Stewart Kaplan

Stewart Kaplan

Stewart Kaplan has years of experience as a Senior Data Scientist. He enjoys coding and teaching and has created this website to make Machine Learning accessible to everyone.

How Outliers Impact Data Range [Boost Your Analytics Skills]

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Learn how outliers influence the dataset's range and the strategies to mitigate their impact. Discover techniques such as Winsorization, Truncation, robust statistical measures, and transformations for accurate data analysis. Explore methods to handle outliers effectively and enhance the range calculation process.

Meeting the High Demand for Software Engineers [Stay Ahead of the Game]

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Discover the secrets to meet the growing demand for software engineers in a tech-driven world. Learn how mastering Python, Java, and cloud computing can pave the way for success. Unveil the strategies for thriving in the industry, from honing technical skills to building a standout portfolio. Elevate your career in software engineering with agility and innovation.

Should I Learn JavaScript for Data Science [Discover Why it’s Essential]

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Discover the advantages of adding JavaScript to your data science skill set! Unveil its adaptability in front and back-end development, plus its treasure trove of libraries for data visualization, ML, and stats. Unleash the career prospects awaiting data scientists equipped with JavaScript prowess. Explore our guide on JavaScript for Data Science for deeper insights now!

How to Create a Bot in Discord [Boost Your Server Now]

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Learn how to create and deploy a bot on Discord successfully! From generating a unique bot token to testing its functionality, this article covers the essential steps. Discover advanced techniques for optimal deployment and maintenance, alongside expert recommendations from the Discord Developer Portal.

Data Lake vs Data Warehouse: Understanding the Differences [Which One Fits Your Data Strategy]

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Discover the disparities between data lakes and data warehouses in this insightful article. Data lakes specialize in handling raw, unstructured data for tasks like big data analytics, while data warehouses are tailored for structured data and high-speed queries for business intelligence. Make an informed choice based on your organization's unique needs and data attributes. Visit TechTarget and CIO for an in-depth analysis.