Agile/Scrum Training in Redding, California

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.

How do I check if a file exists using Python?

[Mar, 28, 2014] |
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Checking to see if a file exists is a two step process in Python. Simply import the module shown below and invoke the isfile function:

import os.path
os.path.isfile(fname)

What Should I Focus On To Become Good C++ Developer?

[Apr, 01, 2020] |
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The original article was posted by Michael Veksler on Quora

A very well known fact is that code is written once, but it is read many times. This means that a good developer, in any language, writes understandable code. Writing understandable code is not always easy, and takes practice. The difficult part, is that you read what you have just written and it makes perfect sense to you, but a year later you curse the idiot who wrote that code, without realizing it was you.

The best way to learn how to write readable code, is to collaborate with others. Other people will spot badly written code, faster than the author. There are plenty of open source projects, which you can start working on and learn from more experienced programmers.

Readability is a tricky thing, and involves several aspects:

Never surprise the reader of your code, even if it will be you a year from now. For example, don’t call a function max() when sometimes it returns the minimum().
Be consistent, and use the same conventions throughout your code. Not only the same naming conventions, and the same indentation, but also the same semantics. If, for example, most of your functions return a negative value for failure and a positive for success, then avoid writing functions that return false on failure.
Write short functions, so that they fit your screen. I hate strict rules, since there are always exceptions, but from my experience you can almost always write functions short enough to fit your screen. Throughout my carrier I had only a few cases when writing short function was either impossible, or resulted in much worse code.
Use descriptive names, unless this is one of those standard names, such as i or it in a loop. Don’t make the name too long, on one hand, but don’t make it cryptic on the other.
Define function names by what they do, not by what they are used for or how they are implemented. If you name functions by what they do, then code will be much more readable, and much more reusable.
Avoid global state as much as you can. Global variables, and sometimes attributes in an object, are difficult to reason about. It is difficult to understand why such global state changes, when it does, and requires a lot of debugging.
As Donald Knuth wrote in one of his papers: “Early optimization is the root of all evil”. Meaning, write for readability first, optimize later.
The opposite of the previous rule: if you have an alternative which has similar readability, but lower complexity, use it. Also, if you have a polynomial alternative to your exponential algorithm (when N > 10), you should use that.

Use standard library whenever it makes your code shorter; don’t implement everything yourself. External libraries are more problematic, and are both good and bad. With external libraries, such as boost, you can save a lot of work. You should really learn boost, with the added benefit that the c++ standard gets more and more form boost. The negative with boost is that it changes over time, and code that works today may break tomorrow. Also, if you try to combine a third-party library, which uses a specific version of boost, it may break with your current version of boost. This does not happen often, but it may.

Don’t blindly use C++ standard library without understanding what it does - learn it. You look at std::vector::push_back() documentation at it tells you that its complexity is O(1), amortized. What does that mean? How does it work? What are benefits and what are the costs? Same with std::map, and with std::unordered_map. Knowing the difference between these two maps, you’d know when to use each one of them.

Never call new or delete directly, use std::make_unique and [cost c++]std::make_shared[/code] instead. Try to implement usique_ptr, shared_ptr, weak_ptr yourself, in order to understand what they actually do. People do dumb things with these types, since they don’t understand what these pointers are.

Every time you look at a new class or function, in boost or in std, ask yourself “why is it done this way and not another?”. It will help you understand trade-offs in software development, and will help you use the right tool for your job. Don’t be afraid to peek into the source of boost and the std, and try to understand how it works. It will not be easy, at first, but you will learn a lot.

Know what complexity is, and how to calculate it. Avoid exponential and cubic complexity, unless you know your N is very low, and will always stay low.

Learn data-structures and algorithms, and know them. Many people think that it is simply a wasted time, since all data-structures are implemented in standard libraries, but this is not as simple as that. By understanding data-structures, you’d find it easier to pick the right library. Also, believe it or now, after 25 years since I learned data-structures, I still use this knowledge. Half a year ago I had to implemented a hash table, since I needed fast serialization capability which the available libraries did not provide. Now I am writing some sort of interval-btree, since using std::map, for the same purpose, turned up to be very very slow, and the performance bottleneck of my code.

Notice that you can’t just find interval-btree on Wikipedia, or stack-overflow. The closest thing you can find is Interval tree, but it has some performance drawbacks. So how can you implement an interval-btree, unless you know what a btree is and what an interval-tree is? I strongly suggest, again, that you learn and remember data-structures.

These are the most important things, which will make you a better programmer. The other things will follow.

Google Email and Scanning Scam

[Mar, 23, 2017] |
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Like me, I believe most people go about their business never to give a serious thought about their assumed private correspondence when using Gmail to email friends, colleagues and business associates. As it turns out, your daily banter may not be so private after all. A recent article in Fortune Magazine, “Judge Rejects Google Deal Over Email Scanning” caught my attention and an immediate thought dominated my curiosity…Google email and scanning scam.

In essence, the article describes Googles’ agreement to change the way it scans incoming messages so that it no longer reads emails while they are in transit, but only when they are in someone's inbox! So, what exactly does that mean? Judge Koh, a San Francisco federal judge, said she's not so sure about that. Her ruling claims the settlement does not provide an adequate technical explanation of Google's workaround, which involves scanning in-transit emails for security purposes, and then later parsing them for advertising data. The judge also proposed a legal settlement to pay $2.2 million to lawyers, but nothing to consumers.

My interest in this story is not so much about the proposed settlements or the specific details about how Google or any of the web giants settle claims based on vague legal language. It is however, more about the naiveté of myself and perhaps many others that never question how the email scanning process really works. I wonder, do most of us really care that Gmail uses contents of our mail to display targeted ads?

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.

You cannot teach beginners top-down programming, because they don't know which end is up. C.A.R. Hoare �

other Learning Options

Software developers near Redding 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.

MoDev LA - Mobile Developers · Linux Open Source Graphics · Ultracode Meetup with Intel: Code & Tell ·

Fortune 500 and 1000 companies in California that offer opportunities for Agile/Scrum 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 Agile/Scrum 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 Agile/Scrum programming
Get your questions answered by easy to follow, organized Agile/Scrum experts
Get up to speed with vital Agile/Scrum 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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Tech Life in California

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