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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.

How to access these articles?

  • Articles published after October 2015, click here.
  • Articles published between February 2015 and October 2015, click here.
  • Articles published prior to February 2015: see below.

January 2015

  1. The impact of asking the wrong question ** - 01/26
  2. Mysterious gaps in Google Analytics numbers ** - 01/26
  3. The 5 Essential Skills Any Data Scientist Needs - * 01/26
  4. Text Mining: Clustering and Unsupervised Methods - * 01/26
  5. Why Topological Data Analysis Works * - 01/19
  6. Statistician versus data scientist arrogance * - 01/19
  7. Top 10 Big Data and Analytics References * - 01/19
  8. In Big Data, Preparing the Data is Most of the Work * - 01/19
  9. VC investment analytics with data visualization * - 01/19
  10. Is Big Data the Single Biggest Threat To Your Job * - 01/19
  11. Data Science Art ** - 01/19
  12. 7 Traps to Avoid Being Fooled by Statistical Randomness* - 01/12
  13. Three Types Of Analytic Talent You Need * - 01/12
  14. Is Big Data the Single Biggest Threat To Your Job * - 01/12
  15. 9 tips for effective data mining * - 01/12
  16. How to Lie with Visualizations: Statistics, Causation vs Correlatio...* - 01/12
  17. Text Analysis 101; A Basic Understanding for Business Users * - 01/12
  18. Insight-driven vs. Intuition-driven Decision Making * - 01/12
  19. Understanding Linear Regression * - 01/12
  20. Latest online courses on Data Science * - 01/12
  21. The 2 Types Of Data Scientists Everyone Should Know About * - 01/05
  22. Get started with Hadoop and Spark in 10 minutes * - 01/15
  23. Most popular data science skills ** - 01/05
  24. What’s Hot & What’s Not in Data Science 2015 - 01/05
  25. Common Problems with Data * - 01/05
  26. Some statisticians have a biased view on data science ** - 01/05
  27. Engineering a far worse attack than Sony, without hacking ** - 01/05
  28. New Model for Scientific Research ** - 01/05
  29. Data Science Software Tools That Cost Nothing * - 01/05
  30. Comparison of statistical software * - 01/05

December 2014

  1. Regression Analysis using R explained * - 12/29
  2. 200 machine learning and data science resources - 12/29
  3. Choosing the Right BI Tool * - 12/29
  4. What can be predicted, and what can't? ** - 12/29 
  5. Are Earthquakes becoming more severe? ** - 12/29
  6. Update about the Data Science Apprenticeship ** - 12/29
  7. The data science project lifecycle * - 12/22
  8. A Statistician's View on Big Data and Data Science * - 12/22
  9. Data Visualization of Employee metrics at the top Tech companies * - 12/22
  10. Best solution to a problem: data science versus statistical paradigm ** - 12/22
  11. Discover, Access, Distill: The Essence of Data Science** - 12/15
  12. 10 data science predictions for 2015 - 12/15
  13. Big Data: The Key Vocabulary Everyone Should Understand * - 12/15
  14. Great Machine Learning Infographics * - 12/15
  15. Big Data, IOT and Security - OH MY! * - 12/15
  16. Sentiment Analysis of 11 Million Tweets * - 12/15
  17. What is the future of Data visualization and Dashboard solutions? * - 12/15
  18. Data science without statistics is possible, even desirable ** - 12/15
  19. 5 basic rules of data organization ** - 12/15
  20. Introducing the Linked Data Business Cube * - 12/8 
  21. Data Scientist: Owning Up to the Title * - 12/8
  22. Trends in Big Data Vs Hadoop Vs Business Intelligence * - 12/8
  23. Small versus big data, to choose a used car ** - 12/8
  24. Highest Paying Programming Skills - 12/01
  25. Text Analysis for Business Users * - 12/01
  26. 4 easy steps to becoming a data scientist ** - 12/01
  27. Easiest way to learn machine learning * - 12/01
  28. Implementing a Distributed Deep Learning Network over Spark * - 12/01
  29. High versus low-level data science ** - 12/01

