San Diego , California Microsoft Training

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention.

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations.

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent.

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

Game Developers' alternatives to C#

[Jun, 23, 2014] |
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With the rise of the smart phone, many people who have long seen themselves as non-gamers have began to download and play to occupy themselves throughout the day. If you're a game developer who has a history of writing your code in C#, then perhaps this still emerging market is something you should consider taking advantage of. This, however, will require the familiarization with other programming languages.

One option for moving away from the C# language is to learn Java. Java is the programming used for apps on the android platform, billions of phones run on this programming language.

If you want to break into the android market, then learning Java is an absolute must.

There are both some pros and some cons to learning java. Firstly, if you already know C# or other languages and understand how they work, then java will be relatively easy to learn due to having similar, but quite simplified, syntax to C-based languages, the class library is large and standardized, but also very well written, and you might find that it will improve the performance and portability of your creations. Not to mention, learning java opens you up to the entirety of the android app and game market, a very large and still growing market that would otherwise stay closed off to you. That's too much ad and sale money to risk missing out on.

The few cons that come with learning the language is that, when coming from other languages, the syntax may take some getting used to. This is true for most languages. The other problem is that you must be careful with the specifics of how you write your code. While java can be written in a very streamlined fashion, it's also possible to write working, but bulky, code that will slow down your programs. Practice makes perfect, and the knowledge to avoid such pitfalls within the language.

If you wish to develop for the iOS on the other hand, knowledge of Objective C is required. The most compelling reason to learn Objective C is the market that it will open you up to. According to the website AndroidAuthority.com, in the article "Google play vs. Apple app store", users of iPhones and other iOS devices are much more likely to spend money on apps rather than downloading free ones.

Though learning Objective C might be a far jump from someone who currently writes in C#, it's certainly learn-able with a little bit of practice.

How do top programmers work?

What are a few unique pieces of career advice that nobody ever mentions?

Good non-programmer jobs for people with software developer experience

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 world?s innovators, high-tech businesses and a myriad of techie start-ups. It also accounts for 1/3rd of all venture capital investments.

Knowledge is power and enthusiasm pulls the switch. ~ Steve Droke

other Learning Options

Software developers near San Diego 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.

San Francisco MariaDB group · Silicon Valley Network & Infrastructure Meetup · San Diego Perl Mongers ·

Fortune 500 and 1000 companies in California that offer opportunities for Microsoft Office 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

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the hartmann software group advantage

why take Microsoft Office 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 Microsoft Office programming
Get your questions answered by easy to follow, organized Microsoft Office experts
Get up to speed with vital Microsoft Office 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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