the little data science book, not sure yet about the title) will be based on some of these (edited and revised) articles: these articles are double-starred below, with red starring **.

Double-starred articles, with blue starring **, were published in our Wiley book.

A great selection of articles prior to 2014, broken down by category, can be found here.

October 2015

  1. Collection Of SVM Libraries By Language - 10/26
  2. Collaborative Filtering Tutorials Across Languages - 10/26
  3. David Donoho reflects on "50 Years of Data Science" * - 10/26
  4. Top hadoop interview questions * - 10/26
  5. Comparing Data Science and Analytics * - 10/26
  6. Data Science Test - How do you rank? * - 10/26
  7. A Tour of Machine Learning Algorithms - 10/26
  8. A Map to Perfection: Using D3.js to Make Beautiful Web Maps - 10/26
  9. Top Machine Learning Employers - 10/26
  10. How To Use Multivariate Time Series Techniques For Capacity Planning * - 10/26
  11. 13 Machine Learning Data Set Collections - 10/26
  12. Zipf's distribution: great illustration ** - 10/26
  13. Big Data: 20 Mind-Boggling Facts Everyone Must Read - 10/26
  14. Forget Data Scientists - Make Everyone Data Savvy - 10/26
  15. What PhDs do wrong (and right!) when applying for Data Science jobs - 10/26
  16. 30 Quotes from a Data Science Pioneer ** - 10/26
  17. A Brief Introduction To Data Mining * - 10/26
  18. Outsourcing Data Analytics - 10/26
  19. Twitter as a platform for OPEN real time analytics * - 10/26
  20. Musical Taste Correlates with SAT Scores? * - 10/26
  21. 5 Warning Signs that Turn Off Data Science Hiring Managers * - 10/26
  22. Stream Processing – What Is It and Who Needs It - 10/26
  23. 100 Blogs on Analytics, Big Data, Data Science, and Machine Learning - 10/19
  24. 16 Great Data Science Bloggers Worth Following ** - 10/19
  25. 14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets ** - 10/19
  26. Great Python books for data scientists and data miners - 10/19
  27. Data Science Learning Resources - 10/19
  28. O'Reilly 2015 Salary Survey for Data Scientists - 10/19
  29. How to track and visualize data lineage * - 10/19
  30. Basic recommendation engine using R * - 10/19
  31. Creating a Graph Application with Python, Neo4j, Gephi and Linkurio...- 10/19
  32. 8 Free Popular Javascript Charting Libraries - 10/19
  33. The 10 Best Books to Read Now on IoT - 10/19
  34. Are you smart enough to work at Google? ** - 10/19
  35. 10 Reasons Why Big Data Analytics is the best career move * - 10/19
  36. A to Z list of Google acquisitions and where they ended up within G...* - 10/19
  37. Getting Your Company Started with Predictive Analytics – Part 2 - 10/19
  38. How Eleven Tech Companies Built Their Big Data Stacks * - 10/19
  39. Why Data Scientists Need to be Good Data Storytellers - 10/19
  40. 5 Tools Everyone in the Big Data Analytics Industry Should Be Using * - 10/12
  41. Are Extreme Weather Events More Frequent? The Data Science Answer ** - 10/12
  42. 5 Reasons To Learn Hadoop * - 10/12
  43. Introduction to Logistic Regression in R * - 10/12
  44. Data Scientists: Skills Mix, Team Makeup - 10/12
  45. Dangers of Using RMSE: Netflix Case Study * - 10/12
  46. Time Series IoT applications in Railroads * - 10/12
  47. Six Companies with Great Job Opportunities for Data Scientists - 10/12
  48. Getting Your Company Started with Predictive Analytics – Part 1 - 10/12
  49. 5 Unbelievable Ways You Can Be a Better Data Scientist in Business * - 10/12
  50. The State of Data Science in 2015 * - 10/12
  51. Is Game Theory important for Data Scientists? - 10/12
  52. 9 Steps to Become a Data Scientist from Scratch - 10/12
  53. Managing your data with graph databases * - 10/5
  54. Trick-or-Treat a Data Scientist * - 10/5
  55. The Biggest Risks of Big Data - 10/5
  56. In Defence of Small Data * - 10/5
  57. How to Become a Data Scientist on a Shoestring - 10/5
  58. Data Scientist versus Decision Scientist - Is There a Difference - 10/5
  59. 18 Reasons Data Scientists are Difficult to Manage ** - 10/5
  60. Top 10 Commercial Hadoop Platforms 
  61. An introduction to ElasticSearch * - 10/5
  62. Predictive Analytics for Beginners – Part 1 * - 10/5
  63. Two great visualizations about data science - 10/5
  64. Data Analysis in R * - 10/5
  65. All you need to know about Hadoop * - 10/5
  66. Feature Engineering Tips for Data Scientists and Business Analysts * - 10/5
  67. Linear Algebra Formulas for Econometrics * - 10/5
  68. Data Science in Simple Terms - 10/5
  69. Statistical Analysis Advisor Chart * - 10/5