November 2014

  1. Popular Software Skills in Data Science Job postings * - 11/24
  2. Don’t Expect A Large Salary Increase If You Didn’t Go To College * - 11/24
  3. A Study on Romantic Breakups on Twitter Using Data Science * - 11/24
  4. Trust The Algorithms, Not The Data * - 11/24
  5. Four great data science, big data, and deep machine learning books - 11/24
  6. The Only Skill you Should be Concerned With * - 11/17
  7. Unicorn Data Scientist Shares his Secrets with You ** - 11/17
  8. Why Data Scientists create poor Data Products ? * - 11/17
  9. 13 New Trends in Big Data and Data Science ** - 11/17
  10. 20 most commented blog posts on DSC - 11/17
  11. Why you should stay away from the stock market - 11/17
  12. 22 tips for better data science ** - 11/10
  13. My thoughts on data science and big data ** - 11/10
  14. The growth of data science over the last two years: 300% ** - 11/10
  15. Start with Good Science on Good Data, Then we'll Talk Big Data * - 11/10
  16. Adversarial analytics and business hacking: Amazon case study ** - 11/10
  17. 3 Must-Ask Questions Before Choosing That Machine Learning Algorithm! * - 11/10
  18. Pseudo data science funded by political money, on Facebook ** - 11/10
  19. Social Influence Analysis * - 11/10
  20. My Data Science Apprenticeship Project ** - 11/03
  21. The Single Best Predictive Modeling Technique * - 11/03
  22. Data science versus statistics, to solve problems: case study ** - 11/03
  23. 13 Machine Learning Books - 11/03

October 2014

  1. Prescriptive versus Predictive Analytics * - 10/27
  2. Data Science 2.0 ** - 10/27
  3. List of data set repositories for cool data science projects ** - 10/27
  4. 15 timeless data science articles - 10/27
  5. Fake traffic un-detected by Google Analytics - 10/27
  6. 50 Face Recognition APIs - 10/27
  7. A Comprehensive List of Big Data Statistics - 10/27
  8. Bottom line on Data Visualization * - 10/20
  9. Data Science is Dead - Long Live the Data Scientist * - 10/20
  10. A data scientist shares his passions ** - 10/20
  11. Is data science a new paradigm, or recycled material? ** - 10/20
  12. 2-D random walks: simulation, video with R source code, curious facts ** - 10/20
  13. Data scientists face burnout due to work-related stress - 10/20
  14. How to Compute Moving Average in R Language and Python * - 10/20
  15. What is Map-Reduce? - 10/20
  16. Data scientists face burnout due to work-related stress - 10/13
  17. A new version of the famous 3V diagram from Drew Conway - 10/13
  18. Popular predictive apps and APIs ** - 10/13
  19. Do you know what is bigger than Big Data? * - 10/13
  20. 200 Top Bloggers on Data Science Central ** - 10/13
  21. Unraveling Real-Time Predictive Analytics * - 10/13
  22. Political orientation of 100,000 websites ** - 10/13
  23. Top 30 DSC blogs, based on new scoring technology ** - 10/6
  24. The end of the Data Scientist Bubble ** - 10/6
  25. Apache Spark: distributed data processing faster than Hadoop * - 10/6
  26. The 22 Skills of a Data Scientist - 10/6
  27. 10 most popular data science presentations on Slideshare - 10/6
  28. 38 Seminal Articles Every Data Scientist Should Read ** - 10/6
  29. Hadoop is Dead. DataFlow is Alive! - 10/6

