Just in case you missed some of our digests while enjoying your vacation, here is a selection of good reading – the last popular articles and resources posted in December 2015: the best from the last 4 or 5 digests. Enjoy the reading!
- Internet of Things: Selected Articles
- 24 Uses of Statistical Modeling (Part I)
- 24 Uses of Statistical Modeling (Part II)
- Architecture of Data Science Projects
- 20 Big Data Repositories You Should Check Out
- 10 Free Machine Learning books
- 10 Harvard Business Review Articles that you should Read
- 100 Data Science Interview Questions and Answers
- Top 10 Machine Learning Algorithms
- R Programming: 35 Job Interview Questions and Answers
- 5 Data Science Leaders Share their Predictions for 2016 and Beyond
- 49 Machine Learning Resources and Related Articles from Top Bloggers
- 12 Most Popular Data Science Articles in 2015
- What you won’t learn in stats classes
- Top Algorithm, Data Science, Big Data and Machine Learning Experts
- A data scientist shares his passions
- The ABCD’s of Business Optimization
There were also numerous great contributions from various authors, including Bernard Marr and Bill Vorhies. The above selection is from our staff. Each of these articles generated anywhere between 1,000 and 8,000 page views in a short period of time.
DSC Resources
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- Buzz: Business News | Announcements | Events | RSS Feeds
- Misc: Top Links | Code Snippets | External Resources | Best Blogs | Subscribe | For Bloggers
Additional Reading
- The 10 Best Books to Read Now on IoT
- 50 Articles about Hadoop and Related Topics
- 10 Modern Statistical Concepts Discovered by Data Scientists
- Top data science keywords on DSC
- 4 easy steps to becoming a data scientist
- 13 New Trends in Big Data and Data Science
- 22 tips for better data science
- Data Science Compared to 16 Analytic Disciplines
- How to detect spurious correlations, and how to find the real ones
- 17 short tutorials all data scientists should read (and practice)
- 10 types of data scientists
- 66 job interview questions for data scientists
- High versus low-level data science
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