September 2015

  1. 50+ Free Data Science Books - 9/28
  2. 100 top data science presentations - 9/28
  3. Data Scientists Skills in the job market * - 9/28
  4. 5 Text Classification Case Studies Using SciKit Learn - 9/28
  5. 10 Real Data Scientist Interview Questions - 9/28
  6. How To Forecast Time Series Data With Multiple Seasonal Periods * - 9/28
  7. 5 Of the Most Viewed Scipy and NumPy Questions with Problems on Sta... - 9/28
  8. 50 external machine learning / data science resources and articles - 9/28
  9. Challenge of the week: identifying patterns in complex time series ** - 9/28
  10. How many analytics companies are there? * - 9/28
  11. The Data Analytics Landscape - A Crystal Ball * - 9/28
  12. What are the odds of becoming President of USA, based on your zodiac? * - 9/28
  13. Recurrent neural networks, Time series data and IoT * - 9/28
  14. There is Something New Under the Sun: Uplift Modeling - 9/28
  15. What does a Data Scientist do? - 9/28
  16. How to monetize my data ? * - 9/28
  17. Foundations of Statistical Theory Being Questioned ** - 9/28
  18. Advantages and Disadvantages of NoSQL databases * - 9/28
  19. What is Hadoop? Great Infographics Explains How it Works - 9/21
  20. Baby Steps to Learn Data Science * - 9/21
  21. 10 Popular Java Machine Learning Tools & Libraries - 9/21
  22. Analyse Tuberculosis data using network analysis * - 9/21
  23. 10 tools and platforms for data preparation * - 9/21
  24. When your data science activities can send you to prison... ** - 9/21
  25. Five Signs You are a Big Data Horder - 9/21
  26. Big Data Top Trends in 2015 - * 9/21
  27. Automating the Data Scientist - 9/21
  28. Using data to understand how much we really spend with Medicare * - 9/21
  29. Most used feature in any BI solution? It's the Export to Excel butt...9/21
  30. 7 Questions Every Data Scientist Should Be Answering for Business * - 9/21
  31. 15 Books every Data Scientist Should Read -9/21
  32. How Shell Uses Analytics To Drive Business Success - 9/21
  33. 50 Articles about Hadoop and Related Topics - 9/21
  34. No cost training to becoming a data scientist * - 9/21
  35. How to Choose Between Learning Python or R First - 9/21
  36. Question for astronomers and movie producers ** - 9/14
  37. Is Python or Perl faster than R? ** - 9/14
  38. Discover, Access, Distill: The Essence of Data Science ** - 9/14
  39. Data scientist Demographics: 2015 versus 2013 - How Things Changed ...** - 9/14
  40. Job interview questions for data scientists - 9/14
  41. SQL: An Introduction * - 9/14
  42. Social Network Analysis reveals the alternative list of global powe...* - 9/14
  43. Mapping the Internet of Things - 9/14
  44. Analytics for Kids ** - 9/14
  45. How to Balance the Five Analytic Dimensions * - 9/14
  46. Grave Mistakes that Companies Make in Big Data Projects - 9/14
  47. Cost Effective Data Science for Smaller Banks and Credit Unions - 9/14
  48. SPARQL is the new King of all Data Scientist’s tools * - 9/14
  49. 15 Deep Learning Libraries - 9/14
  50. 7 Traits of Highly Successful Business Analytics Professionals - 9/14
  51. Paradox: what governs ocean tides: the moon, or the Sun? ** - 9/7
  52. A Plethora of Open Data Repositories (i.e., thousands!) * - 9/7
  53. How NASA experiments with knowledge discovery * - 9/7
  54. Simpson’s Paradox in the Age of Real Time Analytics - 9/7
  55. Data Science with Python & R: Dimensionality Reduction and Clus...* - 9/7
  56. Big Data: Too Many Answers, Not Enough Questions - 9/7
  57. What are Uplift Models? * - 9/7
  58. Training Neural Networks: Q&A with Ian Goodfellow, Google * - 9/7
  59. Can Big Data Algorithms Tell Better Stories Than Humans? - 9/7
  60. 22 easy-to-fix worst mistakes for data scientists ** - 9/7

