Are you still jumping between multiple tools to manage your data? What if there was one unified platform to ingest, query, analyze and even build apps directly from your browser? Whether you're a data analyst, an engineer, or a business user, understanding Snowsight is crucial for maximizing your productivity and managing your data like a pro....

Have you ever wanted to supercharge your data processing with AI while working in a familiar pandas environment? Many data teams struggle with integrating advanced AI capabilities into their existing data workflows without compromising performance or requiring complex infrastructure changes. Snowflake Cortex provides a solution by enabling seamless integration of powerful AI capabilities directly into Modin DataFrames within the unified Snowflake ecosystem....

Ever wondered how to make your enterprise data work smarter, not just harder? Snowflake is stepping up its game in enterprise AI with Snowflake Intelligence. You can think of it as a powerful platform that empowers developers to build clever data agents that can analyze, summarize, and even take action on your company's valuable data....

In this video, you'll build a Do It Yourself LLM-powered voice agent in Python that you can chat to verbally. This voice agent retains memory throughout the conversation and performs both speech-to-text (transcription) and text-to-speech (speech synthesis). With this voice agent, you can chat with the LLM hands-free like you would with a friend! There's lots to customize here and make your own voice agent, watch the video and find out how!

In this video, you'll learn how to prepare and clean bioactivity data for the aromatase inhibitors in Python using the RDKit library. We'll cover the process of loading data from the CHEMBL database, removing duplicate molecules, and standardizing SMILES notations (a string representation of a molecule) in order to create a high-quality, non-redundant dataset suitable for machine learning model building and further data analysis.

In this video, you'll learn how to create an end-to-end call center analytics workflow using Assembly AI and Python. We'll walk you through the process of transcribing audio, identifying speakers, analyzing sentiment, and visualizing the results using heat maps. The tutorial covers key concepts such as API integration, natural language processing, and data visualization techniques to extract meaningful insights from call center conversations.

In recent years, we have witnessed an exponential growth in the generation of data in the biological sciences. To harness such big biological data, computational and machine learning pipelines have become instrumental for exploratory data analysis, predictive modeling, and data-driven decision-making. Open-source web development frameworks like Streamlit enable the scientific community to build and share web applications by using less code, thereby speeding up research and development. This chapter explores the usage of Streamlit for the development of software and tools in the field of bioinformatics.

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), ...

Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). Typical molecular parameters that are used to account for electronic properties, ...