Best of Data Science — July 2025

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    Article
    Avatar of planetpythonPlanet Python·44w

    Python Roadmap with Free Courses/Certifcates to High-Paying Jobs

    Python leads to six-figure salaries when applied in specialized fields like AI, data science, cybersecurity, and automation. Five free certifications are recommended: Cisco's Programming Essentials for foundational skills, IBM Data Science Professional Certificate for data scientist roles, freeCodeCamp's Machine Learning with Python for ML engineering, Information Security certification for cybersecurity programming, and Jovian's Pandas course for data analysis mastery. Success requires specializing Python skills within high-demand domains rather than learning the language in isolation.

  2. 2
    Article
    Avatar of palindromeThe Palindrome·46w

    There Are Many Roads to Machine Learning

    Machine learning has multiple valid entry paths, debunking common gatekeeping myths like needing a PhD or advanced math. High school math is sufficient to start, and success depends on matching your existing skills with your goals rather than following rigid rules. The field accommodates both generalists and specialists, with domain expertise often being more valuable than technical sophistication. The key is finding where you fit rather than forcing yourself into predetermined molds.

  3. 3
    Article
    Avatar of wheresyouredWhere's Your Ed At·44w

    The Remarkable Incompetence At The Heart Of Tech

    Software engineer and IT consultancy director Nik Suresh argues that most enterprise software purchases are distractions from deeper organizational problems. He describes widespread incompetence in corporate software teams, where engineers can deliver nothing of value for years while being paid six-figure salaries. Through personal anecdotes, he illustrates how teams burn hundreds of thousands on tools like Snowflake due to basic configuration errors, and how simple projects take years to complete. The real crisis isn't solved by buying more SaaS products, but requires understanding the fundamental dysfunction in how most companies approach software development and management.

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    Article
    Avatar of tdsTowards Data Science·43w

    The ONLY Data Science Roadmap You Need to Get a Job

    A comprehensive learning roadmap for aspiring data scientists covers six core areas: statistics (summary statistics, probability, hypothesis testing), mathematics (calculus and linear algebra), programming (Python and SQL), technical tools (Git, command line, package management), machine learning fundamentals (regression, decision trees, neural networks), and optional deep learning concepts. The guide emphasizes mastering fundamentals over chasing latest trends, recommending specific textbooks like 'Practical Statistics for Data Science' and courses like Andrew Ng's Machine Learning Specialization. Each section includes practical learning resources and focuses on skills directly applicable to entry-level data science positions.

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    Video
    Avatar of TechWithTimTech With Tim·44w

    How to become a Python developer FAST

    A comprehensive roadmap for becoming a Python developer quickly covers four main steps: mastering core Python skills (syntax, OOP, async programming, advanced features), learning essential development tools (Git, CLI, networking, virtual environments, debugging), specializing in a specific niche (web development, AI/ML, data science, or DevOps), and actively pursuing job opportunities through portfolio building, networking, and interview preparation. The guide emphasizes hands-on, project-based learning over passive consumption of tutorials and recommends focusing on practical skills that demonstrate immediate value to employers.

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    Article
    Avatar of phoronixPhoronix·45w

    QuestDB 9.0 Released For High Performance, Time-Series Database

    QuestDB 9.0 has been released as a major update to the high-performance, open-source time-series database. The database operates under an Apache 2.0 license and focuses on delivering enhanced performance for time-series data management.

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    Video
    Avatar of youtubeYouTube·43w

    Data Science Full Course 2025 (FREE) | Intellipaat

    A comprehensive data science course covering the complete project lifecycle from business problem identification to model deployment. The course explains data science fundamentals through a practical example of supply chain optimization, demonstrates linear regression with detailed mathematical explanations, and provides a year-long roadmap for becoming a data scientist. Key topics include statistics, Python programming, exploratory data analysis, machine learning algorithms, and portfolio building through Kaggle competitions.

  8. 8
    Article
    Avatar of cloudflareCloudflare·46w

    How TimescaleDB helped us scale analytics and reporting

    Cloudflare migrated from vanilla PostgreSQL to TimescaleDB for their Digital Experience Monitoring and Zero Trust Analytics products, achieving 5-35x query performance improvements and 33x storage compression. The team initially chose PostgreSQL over ClickHouse to maintain architectural simplicity, but as data scaled to billions of rows, they needed better performance. TimescaleDB provided automatic partitioning, continuous aggregates, columnstore compression, and sparse indexes while maintaining PostgreSQL compatibility. Key optimizations included proper index column ordering, compression policies, and segmentation strategies that dramatically improved query latency and reduced storage costs.

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    Article
    Avatar of mlmMachine Learning Mastery·45w

    7 Pandas Tricks That Cut Your Data Prep Time in Half

    Seven practical pandas techniques to accelerate data preparation workflows: chaining transformations with assign(), filling missing values using dictionaries in fillna(), flattening list columns with explode(), readable filtering with query(), named aggregations with groupby().agg(), date parsing with pd.to_datetime(), and building modular workflows with pipe(). These methods help reduce boilerplate code, improve readability, and streamline the data cleaning process.

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    Video
    Avatar of codeheadCodeHead·46w

    Should YOU Become An AI Engineer?

    AI engineering combines data science, backend development, and machine learning to build intelligent systems. The role involves extensive Python programming, mathematical knowledge (linear algebra, calculus, statistics), data cleaning, and model training. While well-compensated with six-figure salaries, the field requires patience for long training cycles, debugging complex models, and dealing with messy real-world data. Success demands comfort with ambiguity, continuous learning, and mathematical problem-solving rather than quick feedback loops.

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