August 2015

  1. 10 Most Commented Blog Posts on DSC ** - 8/31
  2. Top LinkedIn Groups for Analytics, Big Data, and Data Science - 8/31
  3. The top 10 worst graphs * - 8/31
  4. Great Machine Learning Infographics - 8/31
  5. A Visual Introduction to Machine Learning - 8/31
  6. 8 Online Classes That Will Make You Smarter About IT * - 8/31
  7. The Science Behind Big Data Analytics * - 8/31
  8. Is Predictive Modeling Dead? - 8/31
  9. Assembling The Data Team: Part 2: Traits to Avoid * - 8/31
  10. Fourth Grade Math Class Called. They Want Their Pie Chart Back. * - 8/31
  11. Linear Algebra in Julia * - 8/31
  12. Data scientist paid $500k can barely code! ** - 8/31
  13. Seasonality and Trend Analysis * - 8/31
  14. Ten top languages for crunching Big Data - 8/31
  15. What is Big Data, and why should you care? - 8/31
  16. 5 Signs That You Are NOT a Data Scientist - 8/31
  17. About the Curse of Dimensionality * - 8/31
  18. Building Web Data Products with R & Shiny - 8/31
  19. Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...** - 8/24
  20. Why so many 'fake' Big Data Gurus? * - 8/24
  21. Big Data Falls Off the Hype Cycle - 8/24
  22. Analytics: Like a Mosquito in a Nudist Colony - 8/24
  23. An Introduction to Data Visualization * - 8/24
  24. Machine Learning Libraries in Go Language - 8/24
  25. What if the Story Doesn't Match the Data? NYTimes & Amazon Case...- 8/24
  26. 10 Resources to Help You Stop Doing Pie Charts - 8/24
  27. Thought for Food: Analyzing the performance of a leading restaurant...* - 8/24
  28. Awesome Curated List of Data Viz Frameworks, Libraries, Software - 8/24
  29. Great Machine Learning Infographics - 8/17
  30. The Career Opportunity in Banking Data Scientists Can’t Afford To M...* - 8/17
  31. 10 Companies Looking to Hire Deep Learning Experts - 8/17
  32. 38 Seminal Articles Every Data Scientist Should Read - 8/17
  33. Data Science: Uber for Law * - 8/17
  34. Tutorial: Lift curve to assess quality of classification models * - 8/17
  35. How to build a Market Basket Analysis Engine * - 8/17
  36. What Will They Call Us in 10 Years? - 8/17
  37. A Simple Introduction to Data Science * - 8/17
  38. Storm data analysis * - 8/17
  39. 5 Really Cool Internet of Things Sports Gadgets - 8/17
  40. What Donald Trump Might Say About Data Science * - 8/17
  41. Why you should use open data to hone your machine learning models * - 8/17
  42. 20 Big Data Repositories You Should Check Out ** - 8/10
  43. Top 'Big Data' accounts on Twitter * - 8/10
  44. Generating Text Using a Markov Model * - 8/10
  45. Why I don't like IT ppl in my Analytics team * - 8/10
  46. 11 most popular data science presentations on Slideshare - 8/10
  47. 15 Questions All R Users Have About Plots * - 8/10
  48. Top 5 Trends in Big Data Analytics * - 8/10
  49. How Many Experts Does It Take? - 8/10
  50. Meta Collection of Data Science and Big Data Analytics Best Practic...* - 8/10
  51. 18 Open Source NoSQL Databases - 8/10
  52. Machine Learning in Javascript - A compilation of Resources - 8/10
  53. Data Science has a Terminology Problem - 8/10
  54. The 10 best cities to find a big data job - 8/10
  55. 11 Awesome Data Visualizations Way Ahead of Their Time [Infographic] - 8/10
  56. Simple rules to catch web spam ** - 8/3
  57. Using colors rather than digits, chars, biometrics or keywords, for...** - 8/3
  58. Comprehensive guide for Data Exploration in R - 8/3
  59. Deep Learning vs Machine Learning vs Pattern Recognition - 8/3
  60. The Big 'Big Data' Question: Hadoop or Spark? - 8/3
  61. Hadley Wickham, the Man Who Revolutionized R - 8/3
  62. 10 Machine Learning Terms Explained in Simple English * - 8/3
  63. 24 Data Science, R, Python, Excel, and Machine Learning Cheat Sheets - 8/3
  64. Introduction to Monte Carlo Methods * - 8/3
  65. The Beginner's Guide to Amazon Web Services - Infographic - 8/3
  66. Dummies for Data Science: A Reading List * - 8/3
  67. Evolution of Deep learning models * - 8/3