September 2014

  1. 100+ leading blogs for statisticians and like-minded professionals - 9/29
  2. Great list of resources: data science, visualization, machine learn...** - 9/29
  3. Difference between data engineers and data scientists ** - 9/29
  4. 20 Big Data Repositories You Should Check Out ** - 9/29
  5. 50 Data Science and Statistics Blogs Worth Reading - 9/29
  6. Preliminary findings about Zipf's Law (the thick tail distribution) * - 9/29
  7. 43 Data Science Thought Leaders, According to Berkeley University - 9/29
  8. Top 2,500 Data Science, Big Data and Analytics Websites ** - 9/22
  9. Top 2,500 Websites - top of the top ** - 9/22
  10. Job skills required to get hired by data science startups ** - 9/22
  11. Top Cities and Other Demographics for Data Scientists ** - 9/22
  12. Defining Big Data ** - 9/22
  13. Big data disguised as small data, causing dangerous side effects ** - 9/22
  14. Web crawler for clustering 2,500 data science websites ** - 9/22
  15. How to find the real web domain hidden behind a bit.ly shortened URL? ** - 9/22
  16. Web traffic statistics from competitors - which vendor do you trust? ** - 9/22
  17. Big Data Technology Vendor Consolidation * - 9/15
  18. Interactive visualization of growing Data Science / Big Data profil...** - 9/15
  19. Lesson 8: Graph Databases * - 9/15
  20. 180 leading data science, big data and analytics bloggers ** - 9/15
  21. Skills you need to become a data scientist * - 9/15
  22. NoSQL Databases are Good for Everything – Except Maybe this One Thing * - 9/8
  23. How to get published on Data Science Central ** - 9/8
  24. Curious formula generating all digits of square root numbers ** - 9/8
  25. 9 Lessons: Picking the Right NoSQL Tools * - 9/8
  26. Are You A Data Scientist ? * - 9/8
  27. Some software and skills that every Data Scientist should know * - 9/8
  28. Frozen versus liquid analytics ** - 9/1
  29. 33 unusual problems that can be solved with data science ** - 9/1
  30. How to Become a Data Scientist * - 9/1
  31. Synthetic criterion to choose the right variables for your predicti...** - 9/1
  32. Crazy Data Science Tutorial: Classification and Clustering * - 9/1
  33. Grouping and Summarizing Data of Big Text Files in R * - 9/1
  34. How to design better search engines? ** - 9/1

August 2014

  1. Network graph analysis for fraud detection and mitigation * - 08/25
  2. Data analysis software compared * - 08/25
  3. 21 great charts - 08/25
  4. Easiest way to learn machine learning * - 08/25
  5. From Data Analyst to Predictive Modelers to Data Scientists * - 08/25
  6. Why Zipf's law explains so many big data and physics phenomenons ** - 08/25
  7. Data Science Projects ** - 08/18
  8. Challenge of the week - Modeling and explaining the law of series ** - 08/11
  9. Your Data Science Portfolio: Math Skills Don't Matter * - 08/11
  10. Data Science: Fixing the Talent Shortage ** - 08/11
  11. Is Python or Perl faster than R? ** - 08/11
  12. Black-box Confidence Intervals: Excel and Perl Implementation ** - 08/11
  13. Word Clouds of Big Data, Data Science and Other Buzz Words * - 08/04
  14. Huge Trello List of Great Data Science Resources * - 08/04
  15. The law of series: why 4 plane crashing in 6 months is a coincidence ** - 08/04
  16. Data Science Cheat Sheet ** - 08/04

July 2014

  1. Can A 50-Person Startup Threaten Oracle, IBM, And Microsoft? - 07/28
  2. 5 Industries That Need Big Data * - 07/28
  3. 10 types of regressions. Which one to use? ** - 07/28
  4. Data Scientist Core Skills * - 07/28
  5. Challenge of the Week - Time Series and Spatial Processes ** - 07/28
  6. Rants from a great under-paid data scientist - 07/28
  7. 16 analytic disciplines compared to data science ** - 07/28
  8. 15 interviews with 15 data scientists - 07/21
  9. 10 Features all Dashboards Should Have ** - 07/21
  10. How to Process Text Files in the Data Analytics * - 07/21
  11. The fastest growing data science / big data profiles on Twitter ** - 07/21
  12. Great list of resources - NoSQL, Big Data, Machine Learning and more - 07/21
  13. 10 Features any Great Dashboard Should Have ** - 07/14
  14. Twelve Emerging Trends in Data Analytics (part 1 of 4) * - 07/14
  15. Top Data Scientists on Twitter ** - 07/14
  16. Beyond The Visualization Zoo * - 07/07
  17. 100 Big data analytics companies, tools, software - 07/07
  18. 25 Data Scientists Popular on LinkedIn - 07/07
  19. 35 books on Data Visualization - 07/07
  20. 12 Books and other resources to learn R - 07/07
  21. Comparison of Tableau, Qlikview and Omniscope * - 07/07
  22. Challenge of the Week - Random Numbers ** - 07/07

