30 Best Data Science Books to Read in 2023
Analytics Vidhya
FEBRUARY 28, 2023
To achieve maximum efficiency, every company strives to use various data at every stage of its operations.
Analytics Vidhya
FEBRUARY 28, 2023
To achieve maximum efficiency, every company strives to use various data at every stage of its operations.
datapine
MAY 14, 2019
“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert. We live in a world saturated with data. Zettabytes of data are floating around in our digital universe, just waiting to be analyzed and explored, according to AnalyticsWeek. click for book source**.
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Smart Data Collective
AUGUST 20, 2021
As we said in the past, big data and machine learning technology can be invaluable in the realm of software development. Machine learning technology has become a lot more important in the app development profession. Machine learning can be useful for solving monetization challenges.
Rocket-Powered Data Science
DECEMBER 16, 2018
but that is just the tip of a vast iceberg — just take a look at the 750-page book “ Encyclopedia of Distances “ What evaluation metric should we use to determine if the clusters are “good enough” or optimal ( i.e., the most compact set of clusters relative to the separation of the clusters)?
Sisense
APRIL 10, 2020
NLP can be used on written text or speech data. For our example, we will use written text for our comparison of R vs Python for data science. We are surrounded by written text every day: emails, SMS messages, webpages, books, and much more. R vs Python for data science: Digging into the differences.
Occam's Razor
MARCH 30, 2017
People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. All sans human-control.
Domino Data Lab
JULY 22, 2019
By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. That was the origin of big data.
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