Rosemead , California ibmb Training

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance

Machine Learning: Is it all Hype?

[May, 17, 2018] |
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Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied. For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist, Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

Python Programming for Cisco Networking Engineers

[Jun, 25, 2018] |
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Wondering why Cisco is teaching network engineers Python in addition to their core expertise?

Yes, arguably there are many other tools available to use to automate the network without writing any code. It is also true that when code is absolutely necessary, in most companies software developers will write the code for the network engineers. However, networks are getting progressively more sophisticated and the ability for network engineers to keep up with the rate of change, scale of networks, and processing of requirements is becoming more of a challenge with traditional methodologies.

Does that mean that all network engineers have to become programmers in the future? Not completely, but having certain tools in your tool belt may be the deciding factor in new or greater career opportunities. The fact is that current changes in the industry will require Cisco engineers to become proficient in programming, and the most common programming language for this new environment is the Python programming language. Already there are more opportunities for those who can understand programming and can also apply it to traditional networking practices.

Cisco’s current job boards include a search for a Sr. Network Test Engineer and for several Network Consulting Engineers, each with "competitive knowledge" desired Python and Perl skills. Without a doubt, the most efficient network engineers in the future will be the ones who will be able to script their automated network-related tasks, create their own services directly in the network, and continuously modify their scripts.

Whether you are forced to attend or are genuinely interested in workshops or courses that cover the importance of learning topics related to programmable networks such as Python, the learning curve at the very least will provide you with an understanding of Python scripts and the ability to be able to use them instead of the CLI commands and the copy and paste options commonly used. Those that plan to cling to their CLI will soon find themselves obsolete.

As with anything new, learning a programming language and using new APIs for automation will require engineers to learn and master the skills before deploying widely across their network. The burning question is where to start and which steps to take next?

In How Do I Get Started Learning Network Programmability? Hank Preston – on the Cisco blog page suggest a three phase approach to diving into network programmability.

“Phase 1: Programming Basics

In this first phase you need to build a basic foundation in the programmability skills, topics, and technologies that will be instrumental in being successful in this journey. This includes learning basic programming skills like variables, operations, conditionals, loops, etc. And there really is no better language for network engineers to leverage today than Python. Along with Python, you should explore APIs (particularly REST APIs), data formats like JSON, XML, and YAML. And if you don’t have one already, sign up for a GitHub account and learn how to clone, pull, and push to repos.

Phase 2: Platform Topics

Once you have the programming fundamentals squared away (or at least working on squaring them away) the time comes to explore the new platforms of Linux, Docker, and “the Cloud.” As applications are moving from x86 virtualization to micro services, and now serverless, the networks you build will be extending into these new areas and outside of traditional physical network boxes. And before you can intelligently design or engineer the networks for those environments, you need to understand how they basically work. The goal isn’t to become a big bushy beard wearing Unix admin, but rather to become comfortable working in these areas.

Phase 3: Networking for Today and Tomorrow

Now you are ready to explore the details of networking in these new environments. In phase three you will dive deep into Linux, container/Docker, cloud, and micro service networking. You have built the foundation of knowledge needed to take a hard look at how networking works inside these new environments. Explore all the new technologies, software, and strategies for implementing and segmenting critical applications in the “cloud native” age and add value to the application projects.”

Community resources:

GitHub’s, PYPL Popularity of Programming Language lists Python as having grown 13.2% in demand in the last 5 years.

Python in the June 2018 TIOBE Index ranks as the fourth most popular language behind Java, C and C++.

Despite the learning curve, having Python in your tool belt is without a question a must have tool.

3 Most Important Things Non-Programmers Should Know About Programming?

[May, 11, 2018] |
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What are the three most important things non-programmers should know about programming?

Written by Brian Knapp, credit and reprint CodeCareerGenius

Since you asked for the three most important things that non-programmers should know about, and I’ve spent most of my career working with more non-programmers than programmers, I have a few interesting things that would help.

Number One - It Is Impossible To Accurately Estimate Software Projects

No matter what is tried. No matter what tool, agile approach, or magic fairy dust people try to apply to creating software… accurately predicting software project timelines is basically impossible.

There are many good reasons for this. Usually, requirements and feature ideas change on a daily/weekly basis. Often it is impossible to know what needs to be done without actually digging into the code itself. Debugging and QA can take an extraordinary amount of time.

