Goto

Collaborating Authors

Best Predictive Analytics Tools and Software 2022

#artificialintelligence

Managing data has always been a challenge for businesses. With new sources and higher volumes of data coming in all the time, it's more important than ever to have the right tools in place. Predictive analytics tools and software are the best way to accomplish this task. Data scientists and business leaders must be able to organize data and clean it to get the process started. The next step is analyzing it and sharing the results with colleagues.


The 16 Best Advanced Analytics Software Applications and Tools for 2021

#artificialintelligence

Solutions Review's listing of the best advanced analytics software, applications, and tools is an annual sneak peek of the top tools included in our Buyer's Guide for Data Science and Machine Learning Platforms. Information was gathered via online materials and reports, conversations with vendor representatives, and examinations of product demonstrations and free trials. The editors at Solutions Review have developed this resource to assist buyers in search of the best advanced analytics software, applications, and tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process -- one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we've profiled the best advanced analytics software providers all in one place.


60 Top Data Science Tools: In-depth Guide [2018 update]

#artificialintelligence

Data science tools are evolving. Becoming data scientist is hard. In any hard task, focus is critical. As a data scientist, Python should probably be the first tool you should master. Kaggle, the community for data science competitions, publishes surveys of data scientist such as their "2017 the State of Data Science" report.


Top Machine Learning Solutions

#artificialintelligence

In today's hyper-fast cloud computing era, machine learning solutions drive exponential progress in improving systems. Machine learning's ability to leverage Big Data analytics and identify patterns offers critical competitive advantage to today's businesses. Often used in combination with artificial intelligence and deep learning, machine learning uses sophisticated statistical modeling. These complex systems may reside in private cloud or public cloud. In any case, the passage of time boosts machine learning: as more data is added to a task and analyzed over time, ML produces more accurate the results.


Using Automation in AI with Recent Enterprise Tools - DataScienceCentral.com

#artificialintelligence

Data Science (DS) and Machine Learning (ML) are the spines of today's data-driven business decision-making. From a human viewpoint, ML often consists of multiple phases: from gathering requirements and datasets to deploying a model, and to support human decision-making--we refer to these stages together as DS/ML Lifecycle. There are also various personas in the DS/ML team and these personas must coordinate across the lifecycle: stakeholders set requirements, data scientists define a plan, and data engineers and ML engineers support with data cleaning and model building. Later, stakeholders verify the model, and domain experts use model inferences in decision making, and so on. Throughout the lifecycle, refinements may be performed at various stages, as needed. It is such a complex and time-consuming activity that there are not enough DS/ML professionals to fill the job demands, and as much as 80% of their time is spent on low-level activities such as tweaking data or trying out various algorithmic options and model tuning. These two challenges -- the dearth of data scientists, and time-consuming low-level activities -- have stimulated AI researchers and system builders to explore an automated solution for DS/ML work: Automated Data Science (AutoML). Several AutoML algorithms and systems have been built to automate the various stages of the DS/ML lifecycle. For example, the ETL (extract/transform/load) task has been applied to the data readiness, pre-processing & cleaning stage, and has attracted research attention.