July 2015

  1. When is Python faster than R? ** - 7/27
  2. 7 Python Tools All Data Scientists Should Know How to Use - 7/27
  3. The Next Big Thing In Big Data: BDaaS - 7/27
  4. 9 Python Libraries Which Can Help You In Image Processing - 7/27
  5. A pletora of big data infographics - 7/27
  6. 31 external machine learning / data science resources and articles - 7/27
  7. A Guide to the Internet of Things - Infographic - 7/27
  8. 7 Skills/Attitudes to Become a Better Data Scientist * - 7/27
  9. 50 Articles about Hadoop and Related Topics - 7/27
  10. Guide To Linear Regression * - 7/27
  11. You need an algorithm, not a Data Scientist * - 7/27
  12. Getting a Data Science Education - 7/27
  13. The Data Science Ecosystem in One Tidy Infographic * - 7/27
  14. The Ultimate Beginner’s Guide to Data Quality and Business Intellig...* - 7/27
  15. Companies Devising New Strategies to Recruit Analytics Talent - 7/20
  16. Linear Algebra for Data Scientists * - 7/20
  17. What is the most dangerous dish at Olive Garden? * - 7/20
  18. Top Data Mining Algorithms Identified by IEEE & Related Python ...- 7/20
  19. 7 Common Biases That Skew Big Data Results - 7/20
  20. NoSQL and RDBMS are on a Collision Course - 7/20
  21. Origin of Techniques used in Data Science * - 7/20
  22. Machine Learning at Scale with Spark * - 7/13
  23. Random Forest in Python * - 7/13
  24. Which drink is more popular Tea, Coffee, Beer, Wine? * - 7/13
  25. Brand Image, Sentiment Analysis and Social Media * - 7/13
  26. Basic Data Exploration in R - * 7/13
  27. High-Return Data Science: Modernizing / Automating Digital Publishi...- ** 7/13
  28. What to do Once we Have Data in Our Hands? * - 7/13
  29. Taking R To The Next Level - * 7/13
  30. How To Identify A Good/Bad Data Scientist In A Job Interview? - 7/13
  31. Interesting new type of chart: obesochart - 7/6
  32. How to score data in Hadoop/Hive in a flash * - 7/6
  33. Statistical Modeling steps - Infographics * - 7/6
  34. Data Science as a profession – Time is Now * - 7/6
  35. 6 Tips for Being an Awesome Data Scientist * - 7/6
  36. How Much Do Data Scientists Really Earn? - 7/6
  37. Why So Many ‘Fake’ Data Scientists? - 7/6
  38. How to assess quality and correctness of classification models * - 7/6
  39. The seven people you need on your Big Data team * - 7/6
  40. Tell us: as a data scientist, what is your super power? ** - 7/6
  41. So You Want to be a Data Scientist - 7/6

