Data Vault is a modern data modelling approach for capturing (historical) data in a structurally auditable and tractable way. While very helpful for data engineers, the Data Vault also enables Data Science in practice.
Learning all you need to learn about data science is only part of the adventure. Landing that first job is another. While it might take a while to get your foot into the door, there are several key efforts you can do to shorten this time as much as possible.
If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started.
If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.
Can AI algorithms help us find love? Can they go a step further and replace a human being as a partner in a relationship? Here, we analyze how far technology has come in helping us meet "our" people, find love, and feel less lonely.
Moving and processing data is happening 24/7/365 world-wide at massive scales that only get larger by the hour. Tools exist to introduce efficiencies in how data can be extracted from sources, transformed through calculations, and loaded into target data repositories. However, on their own, these tools can introduce some restrictions in the processing, especially for the needs of data analytics and data science.
The latest KDnuggets survey is looking to determine the job satisfaction levels of the data community. Take a few moments to contribute your answer and help paint a picture of the current situation.
Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.
Do you love pandas, but don't love it when you reach the limits of your memory or compute resources? Dask provides you with the option to use the pandas API with distributed data and computing. Learn how it works, how to use it, and why it’s worth the switch when you need it most.
When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?
How to Succeed in Becoming a Freelance Data Scientist
4 Machine Learning Concepts I Wish I Knew When I Built My First Model
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