This article describes the analytical technique of gradient boosting regression.
Year: 2021
This article describes the analytical technique of random forest regression.
If you are a developer, contemplating a software development project that must support Big Data, a large user base and/or multiple locations, Apache Spark should definitely be on your short list of considerations for a computing framework. In this article, we look at three reasons you should use Apache Spark in your Big Data projects.
Don’t Be Afraid of Self-Serve Data Preparation! It’s Easy. You Can Do It!
Oh, the mysterious world of data preparation! It is daunting and confusing and…wait, no! It doesn’t have to be. If you aren’t employed as an IT professional, a business analyst or a data scientist, you probably see this arena as confusing and intimidating and you probably want nothing to do with data preparation. BUT, when you need a report, or you have to provide a recommendation to your boss in a staff meeting, you desperately need that data and that analysis, don’t you?
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Java has some amazingly simple tools to make a developer’s life easier. One such tool is the JavaScript Object Notation (JSON) open standard file format and data interchange format. It leverages standard text to store and transfer data objects and makes it significantly easier to build Web Services solutions. It can be leveraged in any language including Java, PHP, .NET, etc. It is easy to read and write and easy to parse and generate data and it is a popular data-interchange language for developers around the world. In this article, we look at two specific web services projects utilizing JSON to build the foundation for business solutions.
Small eCommerce Businesses Can Improve Results with Analytics!
If you are a business owner with an eCommerce or online shopping site, you probably spend a lot of time trying to understand your business results. There are numerous surveys that address the market and the activity in online shopping. Regardless of which survey you read, you are probably surprised to know that more than 2 billion people shop online. But, those numbers don’t tell the whole story. It is also important to understand that people will often check prices online while they are standing in a retail store just to see if they can find something at a better price. So, they don’t always buy your services or products just because they visit your site. And, you are probably not surprised to know that your competition is growing every day. You know that better than anyone.
One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. When business users adopt true self-serve BI tools like Plug n’ Play Predictive Analysis, Smart Data Visualization, and Self-Serve Data Preparation, they can apply the domain knowledge and skill they have developed in their role to create reports, analyze data and make recommendations and decisions with confidence.
1. Outlier, an Outsider!
Outliers, also referred to as anomaly, exception, irregularity, deviation, oddity, arise in data analysis when the data records differ dramatically from the other observations. In layman’s terms, an outlier can be interpreted as any value that is numerically far-flung from most of the data points in a sample of data.
Software Product Need a Tune-Up? Don’t Focus on the Fix. Re-Engineer for Success!
Let me tell you a story. My friend, Shaniqua just got a new job and found, to her dismay, that her first project involved the re-engineering of a software product the business had introduced to the market (to less than favorable response). I talked to Shaniqua on the phone and we discussed her conundrum. The business loved the software product but consumers had a lot of issues with its responsiveness, the lack of an intuitive user experience (Ux) design and many other details, including loading data that was integrated from other sources. In short, the original software product design and development was less than stellar and now the company was paying the price.