June 2015

  1. Who still uses polynomial regression? - 6/29
  2. Visualize your Social Media Analytics * - 6/29
  3. 9 Must-Have Skills You Need to Become a Data Scientist - 6/29
  4. Tools for Data Visualization in R, Python, and Julia - 6/29
  5. Python Visualization Libraries List - 6/29
  6. The 50 Best Masters in Data Science - 6/29
  7. Comparing MongoDB with MySQL * - 6/29
  8. Data scientists are wasting their time * - 6/29
  9. How to choose a statistical model * - 6/29
  10. What roles do you need in your data science team? * - 6/29
  11. Book: Model-Based Machine Learning - 6/22
  12. Document Similarity Analysis Using ElasticSearch and Python - 6/22
  13. Quality and correctness of classification models: Confusion Matrix * - 6/22
  14. 10 Common NLP Terms Explained for the Text Mining Novice * - 6/22
  15. Feature Scaling and Normalization * - 6/22
  16. Python NLTK Tools List for Natural Language Processing (NLP) - 6/22
  17. Naive Bayes for Dummies; A Simple Explanation * - 6/22
  18. Reducing Data Cleansing Time to Get Actionable Insights Faster * - 6/22
  19. What are data scientists interested in? Insights from our search en...** - 6/22
  20. Your Math Is All Wrong: Flipping The 80/20 Rule For Analytics * - 6/22
  21. Decision Boundaries for Deep Learning and other Machine Learning cl...* - 6/22
  22. Big Data: The Amazing Numbers in 2015 - 6/22
  23. What Are the Differences Between Quantitative and Qualitative Data ...* - 6/22
  24. Spectacular patterns found in rare, big numbers used in encryption ...** - 6/22
  25. Case study: how much good traffic a scammer gets from one email spam ** - 6/15
  26. Regression Prediction using AWS Machine Learning - 6/15
  27. How is Big Data Changing the World? * - 6/15
  28. Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...** - 6/15
  29. $320,000 for a Java architect: Discussion about High Salaries ** - 6/15
  30. 7 Amazing Big Data Myths * - 6/15
  31. NewSQL - RDBMS on Steroids * - 6/15
  32. Fast-Track, On-Demand, No-Fee Program to Become a Data Scientist ** - 6/15
  33. Udacity's Data Analyst Nanodegree program * - 6/8
  34. NFL Play by Play analysis using Cloudera Impala * - 6/8
  35. Environmental Monitoring using Big Data * - 6/8
  36. Predicting records (highs or lows) - how to do it right ** - 6/8
  37. R Functions for Exploratory Analysis, Data Frame Merging & Map ...- 6/8
  38. Fraud detection in retail with graph analysis * - 6/8
  39. Data Science Summer Reading List 2015 * - 6/8
  40. Comparison of ML classifiers - a series of articles * - 6/8
  41. Average length of dissertations by higher education discipline - 6/8
  42. Top Five Data Science Masters Programs * - 6/8
  43. Cheat Sheet: Data Visualisation in Python - 6/8
  44. Cheat Sheet: Data Visualization with R - 6/8
  45. Data Science Courses to Avoid ** - 6/8
  46. Big Data: Uncovering The Secrets of Our Universe At CERN - 6/1
  47. 100 articles about Data Science with Excel ** - 6/1
  48. 100+ R tutorials, code snippets, libraries and resources ** - 6/1
  49. Determining the correctness of classification models * - 6/1
  50. Simple Regression use in Big Data * - 6/1
  51. Experimenting with AWS Machine Learning for Classification * - 6/1
  52. Machine Learning is Not the Boogie Man! * - 6/1
  53. Ontology for Data Science * - 6/1
  54. 7 Ingredients for Great Visualizations - 6/1

