Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

June 17, 2017

If You Don’t Think “Working For The Man” Is All That Weird

Filed under: Artificial Intelligence,Finance Services,Jobs,Machine Learning — Patrick Durusau @ 2:29 pm

J.P.Morgan’s massive guide to machine learning and big data jobs in finance by Sara Butcher.

From the post:

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it. In 2017, it’s all about machine learning and big data. If you can get in here, your future in finance will be assured.

J.P. Morgan’s quantitative investing and derivatives strategy team, led Marko Kolanovic and Rajesh T. Krishnamachari, has just issued the most comprehensive report ever on big data and machine learning in financial services.

Titled, ‘Big Data and AI Strategies’ and subheaded, ‘Machine Learning and Alternative Data Approach to Investing’, the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don’t they’ll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

At 280 pages, the report is too long to cover in detail, but we’ve pulled out the most salient points for you below.
…

How important is Sarah’s post and the report by J.P. Morgan?

Let put it this way: Sarah’s post is the first business type post I have saved as a complete webpage so I can clean it up and print without all the clutter. This year. Perhaps last year as well. It’s that important.

Sarah’s post is a quick guide to the languages, talents and tools you will need to start “working for the man.”

It that catches your interest, then Sarah’s post is pure gold.

Enjoy!

PS: I’m still working on a link for the full 280 page report. The switchboard is down for the weekend so I will be following up with J.P. Morgan on Monday next.

December 26, 2014

How to Get Noticed and Hired as a Data Analyst

Filed under: Data Analysis,Jobs — Patrick Durusau @ 3:53 pm

How to Get Noticed and Hired as a Data Analyst by Cheng Han Lee.

From the post:

So, you’ve learned the skills needed to become a data analyst. You can write queries to retrieve data from a database, scour through user behavior to discover rich insights, and interpret the complex results of A/B tests to make substantive product recommendations.

In short, you feel confident about embarking full steam ahead on a career as a data analyst. The next question is, how do you get noticed and actually hired by recruiters or hiring managers?
…

Whether you are breaking into data analytics or looking for another position, Cheng Han Lee’s advice will stand you in good stead in the coming new year!

Enjoy!

November 11, 2011

From Småland’s Woods to Silicon Valley

Filed under: Jobs,Topic Maps — Patrick Durusau @ 7:39 pm

From Småland’s Woods to Silicon Valley

Peter Neubauer, think www.neo4j.org, www.ops4j.org and www.qi4j.org, on entrepreneurship.

From the description:

A company is like a baby. And it takes as long to allow it to grow. Don’t fool yourself and be prepared for a journey from Påskallavik to Menlo Park. It takes a village to raise a child, and a community to grow a company.

The scenes from some of the slides beg for further explanation. 😉

But it is a useful slide deck for anyone who wants to form a successful company. It is easy to form the other kind, no instructions are needed.

Peter concludes with pointers to a number of resources that you will find useful in your journey to a successful company.

Enjoy!

PS: One resource Peter points to is: 5 Things to do when you’re unemployed. Hint: It’s not job hunting. by Penelope Trunk. Good advice and highly amusing. Penelope’s blog is “Advice at the intersection of work and life.” (Those are different?) Anyway, when you are not writing, running, breathing topic maps, you will enjoy her blog.

IT’s Next Hot Job: Hadoop Guru

Filed under: Hadoop,Jobs — Patrick Durusau @ 7:37 pm

IT’s Next Hot Job: Hadoop Guru by Doug Henschen InformationWeek.

“We’re hiring, and we’re paying 10% more than the other guys.”

Those were the first words from Larry Feinsmith, managing director, office of the CIO, at JPMorgan Chase, in his Tuesday keynote address at Hadoop World in New York. Who JPMorgan Chase is hiring, specifically, are people with Hadoop skills, so Feinsmith was in the right place. More than 1,400 people were in the audience, and attendee polls indicated that at least three quarters of their organizations are already using Hadoop, the open source big data platform.

The “and we’re paying 10% more” bit was actually Feinsmith’s ad-libbed follow-on to the previous keynoter, Hugh Williams, VP of search, experience, and platforms at eBay. After explaining eBay’s Hadoop-based Cassini search engine project, Williams said his company is hiring Hadoop experts to help build out and run the tool.

Feinsmith’s core message was that Hadoop is hugely promising, maturing quickly, and might overlap the functionality of relational databases over the next three years. In fact, Hadoop World 2011 was a coming-out party of sorts, as it’s now clear that Hadoop will matter to more than just Web 2.0 companies like eBay, Facebook, Yahoo, AOL, and Twitter. A straight-laced financial giant with more than 245,000 employees, 24 million checking accounts, 5,500 branches, and 145 million credit cards in use, JPMorgan Chase lends huge credibility to that vision.

JP Morgan Chase has 25,000 IT employees, and it spends about $8 billion on IT each year–$4 billion on apps and $4 billion on infrastructure. The company has been working with Hadoop for more than three years, and it’s easy to see why. It has 150 petabytes (with a “p”) of data online, generated by trading operations, banking activities, credit card transactions, and some 3.5 billion logins each year to online banking and brokerage accounts.

