📉📊📈Top 6 tools to analyze data of any nature
DataRobot is a tool for scaling machine learning capabilities. Contains a massive library of open source and in-house models. Solves complex problems in the field of Data Science. Delivers fully explainable AI through human-friendly visual representations. The downside is the price, but a free trial is available
Alteryx combines analytics, machine learning, data science and process automation into a single end-to-end platform. The platform accepts data from hundreds of other data stores (including Oracle, Amazon, and Salesforce), allowing you to spend more time analyzing and less searching. Alteryx allows you to quickly prototype machine learning models and pipelines using automated model training blocks. It helps you easily visualize data throughout the entire problem solving and modeling journey.
H2O is an open source distributed memory machine learning tool with linear scalability. It supports almost all popular statistical and machine learning algorithms, including generalized linear models, deep learning, and gradient boosted machines. H2O takes data directly from Spark, Azure, Spark, HDFS, and various other sources into its distributed in-memory key value store.
SPSS Statistics - designed to solve business and research problems through detailed analysis, hypothesis testing and predictive analytics. SPSS can read and write data from spreadsheets, databases, ASCII text files, and other statistical packages. It can read and write data to external relational database tables via SQL and ODBC.
RapidMiner - supports all stages of the machine learning method, including data preparation, result visualization, model validation, and optimization. In addition to its own collection of datasets, RapidMiner provides several options for creating a database in the cloud to store huge amounts of data. It is possible to store and load data from various platforms such as NoSQL, Hadoop, RDBMS, etc.
Weka is a set of visualization tools and algorithms for data analysis and predictive modeling. All of them are available free of charge under the GNU General Public License. Users can experiment with their datasets by applying different algorithms to see which model gives the best result. They can then use visualization tools to explore the data.