May 2015

  1. What Defines a Big Data Scenario? Infographics * - 5/25
  2. Question: how to determine the optimum number of variables? ** - 5/25
  3. Python (and R) for Data Science - sample code, libraries, projects,...** - 5/25
  4. Web Crawling & Analytics Case Study - Database Vs Self Hosted M...- 5/25
  5. Data science to understand and fight cancer * - 5/25
  6. An Introduction to Deep Learning and it’s role for IoT/ future cities * - 5/25
  7. Hadoop – Whose to Choose * - 5/25
  8. Statistics about the Spark community * - 5/25 
  9. The Handbook Of Data science * - 5/25
  10. How Do I Become a Data Scientist? / Data Science Aspects * - 5/25
  11. 100 Best Data Science Companies to Work for in 2015 - 5/25
  12. Four successful big data / analytics startups in Seattle ** - 5/25
  13. Big Data: Uncovering The Secrets of Our Universe At CERN - 5/25
  14. 9 Python Analytics Libraries * - 5/25
  15. 10 Python Machine Learning Projects on GitHub * - 5/25
  16. Data Integrity: The Rest of the Story * - 5/25
  17. How Apple Uses Big Data To Drive Success - 5/25
  18. Machine Learning Classes - Oxford University - 5/18
  19. Columbia data science course, week 1: what is data science? * - 5/18
  20. Book: Data Science from Scratch - First Principles with Python - 5/18
  21. Model-based simulations for military operations ** - 5/18
  22. Data Science and the Open World Assumption * - 5/18
  23. Tutorial: How to determine the quality and correctness of classific...* - 5/18
  24. The Rogue Data Scientist ** - 5/18
  25. Where Big Data Projects Fail - 5/18
  26. Data Science Wars: R versus Python - 5/18
  27. Internet of Things (IoT) Employers, Job Titles & Locations - 5/18
  28. Hadoop – Whose to Choose * - 5/18
  29. Smart data: IoT analytics for manufacturing and process industries * - 5/11
  30. What is your plan for cultivating analytics talent? * - 5/11
  31. Data Scientists Thoughts that Inspired Me * - 5/11
  32. Visualizing the ties between big pharma and doctors in France * - 5/11
  33. From Farming To Big Data: The Amazing Story of John Deere - 5/11
  34. Using Python on Azure Machine Learning Studio - 5/11
  35. How to Analyze Your Predictable Data: Anomaly Detection * - 5/11
  36. The Amazing Ways Uber Is Using Big Data - 5/11
  37. Free deep learning book - MIT press - 5/4
  38. The lowest paid data scientist - 5/4
  39. Short story on scaling an NLP problem without using a ton of hardware * - 5/4
  40. The 3Ms of Marketing Data Analytics * - 5/4
  41. What is the Internet of Everything (IoE)? * - 5/4
  42. Visualizing Nepal Earthquake: The Human Side of Data Science * - 5/4
  43. How NoSQL Fundamentally Changed Machine Learning * - 5/4
  44. Analytics: No Pain, No Gain * - 5/4
  45. What type of business intelligence tools are companies investing in...5/4
  46. Text Analysis 101: Explicit Semantic Analysis (ESA) Explained * - 3/4
  47. Big Data = Hype; But Why That Doesn't Matter - 5/4
  48. How Big Data and the Internet of Things Create Smart Cities - 5/4

