Data Science | News, how-tos, features, reviews, and videos
For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.
The two-hour workshops offer training in data visualization and analysis with R, Python, and SQL and cost just $20 or €20. Next up is ChatGPT in R.
Combining machine learning and Excel can get you the data transformation you need while data scientists are scarce.
Add-on to Jupyter Notebooks enables a literate Python development style that gives you high-quality documentation, tests, continuous integration, and packaging for free.
Data science is already a vital element of a successful business. Before long it will be part of every application, and AI will be embedded in every transaction workflow.
Business analysts are running into the limits of BI tools and looking for ways to do more advanced analytics. Python is the way forward.
Recent trends show a return to cloud fundamentals, such as data, development, deployment, and security, rather than chasing what’s new and cool.
Modelops improves machine learning model development, testing, deployment, and monitoring. Follow these tips to keep model risks in check and increase the efficiency and usefulness of your ML initiatives.
The San Francisco-based startup has released a SQL-based, self-orchestrating data pipeline platform, claiming it will go to go toe-to-toe with Databricks’ Delta Live Tables.
Python has a wealth of scientific computing tools, so how do you decide which ones are right for you? This book cuts through the noise to help you deliver results.