June 2014

  1. Clustering Similar Images Using MapReduce Style Feature Extraction ... * - 06/30
  2. Unsolicited data scientists solving your problems without using you...  ** - 06/30
  3. Three Fundamental Google rules detected thanks to data science ** - 6/30
  4. Internet of Things? Maybe. Maybe Not * - 06/30
  5. Is data science a sin against the norms of statisticians? ** - 06/23
  6. Must read before attending any data science interview ** - 06/23
  7. Best kept secret about data science competitions ** - 06/23
  8. The cost of underestimating the power big data - 06/23
  9. Challenge of the week: Piecewise linear clustering versus SVM - 06/23
  10. Data Science Summer Reading List 2014 * - 06/16
  11. Should you Tell Your Kids to be Data Scientists – Not Doctors?  - 06/16
  12. About the Curse of Dimensionality * - 06/16
  13. Big Data Poster - 06/16
  14. Data science comic strips - 06/16
  15. A Tour of Machine Learning Algorithms * - 06/09
  16. Build basic recommendation engine using R * - 06/09
  17. 40 maps that explain the Internet * - 06/09
  18. 100+ Interesting Data Sets for Data Science ** - 06/09
  19. Data Science Has Been Using Rebel Statistics for a Long Time ** - 06/09
  20. New Data Science Projects Added to Data Science Apprenticeship ** - 06/09
  21. The Greatest Database Ever * -6/02
  22. The 10 Algorithms That Dominate Our World * - 06/02
  23. Top Ten Big Data Analytics Tips * - 06/02
  24. Three interesting but little known programming languages * - 06/02
  25. Being a data scientist in a small country: challenges and solutions - 06/02
  26. List of NoSQL Databases * - 06/02

May 2014

  1. Tutorial: How to detect spurious correlations, and how to find the ... ** - 05/26
  2. 77 People Who Truly Have Written Interesting Things About Data - 05/26
  3. Automatic Identification of Replicated Criminal Websites Using Comb... * - 05/26
  4. Research Brief: Four Functional Clusters of Analytics Professionals * - 05/26
  5. 50 big data companies to follow * - 05/26
  6. More than 100 data science, analytics, big data, visualization books - 05/26
  7. Proposal for a new type of scoring system ** - 05/26
  8. Resource: Tons of data sets ** - 05/26
  9. My answer to spurious correlations (previous challenge of the week) ** - 05/26
  10. 15+ Great Books for Hadoop - 05/26
  11. Journey of a data scientist ** - 05/19
  12. 20 Excel Spreadsheet Secrets - 05/19
  13. 50 copies of data science book, signed by the author: get yours! - 05/19
  14. The salary of a data science author - 05/19
  15. Data Science Cheat Sheet ** - 05/19
  16. Academic salaries exposed - 05/19
  17. Which one is best: R, SAS or Python, for data science? - 05/12
  18. 30 Basic Tools For Data Visualization * - 05/12
  19. How the gap between data science and statistics grew over time - 05/12
  20. A Statistician's View on Data and Data Science * - 05/12
  21. Statisticians, big data gurus, data scientists, data miners: we're ... - 05/12
  22. Large set of Machine Learning and Related Resources - 05/12
  23. Why the human species hasn't produced trillionaires yet? - 05/12

April 2014

  1. Data science displacing traditional science - 04/28
  2. Good and not so good companies for data scientists - 04/28
  3. 16 resources to learn and understand Hadoop ** - 04/21
  4. How to identify the right data scientist for your company ** - 04/21
  5. Big data: are we making a big mistake? My reaction ** - 04/14
  6. Data Science for business hacking ** - 04/14
  7. The Data Science Venn Diagram Revisited - 04/07
  8. Selection of must-read articles - 04/07
  9. Foundations of classical statistical theory being questioned - 04/07