The benefits of Hadoop? Massive scalability, schema-free flexibility to handle a variety of data types, and low cost. Hadoop systems built on commodity hardware now cost about $4,000 per node, according to Cloudera, the Hadoop enterprise support and management software provider (and the organizer and host of Hadoop World). With the latest nodes typically having 16 compute cores and 12 1-terabyte or 2-terabyte drives, that’s massive storage and compute capacity at a very low cost. In comparison, aggressively priced relational data warehouse appliances cost about $10,000 to $12,000 per terabyte.

OK, but what does Hadoop not have out of the box? Can you say cross-domain subject or data semantics? Some “expert – (insert your name)” is going to have to supply the semantics. Have to know the Hadoop ecosystem, but having a firm background in mapping between semantic domains will make you a semantic “top gun.”

November 4, 2011

The number one trait you want in a data scientist

Filed under: Data Science,Jobs — Patrick Durusau @ 6:11 pm

The number one trait you want in a data scientist by Audrey Watters.

Description: DJ Patil on the traits of data scientists and how data science will evolve within companies.

From the post:

“Data scientist” is an on-the-rise job title, but what are the skills that make a good one? And how can both data scientists and the companies they work for make sure data-driven insights become actionable?

In a recent interview, DJ Patil (@dpatil), formerly the chief scientist at LinkedIn and now the data scientist in residence at Greylock Partners, discussed common data scientist traits and the challenges that those in the profession face getting their work onto company roadmaps.

An interesting read and good interview.

I think the #1 trait will surprise you.

Not all topic map authors are data scientists but it would be hard to write a good topic map and not be a data scientist.

Is this terminology that we want to adopt for ourselves in the topic map community? It is popular and might help on resumes and job applications.

Big Data : Case Studies, Best Practices and Why America should care

Filed under: BigData,Jobs,Marketing — Patrick Durusau @ 6:10 pm

Big Data : Case Studies, Best Practices and Why America should care by Themos Kalafatis.

From the post:

We know that Knowledge is Power. Due to Data Explosion more Data Scientists will be needed and being a Data Scientist becomes increasingly a “cool” profession. Needless to say that America should be preparing for the increased need for Predictive Analytics professionals in Research and Businesses.

Being able to collect, analyze and extract knowledge from a huge amount of Data is not only about Businesses being able to make the right decisions but also critical for a Country as a whole. The more efficient and fast this cycle is, the better for the Country that puts Analytics to work.

This Blog post is actually about the words and phrases being used for this post : All words and phrases on the title of the post (and the introductory text) were carefully selected to produce specific thoughts which can be broken down in three parts :

  • Being a Data Scientist has high value.
  • “Case Studies” and “Best Practices” communicate to readers successful applications and knowledge worthwhile reading.
  • “America should”. This phrase obviously creates specific emotions and feelings to Americans.

Being a “cool” profession or even a member of a “cool” profession doesn’t guarantee good results. Whatever tools you are using, good analytical skills have to lie behind their use. I think topic maps have a role to play in managing “big data” and being a tool that is reached for early and often.

October 29, 2011

Failing at Google Interviews

Filed under: Jobs — Patrick Durusau @ 7:28 pm

Failing at Google Interviews by Alex Bowe.

Not strictly a topic map topic except that topic map practitioners do apply for jobs, including at places like Google. I thought the advice in this article important enough to pass along for anyone in the topic map audience.

The one thing Alex did not mention is the proposition:

Not being hired by Google is their loss.

Proof is left up to the reader.

August 26, 2011

Information Systems Category Editor Needed for Computing Reviews

Filed under: Jobs — Patrick Durusau @ 6:27 pm

Information Systems Category Editor Needed for Computing Reviews

Anyone interested in topic maps is likely to be interested in positions such as this one. Your chance to contribute back to the community.

Computing Reviews, the post-publication review and comment journal of ACM, is seeking a volunteer editor interested in serving as category editor for the information systems area (encompassing models & principles, database management, information storage & retrieval, and information systems applications).

The qualified candidate will be prepared to check written reviews of already-published items for quality, and the classification terms from ACM’s CCS for accuracy, as well as use a Web-based editing system to make any suggested changes to the CCS terms or to the review itself. Most importantly, the category editor provides feedback to the review’s author so that existing guidelines are met. He or she also works with staff and reviewers to develop additional features for the publication. This is an opportunity for an enthusiast in the discipline to use specialist knowledge to contribute to a product that helps others navigate and sift through the computing literature. The time commitment is approximately 1-2 hours per week.

It may just be boiler-plate but I would be most interested in:

He or she also works with staff and reviewers to develop additional features for the publication.

The first feature I would like to see is a common login for my ACM + Digital Library account and Computing Reviews.

The second feature would be citation networks for articles.

What features would you like to see?

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