US Equity Markets Training Data

Data Science Dataset
Russell 1000 and 2000 Companies
S&P500

A data snapshot of more than 2000 US public companies' financials, stats and calculations for data science training purposes

This is an example data from history; subscriber data is updated daily
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Charts of US Equity Markets Training Data

Applications

  • Fundamental analysis
  • Profitability analysis
  • Company segmentation and benchmarking
  • Performance analysis with decision trees

DOCUMENTATION

US Equity Markets Training Data

Overview

TEST This data product contains a snapshot data of more than 2,000 United States public companies on NYSE and NASDAQ exchanges.

The dataset is designed for data science training purposes with rich attributes, variables and calculations, so data scientists can test different hypothesis and algorithms with their favourite tools.

Coverage

There are almost 3,000 public companies in this dataset.

Sector Number of Companies
Finance 520
Consumer Services 514
Health Care 466
Technology 359
Capital Goods 238
Basic Industries 160
Consumer Non-Durables 140
Energy 128
Public Utilities 108
Consumer Durables 85
Miscellaneous 84
Transportation 57

Variables

There are 35 variables that are from financials, price stats, scores and curated calculations. You can check out the dictionary to learn the variables.

Data Collection Methodology

  • Data is cleansed and organized to provide a ready for analysis dataset
  • Variables are chosen from key financial items and stats
  • There are curated scores and calculations so you can train your models with rich variety
  • This dataset is created for ML training purposes and not updating. If you need daily fresh and quality checked data, you should check out the related products below.

Key Features

  • Rich content for analysis and ML training
  • Updated once a year
  • Covers Russell 3000 Companies
  • Covers S&P 500

Data Provider

Alta Bering

Until a few decades ago majority of us were employed in real world businesses. Alta Bering came out of a history of production technology and reconciled it with sophisticated algorithmic math, an after-thought in enterprise system functionality.We believe efficiency is key to mitigating scarcity economics. In the early 2000s, we set our sights on helping companies achieve higher efficiency and growth, not just growth.Today we enable advanced prescriptive analytics for data scientists and enterprise cloud platforms. Our goal is to help business analysts step up to corporate responsibility.Contact us for more about how we deliver our predictive analytics consulting to large and medium sized clients.

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