Data Science Dojo

JANUARY 31, 2023

Big data is conventionally understood in terms of its scale. This one-dimensional approach, however, runs the risk of simplifying the complexity of big data. In this blog, we discuss the 10 Vs as metrics to gauge the complexity of big data. Big numbers carry the immediate appeal of big data.

Big Data 370

Big Data Big Data Data Mining Data Mining 370

AWS Machine Learning Blog

NOVEMBER 29, 2023

With over 300 built-in transformations powered by SageMaker Data Wrangler, SageMaker Canvas empowers you to rapidly wrangle the loan data. For this dataset, use Drop missing and Handle outliers to clean data, then apply One-hot encode, and Vectorize text to create features for ML. Huong Nguyen is a Sr.

Data Preparation 123

Data Preparation ML ML Data Quality 123

Pickl AI

JULY 20, 2023

Defining clear objectives and selecting appropriate techniques to extract valuable insights from the data is essential. Here are some project ideas suitable for students interested in big data analytics with Python: 1. Here are some project ideas suitable for students interested in big data analytics with Python: 1.

Analytics 40

Analytics Analytics Big Data Big Data 40

Flipboard

MARCH 22, 2023

Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and clean data, create features, and automate data preparation in ML workflows without writing any code.

AWS 123

AWS Data Preparation Azure ML 123

Mlearning.ai

JANUARY 26, 2023

It can be gradually “enriched” so the typical hierarchy of data is thus: Raw data ↓ Cleaned data ↓ Analysis-ready data ↓ Decision-ready data ↓ Decisions. For example, vector maps of roads of an area coming from different sources is the raw data.

AWS 98

AWS Database Data Science Clean Data 98

AWS Machine Learning Blog

NOVEMBER 27, 2023

Companies that use their unstructured data most effectively will gain significant competitive advantages from AI. Clean data is important for good model performance. Scraped data from the internet often contains a lot of duplications. Extracted texts still have large amounts of gibberish and boilerplate text (e.g.,

Data Preparation 93

Data Preparation AI AI Python 93

Pickl AI

MAY 15, 2023

The type of data processing enables division of data and processing tasks among the multiple machines or clusters. Distributed processing is commonly in use for big data analytics, distributed databases and distributed computing frameworks like Hadoop and Spark. The Data Science courses provided by Pickl.AI

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52