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Joining Data Sources

Towards Data Science

Most “data science” in the real world involves creating a data set, a visualization, an application that requires pulling and joining data from very different sources to tell a cohesive story. Moving…...

Where do you get your data?

Simply Statistics

Here’s a question I get fairly frequently from various types of people: Where do you get your data? This is sometimes followed up quickly with “Can we use some of your data?” My contention is that if ...

Data Integration — Things to Consider

Towards Data Science

When integrating data from system A to system B, data engineers and other stakeholders should not only focus on the data process, e.g. via ETL/ELT, but also on the source system. What various…

Data Sourcing, Market basket analysis, and SweetViz

Analytics Vidhya

This is organizational data, and since such data are associated with security and privacy concerns, company approval is needed to access it. It is useful for internal policy-making and for building…

The Data Scientist’s Guide to Choosing Data Vendors

Towards Data Science

A practical guide to effectively evaluating and deciding on data to enrich and improve your models A data scientist choosing amongst dozens of data vendors | imagine.art I’ve served as the VP of Data...

Discover public data with the Data Source Handbook

Pete Warden's blog

I’m pleased to announce that the Data Source Handbook is now available from O’Reilly. It’s a compact ebook guide to the most useful APIs and bulk data sets I’ve found, packed with examples and advice....

Data Scraping Outside the Web

Analytics Vidhya

In the beginning, there was only manual data entry. Okay, no problem if we have to input just a few lines of numbers but, what if we have to collect hundreds of books data. Or maybe we need to…

Data Sources

Simply Statistics

Here are places you can get data sets to analyze (for class projects, fun and profit!) Data Market Infochimps Data.gov Factual.com I’m sure there are a ton more…would love to hear from people.

Data Integration: The Definitive Guide

Towards Data Science

This guide on data integration provides a thorough overview of the data integration landscape, describing the various technologies that are available to move data between tools and databases.

A Tip Before Entering the World of Data

Analytics Vidhya

New to coding or data science? Need to develop a data team? Uncertain about which services to outsource? Here is a tip for businesses and indiviudals.

Data Retrieval with SQL — Tutorial & Examples

Towards Data Science

SQL Tutorial with Examples and Cheat Sheet Continue reading on Towards Data Science

Data wrangling

Towards Data Science

First of all, you have to get your data! This can involve extricating the data you want from a larger dataset. It can involve merging two or more datasets. A dog isn’t just for Christmas, it’s for…

The future of data integration

Towards Data Science

Cloud computing, big data, machine learning, data lakes, data warehouses — no doubt, if you’ve been following the tech world you’ve heard these buzz words. These trends and the resulting technologies…...

Data Mangement Strategy: Part 2

Towards Data Science

This is part 2of the series of articles related to carrying out and implementing a successful Data Management Strategy within an aspiring Digital Organization. These are key aspects of every Data…

Doing Data in a “normal” company

Towards Data Science

The first proper gift I asked for when I was a child was a computer, a black MacBook when the cheap ones were in plastic and the expensive ones were in aluminium. As hard as I try to escape from the…

Designing Data Products

Towards Data Science

DJ Patil defines a Data Product as “… a product that facilitates an end goal through the use of data”. At first glance, this appears quite broad — is everything on the web a data product? All…

Data Wrangling: A Beginners Guide

Python in Plain English

If you have landed upon this article, maybe you are a beginner in Data Science field or just revisiting few of the most important steps in the field of Data Analysis. In this guide I would start with…...

A Complete Guide to Understanding Data Orchestration

Towards Data Science

Everything you should know about data orchestration and popular frameworks in 2022 Continue reading on Towards Data Science

Streamlining Data Collection for a Law Firm

Python in Plain English

Case Study: Client Background A prominent law firm faced a recurring challenge: manually gathering specific data from a legal website and entering it into an Excel spreadsheet. This task, performed da...

Data analysis acquisition “worst deal ever”?

Simply Statistics

A little over a year ago I mentioned that data analysis companies were getting gobbled up by larger technology companies. In particular, HP bought Autonomy, a British data analysis company, for about ...

The Easiest Way To Ingest Data Into BigQuery

Towards Data Science

How to use Google Sheets to directly load data into BigQuery — and why it is the best solution for certain use cases Continue reading on Towards Data Science

Five Steps for Building a Great Data Product

Towards Data Science

Closely cooperate with key stakeholders from your organization. Understand your clients. Ask them: What are their goals, what do they need? What is holding them back? How could data help them…

Speed Up Your Data Collection Efforts With Multithreading in Python

Better Programming

Data retrieval doesn’t have to be a one-person job Continue reading on Better Programming

Using Data Science for Customer Acquisition

Towards Data Science

With the aim of bringing new customers to their brands, companies have been developing their customer acquisition processes and strategies. It’s not easy though! The first step of any basic customer…