Smart Data Collective

MARCH 28, 2022

There are four main types of data analytics: Predictive data analytics: It is used to identify various trends, causation, and correlations. It can be further classified as statistical and predictive modeling, but the two are closely associated with each other.

Data Analytics 84

Data Analytics Software Analytics Statistics 84

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.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Domino Data Lab

OCTOBER 23, 2019

As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt “Predicting Social-Media Influence in the NBA” from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. We discussed this as far back as Chapter 1 [in the book]. There is also a complementary Domino project available.

Testing 68

Testing Modeling Interactive Measurement 68

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

Let’s just give our customers access to the data. You’ve settled for becoming a data collection tool rather than adding value to your product. While data exports may satisfy a portion of your customers, there will be many who simply want reports and insights that are available “out of the box.”

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52