A Simple Program to Get Thousands of Stocks' Data - Hands-Off Investing

Effortlessly obtain the historical data of over a thousand stocks. For free.

Anyone Can Do This
Beginners welcome. I’ve created this guide for Python developers of all skill levels. Maybe you don’t even know what Python is, and that’s okay! I’ve got you.

After running this program you’ll be left with a folder on your device that contains the historical data of any number of stocks. Also, I’ve simplified the code by breaking it into sections and giving a description of what each part of the code does.

If you’ve read any of my articles about automating the stock analysis process, you’ve probably seen this code before. I figured that since this program is so integral for algorithmic investing, I need to break it down further and make sure that everyone understands how to use it!

If this is your first time using Python (or coding in general), I’d recommend reading this article for a simple walk-through of the installation and startup process. And don’t worry, everybody learns how to code through practice. This is a great place to start learning!

#investing #stocks #fintech #python #automation #data-science

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A Simple Program to Get Thousands of Stocks' Data - Hands-Off Investing
Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

A Simple Program to Get Thousands of Stocks' Data - Hands-Off Investing

Effortlessly obtain the historical data of over a thousand stocks. For free.

Anyone Can Do This
Beginners welcome. I’ve created this guide for Python developers of all skill levels. Maybe you don’t even know what Python is, and that’s okay! I’ve got you.

After running this program you’ll be left with a folder on your device that contains the historical data of any number of stocks. Also, I’ve simplified the code by breaking it into sections and giving a description of what each part of the code does.

If you’ve read any of my articles about automating the stock analysis process, you’ve probably seen this code before. I figured that since this program is so integral for algorithmic investing, I need to break it down further and make sure that everyone understands how to use it!

If this is your first time using Python (or coding in general), I’d recommend reading this article for a simple walk-through of the installation and startup process. And don’t worry, everybody learns how to code through practice. This is a great place to start learning!

#investing #stocks #fintech #python #automation #data-science

Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt