Machine Learning’ Predicts The FIFA World Cup 2018 Winner!

How can we forget Paul the Octopus who predicted the winner of the world cup in 2010 which had gone viral globally? The Octopus used the flags of both the teams, which were presented in front of him in a box before a game, and the winning team would be selected the box from which Paul would eat first. Paul had 12 correct predictions out of 14.

The result of any given sports game is unpredictable, yet we humans try and predict results either on the basis of statistical analysis, or simply a hunch. Predicting the winner of any a game is part of the game viewing experience and has been a crucial selling point for the betting industry since the beginning. Another interesting method to predict results of games has recently surfaced: Prediction by using Machine Learning, in which the winner of the game or the tournament is predicted by companies who’ve used several different algorithms to predict these results.

Below are some of the companies that used Machine Learning to predict the results of the FIFA World Cup 2018

Prediction by the Technical University of Dortmund In Germany

An algorithm was developed by a team which included researchers from German Technische Universitat of Dortmund, the Ghent University in Belgium, and the Technical University in Munich. The algorithm is fed data which ran approximately 100,000 simulations to predict Spain as the winner.

Prediction by a San Francisco Based Tech Firm – Unanimous AI

San Francisco based technology firm Unanimous AI tried, their hands on the this. Here is what their Founder & CEO had to say “These predictions were generated using swarm A.I. technology” Further, the founder & CEO stated that “This means it uses a unique combination of human insights and artificial intelligence algorithms, resulting in a system that is smarter than the humans or the machines could be on their own. It works by connecting a group of people over the internet using A.I. algorithms, enabling them to think together as a system, and converge upon predictions that are the optimized combination of their individual knowledge, wisdom, instincts, and intuitions”. The final outcome of their simulation pointed that Germany will defeat Brazil in the finals.

Prediction by Goldman Sachs

Technology Teams within this American multinational investment bank, have also tried its hands on predicting the FIFA World Cup winner of 2018, and their machine learning program has given them a final test result. The winning team predicted by Goldman Sachs, using machine learning in Brazil.

Python Language: Why One Should Learn It and How It Can Help

Many programming languages are used today, some are used, and some have gone obsolete. In the last few years, the programming scenario has changed drastically as developers and programmers are searching for more universal and approachable languages. This is the reason why Python language has become so famous recently. The Python community is growing bigger day by day as many programmers are now finding it to be one of the most user-friendly programming languages.

Python language has become so famous that every field and sector is now a user of it. Even though the other programming languages are not losing their fans, Python is increasing its fan base. Therefore, more and more people are now aspiring to learn Python. Some of the reasons why having a certification in Python can be helpful are discussed below:

Machine learning

Today, almost everything runs through algorithms, whether it is a search engine, social media, chat bots, virtual personal assistants, etc. These sophisticated algorithms are the result of machine learning and it has changed the entire technological scenario. With machine learning, the major programming language that is been used is Python, and one can find many libraries dedicated to machine learning only.

Big data

Python is used in data science the most and the professionals in this field are required to have expertise in this programming language. Though there are many other languages like Java, R, etc. which is used for data science, Python remains the favorite. This is because of the diversity it allows in automation technology, along with with the various framework and library available like NumPy, PyBrain, etc.

Web development

There are many websites these days like Reddit which are developed using Python language. The main reason why the Python programming language is used in web development is its speed and effectiveness. Using PHP developing a website can take hours, while using Python will take only a few minutes. Also, there are frameworks and libraries like Django and Flask which make the work much easier.

Community

One of the areas that programmers search for these days is the communities. In these communities, the developers and programmers can connect with others from any part of the world and can share their experiences and technologies. This helps them in learning new things about Python and how to solve various issues that may arise while coding.

Libraries

Libraries are really helpful when it comes to application and website development. One can find any kind of code. Python has a huge number of frameworks and libraries like Flask, Django, NumPy, Scipy, Pandas, Tensorflow, Keras, etc. One needs to concentrate on the logic and objective and the codes are easily available in the libraries.

Simple

Lastly, the biggest reason why programmers use Python is the fact that it is a simple programming language. It is a beginner user-friendly language as it does not require a lot of complex codes and syntaxes which are not understandable. Python has an easy and readable syntax and coding which makes its set-up and usage much easier.

The Pros and Cons of DBaaS-Database As a Service

DBaaS enables you to test drive multiple solutions and only buy the licenses and hardware you need to be successful.

Almost every business these days is data-centered. Whether the data is for internal applications and systems, or for other services that are offered, let’s face it…

Managing data is a key to success.

Before listing the pros and cons of DBaaS, we need to explore a few decisions businesses have to make.

