Top 30 LinkedIn Groups for Analytics, Big Data,
Data Mining, and Data Science

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The groups with the largest number of members (as of April 20, 2013) are:

• Advanced Business Analytics, Data Mining and Predictive Modeling, 76,150
• Big Data / Analytics / Strategy / FP&A / S&OP ..., 60,474
• Business Analytics, 39,851
• Big Data and Analytics, 39,560
• Data Mining, Statistics, Big Data, and Data Visualization, 21,705

We also looked at how active is each group, as measured in the number of comments and discussions per week. We measured these over 4 weeks, from Mar 11 to Apr 7, 2013. Thanks to KDnuggets intern Anmol Rajpurohit for manually collecting the comments and discussions numbers from the activity graphs.

Interestingly, posting discussions is 50% more common than commenting - we found that over all groups, the average number of discussions was 2.4/week per 1000 members, while average number of comments was 1.6/week per 1000 members.

The chart below shows average comments/week vs average discussions/week for all 30 groups, with a circle size proportional to group size and darker color corresponding to faster growth. Group name abbreviations and full stats in the table below.

We note that a large cluster of groups has about 1 comment/week per 1000 members. A second cluster includes two biggest groups and has about 2 comments/1000 members. A third cluster has 5 groups which are very active relative to their size. Two more groups stand out - NGMR has the highest rate of comments, and PAN has the highest rate of discussions.

Below are the 5 most active groups, with Activity = Comments + Discussions. The numbers are counts per week, averaged over 4 weeks, from Mar 11 to Apr 7, 2013.

LinkedIn Group	Members (Apr 20)	Founded	Members /Month	Comments /1k memb	Discussions /1k memb
Advanced Business Analytics, Data Mining and Predictive Modeling (Adv BA)	76,150	28-Sep-07	1,125	2.18	1.82
Big Data / Analytics / Strategy / FP&A / S&OP ... (Big Data ASFSSP)	60,474	20-Feb-09	1,194	2.11	1.88
Business Analytics (Biz A)	39,851	3-Mar-08	638	1.19	1.12
Big Data and Analytics (Big Data & A)	39,560	1-Mar-12	2,860	3.64	1.82
Data Mining, Statistics, Big Data, and Data Vis (DM Stat)	21,705	25-Jul-08	376	2.63	3.87
Next Gen Market Research (NGMR)	21,277	26-Sep-07	314	4.69	2.34
Business Intelligence & Analytics Group (BI&A)	20,000	6-Jan-08	311	1.25	1.73
Global Analytics Network (Global A)	18,130	23-May-08	303	0.43	2.89
BIG DATA Professionals - Architects Scientists Analytics Experts (BD Prof)	17,808	1-Sep-08	316	2.92	4.88
SAS Analytics & BI (cl) (SAS A&BI)	16,858	25-Jun-08	287	0.96	1.52
Machine Learning Connection (cl) (ML Conn)	12,780	12-Mar-08	206	0.78	0.7
Actuary / Actuarial, Predictive Modeling, Data Mining, and Statistics (Actuary)	12,374	24-Sep-08	222	0.12	1.93
Research Methods and Analytics (RMA)	11,569	10-Apr-09	236	2.13	1.11
SAS & Analytics Users (SAS Users)	11,567	13-Apr-08	189	0.65	1.21
Text Analytics (Text A)	11,447	2-Jun-08	193	0.99	1.37
Pattern Recognition, Data Data Mining, Machine Intelligence (cl) (PR)	10,846	2-Oct-08	196	2.26	0.49
Data Science Central (DSC)	9,568	10-Feb-12	660	0.56	3.74
Advanced Analytics (cl) (Adv A)	7,809	11-Jan-09	150	0.75	3.4
Data & Text Analytics Professionals (D&TA Prof)	6,926	24-Sep-07	102	1.02	2.22
Visual Analytics (Visual)	6,094	31-Mar-08	99	1.19	2.42
Lavastorm Analytics Community Group (Lavastorm)	5,658	17-Apr-11	231	0.36	2.69
Advanced Analytics, Predictive Modeling & Statistical Analyses (cl) (Adv AP)	5,464	10-Jul-08	94	2.47	0.28
Predictive Analytics Network (PAN) (PAN)	4,692	16-Mar-09	94	0.97	6.71
Data Scientists (Dscientists)	3,621	8-Jun-09	77	3.48	6.16
Predictive Model Markup Language (PMML) (PMML)	3,236	24-Sep-09	74	0.23	0.16
Data Mining Technology (cl) (DMT)	3,213	20-Jun-08	55	0.63	1.19
KDnuggets Analytics and Data Mining (KDnuggets)	2,939	4-Feb-08	46	3.45	5.52
Healthcare Data Mining and Modeling (Healthcare)	2,463	11-Jul-08	42	0.21	0
RDataMining (RDM)	2,366	30-Aug-11	118	3.47	3.25
Business Intelligence Tools (BI Tools)	2,289	2-Jul-08	39	0	3.97
Average	15624	22-Dec-08	362	1.6	2.4

Comments from the Web

In Data Science Central:

Eli Y. Kling

An excellent bit of insightful analysis. Good on you. My first reaction was to join all the groups listed. But that is missing the very good bit of insight presented here. Personally I do not care how many posts appear as there is a great deal of waffle and overlap. I will go for the one that generate conversations. Perhaps the next step is to look at the length of the threads.

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