April 2015

  1. What Is The Profession Of Data Science Really About Now And In The ...* - 4/27
  2. Analyzing customer data in sales & marketing - part 1 * - 4/27
  3. 36 Computer and Data Science Concepts Explained In Layman’s Terms - 4/27
  4. Great Github list of public data sets - 4/27
  5. Visualize Your Data Using Silk.co * - 4/27
  6. 43 Data Science Thought Leaders, According to Berkeley University - 4/20
  7. 180 leading data science, big data and analytics bloggers - 4/20
  8. Big Data Explained in Less Than 2 Minutes - To Absolutely Anyone * - 4/20
  9. The 7 Most Data-Rich Companies In The World * - 4/20
  10. 1,000 New Functions for Your Spreadsheet * - 4/20
  11. The Surprising Things You Don’t Know About Big Data - 4/20
  12. The 7 Most Unusual Applications of Big Data You’ve Ever Seen! * - 4/20
  13. An Excel Tutorial on Analyzing Large Data Sets - 4/20
  14. What makes wine sell? * - 4/20
  15. Analytics have zero value -- until they inform a decision * - 4/20
  16. 40 Excel Tricks - 4/13
  17. The 5 V's of Big Data by Bernard Marr - 4/13
  18. That’s Data Science: Airbus Puts 10,000 Sensors in Every Single Wing! - 4/13
  19. A Data Scientist's Advice to Business Schools * - 4/13
  20. Top 5 Disruptive Technologies that Will Change the World * - 4/13
  21. Ten General Principles in Data Mining/Science * - 4/13
  22. Finding Group Structures in Data using Unsupervised Machine Learning * - 4/13
  23. D3 Data Visualizations Case Study: Apple Patents - 4/13
  24. Top Downloaded & Most Discussed R Packages - 4/13
  25. The Hype Around Graph Databases And Why It Matters * - 4/13
  26. Simplest Way to Monetize Data: Think of Data as a Product * - 4/13
  27. Mega collection of data science books and terminology - 4/13
  28. 6 Cloud Based Machine Learning Services * - 4/6
  29. Meet the New Chief Data Scientist of the United States Government * - 4/6
  30. How to Transform your Google Spreadsheet Into an Opinion Mining Tool * - 4/6
  31. What Technology & Tool Skills Do Data Scientists Jobs Require? - 4/6
  32. How to Become a Data Scientist for Free * - 4/6
  33. Predicting Flights Delay Using Supervised Learning, Logistic Regres...* - 4/6
  34. 50 great data visualizations - 4/6
  35. Abridged List of Machine Learning Topics - 4/6
  36. 8 Hadoop articles that you should read - 4/6
  37. Mega collection of data science books and terminology - 4/6

March 2015

  1. 8 Hadoop articles that you should read - 3/30
  2. Do data scientists need to be domain experts to deliver good analyt...* - 3/30
  3. How a Chief Data Officer Can Make Your Data Great * - 3/30
  4. The Data Science Ecosystem in One Tidy Infographic * - 3/30
  5. 27 free data mining books - 3/30
  6. Predicting Car Prices Part 1: Linear Regression * - 3/30
  7. Predicting Car Prices Part 2: Using Neural Network * - 3/30
  8. Decision tree vs. linearly separable or non-separable pattern * - 3/30
  9. Can you Be a Growth Hacker Without Being a Data Scientist? * - 3/30
  10. Challenge of the week: mathematical paradox, $500,000 award ** - 3/30
  11. Impact of target class proportions on accuracy of classification * - 3/23
  12. THE PAST (Entity-Attribute-Value) vs THE FUTURE (Sign, Signifier, S...* - 3/23
  13. Mining Web Pages in Parallel * - 3/23
  14. Applying Data Science to Oscar Nominations * - 3/23
  15. Unstructured Data: InfoGraphics - 3/23
  16. Smart Big Data: The All-Important 90/10 Rule - 3/23
  17. Hacking Y Combinator - 3/23
  18. Real-time analytics weather - 3/23
  19. 100 R packages - 3/23
  20. 40 maps that explain the Internet - 3/23
  21. Do You Really Need a Unicorn? - 3/23
  22. Is Spark The Data Platform Of The Future? * - 3/16
  23. Contradictions of Big Data * - 3/16
  24. The Evolution of a Data-Driven Startup * - 3/16
  25. Data Integrity - A Sequence of Words Lost in the World of Big Data * - 3/16
  26. Infographics: The Half Life of Data * - 3/16
  27. Three periodic tables for data scientists - 3/16
  28. How Data can Help Law School Applicants Get Into the Program of The...* - 3/16
  29. Free eBook: Practical Data Cleaning - 3/16
  30. The Awesome Ways Big Data Is Used Today To Change Our World * - 3/9
  31. All Machine Learning Models Have Flaws - 3/9
  32. Swimming Pools and Nicholas Cage: A Look at How We Misunderstand Co...* - 3/9
  33. Taking the Fear Out of Statistics * - 3/9
  34. Infographics on data quality - 3/9
  35. Salary Trends for Data Science Professionals - 3/9
  36. Avoiding a common mistake with time series * 3/9
  37. Jackknife and linear regression in Excel: implementation and compar...- ** 3/9
  38. Why Does Everyone Need to Learn How to Code? To Understand Data * - 3/9
  39. Is Microsoft Putting Big Data At The Heart Of Their Business? * - 3/9
  40. Why Women Make Great Data Scientists * - 3/9
  41. Who are the Top Hirers for Data Scientists? - 3/9
  42. Challenge of the week: detecting multiple periodicity in time series ** - 3/2
  43. Building blocks of data science ** - 3/2
  44. Is Microsoft Putting Big Data At The Heart Of Their Business? * - 3/2
  45. Why Women Make Great Data Scientists * - 3/2
  46. Who are the Top Hirers for Data Scientists? ** - 3/2
  47. The Imminent Future of Predictive Modeling * - 3/2
  48. Data + Science = Sexy * - 3/2
  49. Optimal Binning for Scoring Modeling (R Package) * - 3/2
  50. Have You Heard About DRILL? * - 3/2
  51. Visualization of data science patterns * - 3/2
  52. An Open-Source JavaScript Library for Mobile-Friendly Interactive M...3/2
  53. Big Data in Oil and Natural Gas industries * - 3/2
  54. Book: Data Science in R - 3/2