And worst of all…

Project Managers are always pushing for shorter timelines. They largely have no respect for reality. So, at some point they are given estimates just to make them feel better about planning.

No matter how much planning and estimation you do, it will be wrong. At best it will be directionally correct +/- 300% of what you estimated. So, a one year project could actually take anywhere between 0 and 5 years, maybe even 10 years.

If you think I’m joking, look at how many major ERP projects that go over time and over budget by many years and many hundreds of millions of dollars. Look at the F-35 fighter jet software issues.

Or in the small, you can find many cases where a “simple bug fix” can take days when you thought it was hours.

All estimates are lies made up to make everyone feel better. I’ve never met a developer or manager who could accurately estimate software projects even as well as the local weatherman(or woman) predicts the weather.

Number Two - Productivity Is Unevenly Distributed

What if I told you that in the average eight hour work day the majority of the work will get done in a 30 minute timeframe? Sound crazy?

Well, for most programmers there is a 30–90 minute window where you are extraordinarily productive. We call this the flow state.

Being in the flow state is wonderful and amazing. It often is where the “magic” of building software happens.

Getting into flow can be difficult. It’s akin to meditation in that you have to have a period of uninterrupted focus of say 30 minutes to “get in” the flow, but a tiny interruption can pull you right out.

Now consider the modern workplace environment. Programmers work in open office environments where they are invited to distract each other constantly.

Most people need a 1–2 hour uninterrupted block to get 30–90 minutes of flow.

Take the 8 hour day and break it in half with a lunch break, and then pile in a few meetings and all of a sudden you are lucky to get one decent flow state session in place.

That is why I say that most of the work that gets done happens in a 30 minute timeframe. The other 7–8 hours are spent being distracted, answering email, going to meetings, hanging around the water cooler, going to the bathroom, and trying to remember what you were working on before all these distractions.

Ironically, writers, musicians, and other creative professionals have their own version of this problem and largely work alone and away from other people when they are creating new things.

Someday the programming world might catch on, but I doubt it.

Even if this became obvious, it doesn’t sit well with most companies to think that programmers would be paid for an 8 hour day and only be cranking out code for a few hours on a good day. Some corporate middle manager would probably get the bright idea to have mandatory flow state training where a guru came in and then there would be a corporate policy from a pointy haired boss mandating that programmers are now required to spend 8 hours a day in flow state and they must fill out forms to track their time and notify their superiors of their flow state activities, otherwise there would be more meetings about the current flow state reports not being filed correctly and that programmers were spending too much time “zoning out” instead of being in flow.

Thus, programmers would spent 7–8 hours a day pretending to be in flow state, reporting on their progress, and getting all their work done in 30 minutes of accidental flow state somewhere in the middle of all that flow state reporting.

If you think I’m joking about this, I’m not. I promise you this is what would happen to any company of more than 2 employees. (Even the ones run by programmers.)

Number Three - It Will Cost 10x What You Think

Being a programmer, I get a lot of non-programmers telling me about their brilliant app ideas. Usually they want me to build something for free and are so generous as to pay me up to 5% of the profits for doing 100% of the work.

Their ideas are just that good.

Now, I gently tell them that I’m not interested in building anything for free.

At that point they get angry, but a few ask how much it will cost. I give them a reasonable (and very incorrect) estimate of what it would cost to create the incredibly simple version of their app idea.

Let’s say it’s some number like $25,000.

They look at me like I’m a lunatic, and so I explain how much it costs to hire a contract programmer and how long it will actually take. For example’s sake let’s say it is $100/hr for 250 hours.

To be clear, these are made up numbers and bad estimates (See Number One for details…)

In actuality, to build the actual thing they want might cost $250,000, or even $2,500,000 when it’s all said and done.

Building software can be incredibly complex and expensive. What most people can’t wrap their head around is the fact that a company like Google, Apple, or Microsoft has spent BILLIONS of dollars to create something that looks so simple to the end user.

Somehow, the assumption is that something that looks simple is cheap and fast to build.

Building something simple and easy for the end user is time consuming and expensive. Most people just can’t do it.

So, the average person with a brilliant app idea thinks it will cost a few hundred or maybe a few thousand dollars to make and it will be done in a weekend is so off the mark it’s not worth considering their ideas.

And programmers are too eager to play along with these bad ideas (by making bad estimates and under charging for their time) that this notion is perpetuated to the average non-programmer.

