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I did a quick search using our brand new search tool (powered by Google), and here's what I found for a few popular keywords. The number represents the number of pages on the DSC network, indexed by Google, for each keyword. Currently, only the top 100 pages, for each keyword, are displayed.

Top keywords

If you click on a keyword, you will find the DSC pages indexed by Google, for the keyword in question. Note that Google tends to favor old articles over new ones, when ranking pages. This is probably because older articles have received more page views. Click here to learn about a methodology that addresses this issue. Also, read the subsection scoring algorithm, in section 1 in this article, for a solution that put customized weights both on time and popularity, to rank categorized (indexed) articles.  

I wish there was an option to also sort by date, rather than by relevancy, in the Google search box.

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Thanks.  I really found that Python-SQL-R is pervasive with the younger analysts and tool builders in semiconductor industry where I have worked for decades..watching the constant change of languages, proprietary analytical systems like SAS, and DBMS's like Oracle and open-source adventures like PostGres pop up in my clients world in spite of the Microsoft-only rules of their IT folks.  In old days, IT proclaimed IBM only and we instead chose Vax Clusters for manufacturing MES systems.  IT pronounce MS Office, and we chose JMP and augmented it with R (I am not the programmer, just the project manager that had to corral the local talent to help my clients..and I found them in engineering ranks rather than IT ranks most of the time.

Our industry has been data rich for decades.  Big data was in our lives since 1960's but was only integrated in 1980's so we had many years of flat file repositories, and streaming data protocols like SECS II and GEM etc for listening to our million dollar machines. Just when I turned 75 I found Linked In discussion groups that let me fill in many gaps in my thinking, and the new Free Online Courses (MOOCS) like the Python-notebook based linear algebra course from Texas, or the many Macroeconomics courses from best universities that finally seem to be trying to add technology change into their "models" about 4 decades too late to be useful for most purposes.  

Modems are finally almost gone, and the MacIntosh model is finally recognized as better than Windows for normal people, and Linux has taken over the world of URL support....but existing on hundreds of blade servers on IBM mainframes..amazingly and the stuffed elephant Hadoop deals with the social media data mess so we manufacturing people can get better prices for Oracle on our old VMS systems that may never go away on factory floors.  

Mix and match, old and older and bleeding edge all co-existing.  That is the semiconductor mfg world.  

So thanks for help us find a way to communicate with the "next generation" of data engineers and scientists.  Samsung is spending 14 billion on a 10nm fab in Korea over next year or three, and I can only imagine the software maze they will have to support and augment for next decade.  Intel's last big fab is still sitting idle in Arizona, due perhaps to mgmt ignoring mobile low power needs and then smart phone and now Internet of Things and low volume high performance processors to catch up with the IBM Power architecture that has ruled its niche for decades. Intel may be having analysis paralysis given all the new product options.  But they will come around again.  

Please do all you can to help our diverse industry, from MEMS to Bleeding edge memory and microprocessors, from IoT microcontrollers to analog and mixed signal and power discretes with IC drivers in 2 chip modules...all of which are tested and that data is really messy when aligned with the factory tool history data and product design changes. Only the bleeding edge will be consolidated.  The legacy factories will continue to retool and change focus.  But the mix will survive.

And the SYSTEMS are like a museum of all things once innovative. 

Some tips to we are starting in the data engineering field?

Michael Clayton said:

Thanks.  I really found that Python-SQL-R is pervasive with the younger analysts and tool builders in semiconductor industry where I have worked for decades..watching the constant change of languages, proprietary analytical systems like SAS, and DBMS's like Oracle and open-source adventures like PostGres pop up in my clients world in spite of the Microsoft-only rules of their IT folks.  In old days, IT proclaimed IBM only and we instead chose Vax Clusters for manufacturing MES systems.  IT pronounce MS Office, and we chose JMP and augmented it with R (I am not the programmer, just the project manager that had to corral the local talent to help my clients..and I found them in engineering ranks rather than IT ranks most of the time.

Our industry has been data rich for decades.  Big data was in our lives since 1960's but was only integrated in 1980's so we had many years of flat file repositories, and streaming data protocols like SECS II and GEM etc for listening to our million dollar machines. Just when I turned 75 I found Linked In discussion groups that let me fill in many gaps in my thinking, and the new Free Online Courses (MOOCS) like the Python-notebook based linear algebra course from Texas, or the many Macroeconomics courses from best universities that finally seem to be trying to add technology change into their "models" about 4 decades too late to be useful for most purposes.  

Modems are finally almost gone, and the MacIntosh model is finally recognized as better than Windows for normal people, and Linux has taken over the world of URL support....but existing on hundreds of blade servers on IBM mainframes..amazingly and the stuffed elephant Hadoop deals with the social media data mess so we manufacturing people can get better prices for Oracle on our old VMS systems that may never go away on factory floors.  

Mix and match, old and older and bleeding edge all co-existing.  That is the semiconductor mfg world.  

So thanks for help us find a way to communicate with the "next generation" of data engineers and scientists.  Samsung is spending 14 billion on a 10nm fab in Korea over next year or three, and I can only imagine the software maze they will have to support and augment for next decade.  Intel's last big fab is still sitting idle in Arizona, due perhaps to mgmt ignoring mobile low power needs and then smart phone and now Internet of Things and low volume high performance processors to catch up with the IBM Power architecture that has ruled its niche for decades. Intel may be having analysis paralysis given all the new product options.  But they will come around again.  

Please do all you can to help our diverse industry, from MEMS to Bleeding edge memory and microprocessors, from IoT microcontrollers to analog and mixed signal and power discretes with IC drivers in 2 chip modules...all of which are tested and that data is really messy when aligned with the factory tool history data and product design changes. Only the bleeding edge will be consolidated.  The legacy factories will continue to retool and change focus.  But the mix will survive.

And the SYSTEMS are like a museum of all things once innovative. 

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