Best of Data Analysis — October 2025

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   Article

   Code Like A Girl·30w

   Learning to Be Okay With Not Knowing Everything

   The data analytics field requires continuous learning rather than mastery of everything. Success comes from embracing curiosity over certainty, learning in layers rather than all at once, and focusing on relevant skills instead of chasing every trend. The imposter feeling is normal, and even experienced professionals regularly search for answers. Progress matters more than perfection, and being comfortable with not knowing everything is essential for sustainable growth in data careers.

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2. 2
   Article

   Hacker News·31w

   Comparing the power consumption of a 30 year old refrigerator to a brand new one

   A comparison of power consumption between a 30-year-old UPO Jääkarhu refrigerator and a modern replacement using smart plug monitoring. The old unit consumed 2.6 kWh daily versus 0.7 kWh for the new one—a 3.7x difference. Monthly savings of approximately 57 kWh translate to a payback period of about 38 months at 17 cents per kWh. The analysis demonstrates practical IoT monitoring applications for home energy optimization and appliance replacement decisions.

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3. 3
   Article

   Cloudflare·30w

   From .com to .anything: introducing Top-Level Domain (TLD) insights on Cloudflare Radar

   Cloudflare Radar launched a new Top-Level Domain (TLD) insights page that provides comprehensive data on TLD popularity, traffic patterns, and security metrics. The page uses DNS Magnitude—a metric measuring how many unique networks query domains within a TLD—to rank over 2,500 TLDs. Surprisingly, .su (Soviet Union's legacy TLD) tops the ranking due to queries from a popular online game. Individual TLD pages offer detailed information including DNSSEC support, RDAP availability, DNS query volumes, certificate issuance data, and geographic distribution. The feature extends existing DNS insights to all delegated TLDs and integrates with Cloudflare Registrar for domain registration. All data is accessible via API and the Radar Data Explorer.

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4. 4
   Article

   Medium·30w

   Create a Stock Screener with MCP Servers in Minutes

   Learn how to build a stock screener using Financial Modeling Prep's MCP Server and the Model Context Protocol. The tutorial demonstrates fetching structured financial data through a unified interface, filtering stocks based on metrics like P/E ratio and debt-to-equity, and implementing the screener in Python with pandas. Setup involves either using Cursor IDE or running a local Docker container, with the MCP server providing clean JSON responses without managing multiple API endpoints or authentication.

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