Journal Directory Related to Data Science

This blog contains a list of journals related to Data Science and Analytics:

  1. Japanese Journal of Statistics and Data Science, Springer
  2. 10 Essential Academic Journal for Data Scientists, Analytics India
  3. International Journal of Data Science and Analysis, Science Publishing Group
  4. International Journal of Data Science and Analytics, Springer
  5. Data Science Journal, CoData
  6. Journals, Magazine in Analytics, Big Data, Data Mining, and Knowledge Discovery, KDnuggets.com
  7. Data Science, IOS Press
  8. EPJ Data Science, Springernature.com
  9. International Journal of Data Science (IJDS), Inderssience publishers
  10. The Journal of Finance and Data Science, KeAi Chinese Roots Global Impact
  11. SIAM Journal on Mathematics of Data Science
  12. Data Science Journal, SJR
  13. ASA Data Science Journal
  14. Scientific Data, Nature.com
  15. Data Science: Methods, Infrastructure, and Applications
  16. Statistical Analysis and Data Mining, Wiley Online Library
  17. International Journal of Population Data Science (IJPDS)
  18. Earth System Science Data
  19. Archives of Data Science, Series A
  20. Oxford Journal of Intelligent Decision and Data Science
  21. Annual Reviews of Biomedical Data Science
  22. Practical Data Science for Stats -a Peer J Collection
  23. Data Science and Pattern Recognition
  24. Frontiers of Marketing Data Science Journal, i-com Global Forum for Marketing Data and Measurement
  25. ACM Transactions on Data Science (TDS), ACM Digital Library
  26. Advances in Data Science and Adaptive Analysis, World Scientific
  27. Top Journals and conference in Data Mining and Data Science, a blog
  28. Mathematics of Computation and Data Science
  29. Journal of Data Science and Its Applications
  30. Research Data Journal for the Humanities and Social Sciences
  31. Data Technologies and Applications, emerald Publishing
  32. Data Journal Directory
  33. Harvard and Elsevier Explore Collaborations in Data Science
  34. The Role of Statistics in Data Science-An ASA Statement
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Seven Pillars of Statistical Wisdom

Proverb IX:1 “Wisdom has built her house; she has hewn out its seven pillars.”

Based on the Proverb IX:1, Stephen Stigler, a statistician and historian, published a book entitled ”The Seven Pillars of Statistical Wisdom” in March 2016 by the Harvard University Press. The seven pillars of statistical wisdom are Aggregation, Information, Likelihood, Intercomparison, Regression, Design and Residuals.

In the age of big data, it seems to many that statistics lost the battle of new gold digging wave: Data is information, hence is money. From the famous 4v (volume, velocity, variety and veracity) to 1V (Value), everyone is rushing and investigating to dig out the big V using the conventional data-driven statistical analysis. What is neglected is the validity of the conventional model selection procedure under the big data assumption. Thus, in order to make valid conclusion of the model selected, it is important to realize that what we produce should be reproducible.  For this purpose, we need to stand firm with inference, rather than just pick what is good and fool oneself. Ioannidis had realized a decade ago that most scientific discoveries are false and published a paper “Why most published research findings are false”to warn the readers to correctly use statistical inference. But it didn’t have much effect. Numerous scientific results are still published just based on empirical case studies with no assurance of reproducible property. Furthermore, the data  used for publication are kept as private assets, though most of them are federal funded projects. There is no way to reproduce the results as reported. Recently, ASA issued six principles of using p-value to prevent misuse of  p-value for false statistical inference. The USA national science foundation has adopted the recommendation from “The Mathematical Sciences in 2025” published by the National Academies Press. That is, for Big Data analysis, correct inference after massive data snooping is required. Berk et al. (2016) pointed out that the common practice in big data analysis are data-driven and the conventional statistical inference based on the selected model  is generally invalid. Thus, Post-Selection Inference is required. In their paper, the authors proposed simultaneous inference and hence suitably widening conventional confidence and retention intervals which are proven to be universally valid under all possible model selection procedures. Tibshirani‘s recent publication on Statistical Learning with Sparsity has included a chapter on “Statistical Inference” which collected the most recent development on “Post-Selection Inference”.

BAT: eCommerce in China

What is the current status of eCommerce in China? The answer is BAT.  What does BAT stand for? Well, here comes the meaning of BAT. It is about three giant eCommerce companies in China, where
B=Baidu.com
A=Alibaba.com
T=Tencent.com

A single graph in the following article tells you all about eCommerce in China. If you can read Chinese, please click the link to learn more.
http://mp.weixin.qq.com/s?__biz=MzA3NjI2NzUzNQ%3D%3D&mid=400691807&idx=1&sn=90a21c4ac5437bb168b13e1bbdc193d5&scene=5&srcid=1119kjqwfVosttvwjBFAv1UT#rd

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