Oracle, MySQL, Cassandra, Hadoop Database Training Classes in West Haven, Connecticut
Learn Oracle, MySQL, Cassandra, Hadoop Database in West Haven, Connecticut 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 Oracle, MySQL, Cassandra, Hadoop Database related training offerings in West Haven, Connecticut: Oracle, MySQL, Cassandra, Hadoop Database Training
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- RED HAT ENTERPRISE LINUX AUTOMATION WITH ANSIBLE
15 September, 2025 - 18 September, 2025 - Fast Track to Java 17 and OO Development
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Blog Entries publications that: entertain, make you think, offer insight
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.
Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.
Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.
Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.
The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.
Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.
Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.
Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.
The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.
Related:
What Are The 10 Most Famous Software Programs Written in Python?
Technology is wonderful. It helps us run our businesses and connects us to the world. But when computer problems get in the way of getting what you need to get done, you can go from easygoing to mad-as-a-hornet in 3 seconds flat. Before you panic or give in to the temptation to throw your computer out the window, try these easy fixes.
5 Common Computer Problems
- Sluggish PC
A sluggish PC often means low disk space caused by an accumulation of temporary Internet files, photos, music, and downloads. One of the easiest fixes for a slow PC is to clear your cache.
The way you’ll do this will depend on the Internet browser you use:
- Chrome– On the top right-hand side of the screen, you’ll see what looks like a window blind. Click on that. Click on ‘History’ and hit ‘Clear Browsing Data’.
- Safari– On the upper left-hand side, you’ll see a tab marked ‘Safari’. Click on that. Scroll down and hit ‘Empty Cache’.
- Internet Explorer– Click on ‘Tools’ and scroll down to ‘Internet Options’. Under ‘Browsing History’ click ‘Delete’. Delete files and cookies.
- FireFox – At the top of the window click ‘Tools’ then go to ‘Options’. Select the ‘Advanced’ panel and click on the ‘Network’ tab. Go to ‘Cached Web Content’ and hit ‘Clear Now’.
Creating an enum in Python prior to Python 3.4 was accomplished as follows:
def enum(**enums)::
return type('Enum',(),enums)
then use as:
Animals=enum(Dog=1,Cat=2)
and accessed as:
Animals.Dog
The new version can be created as follows:
from enum import Enum
class Animal(Enum):
Dog=1
Cat=2
Tech Life in Connecticut
Company Name | City | Industry | Secondary Industry |
---|---|---|---|
Stanley Black and Decker, Inc. | New Britain | Manufacturing | Tools, Hardware and Light Machinery |
EMCOR Group, Inc. | Norwalk | Energy and Utilities | Energy and Utilities Other |
The Hartford Financial Services Group Inc. | Hartford | Financial Services | Insurance and Risk Management |
Crane Co. | Stamford | Manufacturing | Tools, Hardware and Light Machinery |
Cenveo. Inc. | Stamford | Business Services | Business Services Other |
Amphenol Corporation | Wallingford | Computers and Electronics | Semiconductor and Microchip Manufacturing |
W. R. Berkley Corporation | Greenwich | Financial Services | Insurance and Risk Management |
Silgan Holdings Inc. | Stamford | Manufacturing | Manufacturing Other |
Hubbell Incorporated | Shelton | Manufacturing | Concrete, Glass, and Building Materials |
IMS Health Incorporated | Danbury | Business Services | Management Consulting |
CIGNA Corporation | Hartford | Financial Services | Insurance and Risk Management |
Chemtura Corp. | Middlebury | Manufacturing | Chemicals and Petrochemicals |
Harman International Industries, Inc | Stamford | Computers and Electronics | Audio, Video and Photography |
United Rentals, Inc. | Greenwich | Real Estate and Construction | Construction Equipment and Supplies |
The Phoenix Companies, Inc. | Hartford | Financial Services | Investment Banking and Venture Capital |
Magellan Health Services, Inc. | Avon | Healthcare, Pharmaceuticals and Biotech | Healthcare, Pharmaceuticals, and Biotech Other |
Terex Corporation | Westport | Manufacturing | Heavy Machinery |
Praxair, Inc. | Danbury | Manufacturing | Chemicals and Petrochemicals |
Knights of Columbus | New Haven | Non-Profit | Social and Membership Organizations |
Xerox Corporation | Norwalk | Computers and Electronics | Office Machinery and Equipment |
Starwood Hotels and Resorts Worldwide, Inc. | Stamford | Travel, Recreation and Leisure | Hotels, Motels and Lodging |
United Technologies Corporation | Hartford | Manufacturing | Aerospace and Defense |
General Electric Company | Fairfield | Computers and Electronics | Consumer Electronics, Parts and Repair |
Pitney Bowes, Inc. | Stamford | Manufacturing | Tools, Hardware and Light Machinery |
Charter Communications, Inc. | Stamford | Telecommunications | Cable Television Providers |
Aetna Inc. | Hartford | Financial Services | Insurance and Risk Management |
Priceline.com | Norwalk | Travel, Recreation and Leisure | Travel, Recreation, and Leisure Other |
training details locations, tags and why hsg
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.
- We have provided software development and other IT related training to many major corporations in Connecticut since 2002.
- 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 Oracle, MySQL, Cassandra, Hadoop Database programming
- Get your questions answered by easy to follow, organized Oracle, MySQL, Cassandra, Hadoop Database experts
- Get up to speed with vital Oracle, MySQL, Cassandra, Hadoop Database 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…