So, a good rule of thumb is that software will cost 10 times as much as you think and take 10 times as long to finish.

And that leads to a bonus point…

BONUS - Software Is Never Done

Programmers never complete a software project, they only stop working on it. Software is never done.

I’ve worked at many software companies and I’ve never seen a software project “completed”.

Sure, software gets released and used. But, it is always changing, being updated, bugs get fixed, and there are always new customer requests for features.

Look at your favorite software and you’ll quickly realize how true this is. Facebook, Instagram, Google Search, Google Maps, GMail, iOS, Android, Windows, and now even most video games are never done.

There are small armies of developers just trying to keep all the software you use every day stable and bug free. Add on the fact that there are always feature requests, small changes, and new platforms to deal with, it’s a treadmill.

So, the only way out of the game is to stop working on software. At that point, the software begins to decay until it is no longer secure or supported.

Think about old Windows 3.1 software or maybe old Nintendo Cartridge video games. The current computers and video game consoles don’t even attempt to run that software anymore.

You can’t put an old video game in your new Nintendo Switch and have it “just work”. That is what happens when you think software is done.

When programmers stop working on software the software starts to die. The code itself is probably fine, but all the other software keeps moving forward until your software is no longer compatible with the current technology.

So, those are the four most important things that non-programmers should know about programming. I know you asked for only three, so I hope the bonus was valuable to you as well.

Tech Life in California

Largely influenced by several immigrant populations California has experienced several technological, entertainment and economic booms over the years. As for technology, Silicon Valley, in the southern part of San Francisco is an integral part of the worldâ€™s innovators, high-tech businesses and a myriad of techie start-ups. It also accounts for 1/3rd of all venture capital investments.

A program which perfectly meets a lousy specification is a lousy program. Cem Kaner

other Learning Options

Software developers near Rosemead have ample opportunities to meet like minded techie individuals, collaborate and expend their career choices by participating in Meet-Up Groups. The following is a list of Technology Groups in the area.

Node Summit · Wikipedia Tech · eLearning Experienced Developers (eLEDs) ·

Fortune 500 and 1000 companies in California that offer opportunities for IBM developers