February 2015

  1. Most popular data science keywords on DSC ** - 2/23
  2. Top Mistakes Developers Make When Using Python for Big Data Analytics ** - 2/23
  3. 10 Modern Statistical Concepts Discovered by Data Scientists ** - 2/23
  4. Big Data: The 4 Layers Everyone Must Know * - 2/23
  5. Random Walks or Fractal Matters * - 2/23
  6. PythoneeR * - 2/23
  7. Data Scientists: Where do you all live? - 2/23
  8. What is big data - Infographics by Bernard Marr * - 2/23
  9. Discovering the relationship of the G20 members using Data Mining * - 2/23
  10. Experiments of Deep Learning with {h2o} package on R - 2/23
  11. Practical Data Science in Python: Guidebook - 2/23
  12. Online Courses to Freshen Up Your Knowledge of Big Data * - 2/23
  13. 40+ Interviews on Data Science Industry * - 2/23
  14. Awesome Data Science Repository * - 2/23
  15. Do all credit card accounts eventually die from fraud? ** - 2/23
  16. Data Scientist Shares his Growth Hacking Secrets ** - 2/16
  17. DSC Data Science Search Engine ** - 2/16
  18. Big Data: What Are The Key Jobs and Salaries Available? * - 2/16
  19. Big Data or Not Big Data: What is your question? * - 2/16
  20. Real Time Spatial Analytics Using AWS Cloud Search - 2/16
  21. Decision Scientist vs. Data Scientist * - 2/16
  22. Programming for Data Science the Polyglot approach: Python + R + SQL * - 2/16
  23. 2015 Survey of Data Scientists Reveals Strategic Insights * - 2/16
  24. SuperBowl XLIX in Tweets: Sentiment Analysis of 4 Million Tweets * - 2/16
  25. 400 Categorized Job Titles for Data Scientists ** - 2/9
  26. Big data and Data Visualization * - 2/9
  27. 64 new external resources and articles about data science, big data - 2/9
  28. Four great visualizations - 2/9
  29. Data science: It's greater than the sum of the parts - 2/9
  30. Will Big Data Make Data Scientists Redundant? * - 2/9
  31. Making the Business Case for Text Analytics * - 2/9
  32. Data Analysis with Pandas - 2/9
  33. Book Introduction: High-Performance Data Mining and Big Data Analytics - 2/9
  34. 7500 companies hiring data scientists ** - 2/2
  35. 4 Ways Big Data Is Transforming Healthcare * - 2/2
  36. R Spatial Representation * - 2/2
  37. The Art of Data Science - Part 1 * - 2/2
  38. The Free 'Big Data' Sources Everyone Should Know * - 2/2
  39. Solving Poisson Distribution Problems Using SciPy * - 2/2
  40. Data Science for IoT: The role of hardware in analytics * - 2/2
  41. Poster about data science * - 2/2
  42. 62 new external resources and articles about data science, big data - 2/2
  43. New data science comic strip - 2/2
  44. Free Online Book: Forecasting, Principles and Practice * - 2/2
  45. How to: Parallel Programming in R and Python [Video] * - 2/2

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