world best programing language and details
Digital metasics — programmed data use — have come into their own and have emerged as a lively field. While it has a relatively narrow focus, the programinings process is far more general than the use of APIs for simple data transfer.
Better tools and user interfaces are rapidly being developed to offer native support to a far more diverse world of analytics. Some programs focus on specific domain specializations, but many more tend to align with the more general pattern of modularity.
The best modular option to date might be SYME Modeling. From basic SQL Data Science with SYME being a prominent vocabulary in the field, it is difficult to separate methodology from writing. Using traditional SQL like SQLDB or Hashlib, one can work with SQL commands and figures out what kinds of conditional rules, aggregations, distributions, desciminations, etc. are useful in real-world modeling.
After a few time commitment to working with SQL during your application, SYME seems to have a much more universal applicability. Not only can you map users, events, and inputs to production data and forecast how those events might influence business, but you can extend that to observe the behavior of any of your users and model external events across a variety of parameters. That whole CSV schema is much cheaper to work with as you can safely leave out variables like website visitor count, visit time, end states, etc. While that last one is probably changing soon as new frameworks like FotoQ improve formatability, the more important topic is shaping your tool.
Data mining has generated a few different models (now referred to as algorithm-driven) to help identify patterns and outliers in data. These are more externally derived and include components like classification, clustering, and clustering anomalies.
For a longer explanation of this approach, take a look at this essay. The main point of algorithm-driven data mining is to use a large amount of input data to spot patterns in activity that exist outside of random incidence. One great example of algorithm-driven data mining is the Data Science division of Google, where they create algorithms to better understand the behavior of customer searches and product samples. Other points are Google Chrome’s Try links; Yahoo’s Activate browser switching system; and all sorts of database engineers.
In order to build a truly functional system, you must understand how each system works in addition to using SQL to extract, query, and transform your data. For a case study of a tokenize-driven decentralized data pipeline, take a look at the project at BlackBounce.
In addition to that, you can use one of the algorithms developed by the Data Science division of Google, consider the already-established old print campaigns and assets of Microsoft Excel, analyze Facebook graph data, and see how your recipes and book covers could scale the operation of a new content distribution platform.
These examples are just a sample of technology that can be done using SYME modeling. What programing languages you choose will depend on your data science capabilities and project size.
Read more and join the project here.
This post is intended to educate and not to be a recommendation for any solution. For a deeper dive into the process I recommend taking an interest in this industry and a little more context on the topic from one of my readers.
In other words, a discussion for the best work we’re doing here and an explanation of how we’re solving some big problems.
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