Company Name	City	Industry	Secondary Industry
Mattel, Inc.	El Segundo	Retail	Sporting Goods, Hobby, Book, and Music Stores
Spectrum Group International, Inc.	Irvine	Retail	Retail Other
Chevron Corp	San Ramon	Energy and Utilities	Gasoline and Oil Refineries
Jacobs Engineering Group, Inc.	Pasadena	Real Estate and Construction	Construction and Remodeling
eBay Inc.	San Jose	Software and Internet	E-commerce and Internet Businesses
Broadcom Corporation	Irvine	Computers and Electronics	Semiconductor and Microchip Manufacturing
Franklin Templeton Investments	San Mateo	Financial Services	Investment Banking and Venture Capital
Pacific Life Insurance Company	Newport Beach	Financial Services	Insurance and Risk Management
Tutor Perini Corporation	Sylmar	Real Estate and Construction	Construction and Remodeling
SYNNEX Corporation	Fremont	Software and Internet	Data Analytics, Management and Storage
Core-Mark International Inc	South San Francisco	Manufacturing	Food and Dairy Product Manufacturing and Packaging
Occidental Petroleum Corporation	Los Angeles	Manufacturing	Chemicals and Petrochemicals
Yahoo!, Inc.	Sunnyvale	Software and Internet	Software and Internet Other
Edison International	Rosemead	Energy and Utilities	Gas and Electric Utilities
Ingram Micro, Inc.	Santa Ana	Computers and Electronics	Consumer Electronics, Parts and Repair
Safeway, Inc.	Pleasanton	Retail	Grocery and Specialty Food Stores
Gilead Sciences, Inc.	San Mateo	Healthcare, Pharmaceuticals and Biotech	Pharmaceuticals
AECOM Technology Corporation	Los Angeles	Real Estate and Construction	Architecture,Engineering and Design
Reliance Steel and Aluminum	Los Angeles	Manufacturing	Metals Manufacturing
Live Nation, Inc.	Beverly Hills	Media and Entertainment	Performing Arts
Advanced Micro Devices, Inc.	Sunnyvale	Computers and Electronics	Semiconductor and Microchip Manufacturing
Pacific Gas and Electric Corp	San Francisco	Energy and Utilities	Gas and Electric Utilities
Electronic Arts Inc.	Redwood City	Software and Internet	Games and Gaming
Oracle Corporation	Redwood City	Software and Internet	Software and Internet Other
Symantec Corporation	Mountain View	Software and Internet	Data Analytics, Management and Storage
Dole Food Company, Inc.	Thousand Oaks	Manufacturing	Food and Dairy Product Manufacturing and Packaging
CBRE Group, Inc.	Los Angeles	Real Estate and Construction	Real Estate Investment and Development
First American Financial Corporation	Santa Ana	Financial Services	Financial Services Other
The Gap, Inc.	San Francisco	Retail	Clothing and Shoes Stores
Ross Stores, Inc.	Pleasanton	Retail	Clothing and Shoes Stores
Qualcomm Incorporated	San Diego	Telecommunications	Wireless and Mobile
Charles Schwab Corporation	San Francisco	Financial Services	Securities Agents and Brokers
Sempra Energy	San Diego	Energy and Utilities	Gas and Electric Utilities
Western Digital Corporation	Irvine	Computers and Electronics	Consumer Electronics, Parts and Repair
Health Net, Inc.	Woodland Hills	Healthcare, Pharmaceuticals and Biotech	Healthcare, Pharmaceuticals, and Biotech Other
Allergan, Inc.	Irvine	Healthcare, Pharmaceuticals and Biotech	Biotechnology
The Walt Disney Company	Burbank	Media and Entertainment	Motion Picture and Recording Producers
Hewlett-Packard Company	Palo Alto	Computers and Electronics	Consumer Electronics, Parts and Repair
URS Corporation	San Francisco	Real Estate and Construction	Architecture,Engineering and Design
Cisco Systems, Inc.	San Jose	Computers and Electronics	Networking Equipment and Systems
Wells Fargo and Company	San Francisco	Financial Services	Banks
Intel Corporation	Santa Clara	Computers and Electronics	Semiconductor and Microchip Manufacturing
Applied Materials, Inc.	Santa Clara	Computers and Electronics	Semiconductor and Microchip Manufacturing
Sanmina Corporation	San Jose	Computers and Electronics	Semiconductor and Microchip Manufacturing
Agilent Technologies, Inc.	Santa Clara	Telecommunications	Telecommunications Equipment and Accessories
Avery Dennison Corporation	Pasadena	Manufacturing	Paper and Paper Products
The Clorox Company	Oakland	Manufacturing	Chemicals and Petrochemicals
Apple Inc.	Cupertino	Computers and Electronics	Consumer Electronics, Parts and Repair
Amgen Inc	Thousand Oaks	Healthcare, Pharmaceuticals and Biotech	Biotechnology
McKesson Corporation	San Francisco	Healthcare, Pharmaceuticals and Biotech	Pharmaceuticals
DIRECTV	El Segundo	Telecommunications	Cable Television Providers
Visa, Inc.	San Mateo	Financial Services	Credit Cards and Related Services
Google, Inc.	Mountain View	Software and Internet	E-commerce and Internet Businesses

training details locations, tags and why hsg

the hartmann software group advantage

why take IBM Training with the hartmann software group?

A successful career as a software developer or other IT professional requires a solid understanding of software development processes, design patterns, enterprise application architectures, web services, security, networking and much more. The progression from novice to expert can be a daunting endeavor; this is especially true when traversing the learning curve without expert guidance. A common experience is that too much time and money is wasted on a career plan or application due to misinformation.

The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:

Learn from the experts.
1. We have provided software development and other IT related training to many major corporations in California since 2002.
2. Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
Discover tips and tricks about IBM programming
Get your questions answered by easy to follow, organized IBM experts
Get up to speed with vital IBM programming tools
Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
Prepare to hit the ground running for a new job or a new position
See the big picture and have the instructor fill in the gaps
We teach with sophisticated learning tools and provide excellent supporting course material
Books and course material are provided in advance
Get a book of your choice from the HSG Store as a gift from us when you register for a class
Gain a lot of practical skills in a short amount of time
We teach what we know…software
We care…

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IBM Training Classes in Rosemead, California

Learn IBM in Rosemead, California and surrounding areas via our hands-on, expert led courses. All of our classes either are offered on an onsite, online or public instructor led basis. Here is a list of our current IBM related training offerings in Rosemead, California: IBM Training

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Tech Life in California

training details locations, tags and why hsg

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