March 2014

  1. Is Data Scientist the right career path for you? - 03/31
  2. Nate Silver's famous run of successful predictions came to an halt - 03/31
  3. Top 10 Capabilities for Exploring Complex Relationships in Data for... * - 03/31
  4. The Data Science Toolkit - My Boot Camp Ciriculum * - 03/31
  5. From the trenches: 360-degree data science ** - 03/31
  6. Foundations of classical statistical theory being questioned ** - 03/31
  7. Comparing apples and oranges - 03/31
  8. Jackknife logistic and linear regression for clustering and predict...** - 3/24
  9. Big Data A to ZZ – A Glossary of my Favorite Data Science Things * - 3/24
  10. Machine Learning in Parallel with Support Vector Machines, Generali...* - 3/20
  11. Big data, big pay: 10 data jobs with climbing salaries - 3/24
  12. Learn experimental design with our live, real-time ongoing analysis ** - 3/24
  13. Another interesting 'data science is dead' article - 3/24
  14. New book: Data Just Right - 3/24
  15. Analytics Handbook - 3/24
  16. The best kept secret about linear and logistic regression ** - 3/17
  17. 7 Key Skills of Effective Data Scientists * - 3/17
  18. The Ideal Data Science Team * - 3/17
  19. Two big datasets to challenge your data science expertise - 3/17
  20. Great example of root cause analysis ** - 3/17
  21. Life Cycle of Data Science Projects ** - 3/17
  22. 17 areas to benefit from big data analytics in next 10 years ** - 3/17
  23. R in the cloud - 3/17
  24. Predictive model used in air traffic cancellator ** - 3/10
  25. Sometimes outliers are real data ** - 3/10
  26. The Growth of Hadoop from 2006 to 2014 - 3/10
  27. How to compete against data scientists charging $30/hour ** 3/10
  28. Recommender Systems - past, present, and future * - 3/3
  29. Introduction to my data science book - 3/3
  30. Forecasting with the Baum-Welch Algorithm and Hidden Markov Models * - 3/3
  31. The Data Science Toolkit - The Future Web Toolkit * - 3/3

February 2014

  1. 20 short tutorials all data scientists should read (and practice) ** - 2/24
  2. Salary history and career path of a data scientist ** - 2/24
  3. Big Data Vendor Revenue and Market Forecast 2013-2017 - 2/24
  4. Two periodic tables for data scientists - 2/24
  5. How much is big data compressible? An interesting theorem ** - 2/24
  6. One Page R: A Survival Guide to Data Science with R - 2/17
  7. Interview with Dr. Roy Marsten, the Man Shaping Big Data - 2/17
  8. The top 1% data users consume 99% of all the data being produced - 2/17
  9. Big data is cheap and easy ** - 2/17
  10. R skills attract the highest salaries - 2/17
  11. Proposal for bulk email processing ** - 2/17
  12. 10 questions about big data and data science ** - 2/10
  13. How analytics will drive the future - * 2/10
  14. R + Python * - 2/10
  15. 2013 Data Science Salary Survey, by O'Reilly - 2/10
  16. Exploratory Data Analysis – Kernel Density Estimation and Rug Plots...* - 2/10
  17. Interview with David Cox, the most famous statistician still alive - 2/10
  18. California regulator seeks to shut down ‘learn to code’ bootcamps - 2/10
  19. Scary fraud scheme to empty your bank account - 2/10
  20. Big data misused to justify vaccination ** - 2/10
  21. Ingredients Of Data Science * - 2/3
  22. Predicting the Super Bowl * - 2/3
  23. Machine learning: an overview * - 2/3
  24. The Data Science Toolkit - first steps towards becoming a Data Scie...* - 2/3

