Powerful Data Analytics & Visualization Tools

#artificialintelligence

A new decade offers a natural inflection point for business transformation and advancement. With innovation and related disruption accelerating, organizations no longer have time to wait to see what change brings. Instead they must embrace it and invest in it. As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights from leading influencers in business intelligence, data analytics, and digital transformation on top enterprise analytics trends.


Introduction to Altair - A Declarative Visualization Library in Python

@machinelearnbot

Visualization is one of the most exciting parts of data science. Plotting huge amounts of data to unveil underlying relationships has its own fun.


From Data Analysis to Machine Learning

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This article was originally posted here, by Mubashir Qasim. "In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.


From Data Analysis to Machine Learning

@machinelearnbot

"In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.