These include numerous quick decisions about data handling that can set them on a path that, if incorrect, are difficult and costly to correct. Those decisions are:

· What database type to use, SQL or NoSQL?

· What are the data storage and query needs? Transactional? Big Data?

· What database system to use? A few SQL choices might be Oracle, MySQL, MSSQL, and Sybase. A few No-SQL choices might be MongoDB or Cassandra.

· Do we have DBA (database administrator) talent or do we have to hire?

· What kind of server or resources are needed? What are my power, server, disk, processing, network, and IO requirements?

· How do I maintain, backup, administer and otherwise own the database framework?

· What is my cost of ownership?

First let’s explore which database type to use, SQL or NoSQL.

Traditional database types classified as SQL have a significant place in businesses and are a mainstay for business choices. However, as companies start to create applications that drive decisions based on significant database analysis of large, almost unfathomable amounts of data, they migrate to NoSQL solutions like MongoDB or Cassandra.

The architecture of NoSQL makes it a good choice for big data solutions while the built in protections of a transactional based system like Oracle make it a better choice for banking or similar solutions.

When it comes to picking a specific system, businesses tend to stick with what they know. In other words, if they already have Oracle, and Oracle talent, then when management asks those individuals which database system they should use on Project X, it should be no surprise that they pick Oracle.

Matching a specific database system to a set of business requirements is an arduous task that should always be looked at with a fresh perspective. It should not just be based on what talent is already employed or what systems a business is comfortable with.

Let’s face it, if a business picks correctly, all is good. If they pick incorrectly, they have wasted a lot of resources which equates to dollars. Enter DBaaS.

Where DBaaS excels is that it gives businesses the ability to test the waters a bit, to try before they invest heavily.

DBaaS acts as a stepping stone to total ownership, a cost effective solution to help you figure out your needs prior to investing heavily.

DBaaS has both pros and cons.

First, it is necessary to distinguish between “hosting database systems” and DBaaS.

There are many cloud based solutions that “host” a database system but provide no significant help in configuration, tuning, consulting, and providing the talent needed to actually use those systems.

True DBaaS provides both the system and the talent to help you utilize the database and determine how to store, query, and analyze your data. The value of DBaaS goes way beyond the hosting.

The pros of DBaaS include:

· No equipment or software licenses.

· Flexibility. Multiple choices are available to test drive your applications and pick the right platform for your business requirements.

· Significantly less staffing requirements. The DBaaS provider handles installation, configuration, and in many cases development.

· Offsite hosting, providing protection from local power failures or disasters. Many businesses design their system with power redundancy in mind, but, in reality, rarely meet those goals.

· SLA agreements that have redundancy, uptime, and backup protections. A DBaaS provider has intent focus on protecting your data.

Meantime the cons of DBaaS include:

· Limited access to underlying servers. This can present itself as a feeling of no control.

· Very little knowledge of how your data is protected from cyber security threats. This can be dangerous for sensitive data.

So how do you decide? Is there a transition from one to the other? Yes, almost always, but by following a few guidelines to start with, DBaaS can be used properly.

Those who wish to use DBaaS should adhere to the following guidelines:

1. Do all development using DBaaS. This is your chance to test drive different architectures and features.

2. Unless you have full disclosure of how your data is protected, managed, and secured by DBaaS providers, it is suggested to consult with database architects to host sensitive data internally. Note, this is typically not big data. When we use the terms sensitive data, we mean just that. Data like SSNs, account details, financials, personal data, etc. Does this mean that you cannot use DBaaS for this? No, it means that you first have to find a DBaaS provider that will show you everything from how your encrypted data gets in their system to storage, access, etc.

3. When you are not sure of what your database needs really are, use DBaaS first. This lets you try SQL or NoSQL. This lets you explore the encryption capabilities of Oracle versus MySQL. Think of DBaaS like buying a car. You test drive sedans, trucks, and SUVs, and try different manufacturers and features. You may decide to lease or buy.

4. Always monitor and evaluate the cost of ownership. As your system grows, the operating costs might make sense to drop DBaaS and build an in-house system. By then, however, you have already decided on what you really need.

The goal with DBaaS is to test drive multiple solutions and only buy the licenses and hardware you need to be successful. You can then hire the correct talent to manage your system.

David Moye is a Principal with Forensic IT in St. Louis, MO, a firm providing big data solutions to companies nationwide. David helped found Forensic IT in 2003 and has some 25 plus years of experience as a software engineer and solution architect. Along with at least a half a dozen core programming languages, he is a certified DBA in Oracle and Sybase and has spent years working with MS-SQL and MySql.