January 2014

  1. Practical illustration of Map-Reduce (Hadoop-style), on real data ** - 1/27
  2. My thoughts on big data and data science: no, it's not hype ** - 1/27
  3. Three myths about data scientists and big data ** - 1/27
  4. Why Companies can't find analytic talent ** - 1/27
  5. Six categories of Data Scientists ** - 1/20
  6. 2014 Analytics Salary Guide - 1/20
  7. Machine Learning and Data Mining Books * - 1/20
  8. Big Data and Data Science Books * - 1/20
  9. Data Scientist versus Data Architect ** - 1/20
  10. Data Scientist versus Data Engineer ** - 1/20
  11. Data Scientist versus Statistician ** - 1/20
  12. Data Scientist versus Business Analyst ** - 1/20
  13. Big Data & Natural Language Processing - 1/20
  14. 10 ways for Banks to achieve greater profit and customer satisfaction * - 1/13
  15. Boosting Algorithms for Better Predictions * - 1/6
  16. Big data sets available for free ** - 1/6
  17. 6,000 Companies Hiring Data Scientists - 1/6
  18. What is Wrong with the Definition of Data Science ** - 1/6

December 2013

  1. A synthetic variance designed for Hadoop and big data ** - 12/30
  2. Facebook missing revenue because of poor data science integration ** - 12/30
  3. The Youngest Data Scientist ** - 12/23
  4. Harvard classes on data science - 12/23
  5. Uniquely identify a human being with two questions ** - 12/23
  6. Operational Data Science: excerpt from 2 great articles * - 12/16
  7. Has the pace of information growth started to slow? ** - 12/16
  8. Detecting Patterns with the Naked Eye ** - 12/16
  9. Retailers Using Big Data: The Secret Behind Amazon and Nordstrom’s ...* - 12/16
  10. Why statistical community is disconnected from Big Data and how to ...** - 12/9
  11. Lambda Architecture for Big Data Systems * - 12/9
  12. How to estimate how well connected your colleagues are ** - 12/9
  13. New in Plotly: Interactive Graphs with IPython * - 12/9
  14. Predictive Analytics for Financial Services * - 12/9
  15. How to cut everyone's commute time by a factor two ** - 12/9
  16. A New Source of Revenue for Data Scientists: Selling Data ** - 12/2
  17. Moore's law applied to big data ** - 12/2
  18. Attribution Modeling ** - 12/2
  19. Salaries for Hadoop professionals ** - 12/2
  20. A Statistician's View on Big Data and Data Science * - 12/2
  21. 125 Years of Public Health Data to Help Fight Contagious Diseases - 12/2

November 2013

  1. Java Coding Samples for Online Data-mining * - 11/25 
  2. Taxonomy of Data Scientists ** - 11/25
  3. The Data Science Equation ** - 11/25
  4. ETL, ELT and Data Hub: Where Hadoop is the right fit ? * - 11/25
  5. 23 Great Schools with Master’s Programs in Data Science - 11/25
  6. Big data set - 3.5 billion web pages - made available for all of us ** - 11/25
  7. Statistics needed for DS and data mining * - 11/25
  8. Another large data set - 250 million data points ** 11/25
  9. Fast Combinatorial Feature Selection with New Definition of Predict... ** - 11/18
  10. How to compare and rank data science programs? ** - 11/18
  11. Hidden decision trees revisited ** - 11/18
  12. Zipfian Academy versus Data Science Apprenticeship - 11/11
  13. Hadoop as a Data Management Hub * - 11/11
  14. A Practical Introduction to Data Science from Zipfian Academy * - 11/11
  15. More than 100 data science, analytics, big data, visualization books ** - 11/11
  16. Predictive Analytics: Man versus Machine Competition - 11/04
  17. Data Science Project: Captcha Attack - 11/04
  18. 16 Reasons Data Scientists are Difficult to Manage ** - 11/04
  19. Interesting Data Science Application: Steganography ** - 11/04

October 2013

  1. IBM Distinguished Engineer solves Big Data Conjecture ** - 10/28
  2. R Tutorial for Beginners: A Quick Start-Up Kit * - 10/28
  3. The Professionalization of Data Science* - 10/28
  4. Big Data Micro-Segmentation * - 10/28
  5. Difference between data engineers and data scientists ** - 10/21
  6. A little known component that should be part of most data science a... ** - 10/21
  7. Warm-up exercise before data science * - 10/21
  8. Get state and region from zip code, with simple Perl, Python, or R - 10/21
  9. Credit card number and password encoder / decoder ** - 10/21
  10. WEKA: Pluses and minuses - 10/21
  11. Oil n Gas Sensor Data + Big Data Analytics = Game Changer * - 10/21
  12. 11 Features any database, SQL or NoSQL, should have ** - 10/14
  13. Basic Understanding of Big Data * - 10/14
  14. Python Scikit-learn to simplify Machine learning * - 10/07
  15. Top Big Data Skills In Demand * - 10/07
  16. Data Science programs and training currently available - 10/07
  17. Sample source code for various data science tasks and projects - 10/07
  18. Wine and alcohol analytics ** - 10/07

September 2013

  1. Data Scientist vs. Statistician ** - 09/30
  2. Random Forests Algorithm * - 09/30
  3. Clustering idea for very large datasets ** - 09/30
  4. Analytics for kids ** - 09/30
  5. A Data Science Example: Deciding When to Sell Your House * - 09/23
  6. 50+ Open Source Tools for Big Data - 09/23
  7. Deriving Value with Data Visualization Tools * - 09/23
  8. Our best data visualization articles - 09/23
  9. Google F1 Database: One Step Closer To Discovering The DB Holy Grail - 09/23
  10. The Purple People: finding business expertise among Data Scientists * - 09/23
  11. Building better search tools: problems and solutions ** - 9/16
  12. Hadoop vs. NoSql vs. Sql vs. NewSql By Example * - 9/16
  13. The Best Of Open Source For Big Data * - 9/16
  14. The dangers of pseudo analytic science ** - 9/16
  15. Can you win a Facebook data science job? Take the test! - 9/9
  16. Marrying computer science, statistics and domain expertize ** - 9/9
  17. What will America pay for H1-B Jobs? * - 9/9
  18. Question: Career change into data science (many comments) * - 9/9
  19. Is the peanut war fueled by lack of analytic thinking? - 9/9
  20. Conditional Formatting in Excel – Highest Number in Each Row * - 9/9
  21. How to eliminate a trillion dollars in healthcare costs - 9/2
  22. Predictive Analytics: Harnessing the Power of Big Data * - 9/2
  23. An indispensable Python : Data sourcing to Data science * - 9/2
  24. Data Scientist Core Skills * - 9/2
  25. Prescriptive Analytics * - 9/2
  26. Top Languages for analytics, data mining, data science - 9/2
  27. The death of the statistician - 9/2
  28. Same dataset - Two different kind of visualizations * - 9/2
  29. Predictive modeling is useless! Here's why * - 9/2

August 2013

  1. BI vs. Big Data vs. Data Analytics By Example * - 8/26
  2. Data Science / Big Data Salary Survey by Burtch Works - 8/26
  3. 40 maps that explain the world - 8/26
  4. A Data Scientist’s Guide to Making Money from Start-Ups - 8/26
  5. How do I forecast a timeseries of data using GARCH(1,1)? * - 8/26
  6. How is big data used in the porn industry? - 8/26
  7. Will Data Science Forever Change Branding Strategies? - 8/19
  8. Batch vs. Real Time Data Processing * - 8/19
  9. Who Has The Largest Predictive Data Analytics? * - 8/19
  10. Underfitting/Overfitting Problem in Machine learning * - 8/19
  11. Why is Vlookup (in Excel) 1,000 times slower than hash tables in Py...** - 8/19
  12. 101 prime resources on mathematics - 8/19
  13. SQL: optimizing or eliminating joins? ** - 8/12
  14. Hadoop: What It Is And Why It’s Such A Big Deal * - 8/12
  15. A new type of weapons-grade secure email ** - 8/12
  16. 60+ R resources - 8/12
  17. 10 Enterprise Predictive Analytics Platforms Compared (2013) - 8/12
  18. What Makes a Good Data Scientist? - 8/12

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