Best of Data Science — 2025

  1. 1
    Article
    Avatar of mlmMachine Learning Mastery·1y

    The Roadmap for Mastering Machine Learning in 2025

    Machine learning (ML) is integral to many sectors, making it a valuable skill by 2025. This guide offers a step-by-step roadmap for mastering ML, starting with prerequisites in mathematics and programming, followed by core ML concepts, deep learning, and specialization in fields like computer vision or NLP. It also covers model deployment and building a portfolio to showcase projects. The emphasis is on practical learning through projects and continuous skill enhancement.

  2. 2
    Article
    Avatar of tdsTowards Data Science·1y

    Why I stopped Using Cursor and Reverted to VSCode

    The author details their decision to revert from using Cursor to VSCode as their primary IDE, citing updated features in GitHub Copilot, cost-effectiveness, and familiarity from prior use. Key considerations include improved compatibility with Jupyter Notebooks and the new availability of advanced LLMs in VSCode. Emphasis is placed on the rapid development pace of GitHub Copilot and Microsoft's resources to enhance functionality, closing the gap with competitors like Cursor.

  3. 3
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    Essential Machine Learning Concepts Animated

    Understanding AI and machine learning is essential for developers. This visually engaging course on freeCodeCamp.org's YouTube channel by Vladimirs from Turing Time Machine simplifies over 100 core ML and AI concepts with animations and real-world analogies. It covers foundational terms, statistical methods, optimization techniques, evaluation metrics, various model types, practical workflow elements, and related disciplines like NLP and object detection.

  4. 4
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    5 Agentic AI Design Patterns

    Explore five agentic AI design patterns that enhance the effectiveness of AI agents through reflection, tool use, reason and act, planning, and multi-agent approaches. Learn how Firecrawl Extract facilitates web scraping by using simple English prompts to extract clean, structured data. Discover additional resources on machine learning techniques and data science provided by Daily Dose of Data Science.

  5. 5
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    Master Database Management Systems

    Learn the essentials of Database Management Systems with an in-depth course from freeCodeCamp.org. The course covers foundational concepts, SQL, database design, and transaction processing using practical examples. It's suitable for exams and technical interviews, equipping students and professionals to efficiently handle data across various applications.

  6. 6
    Article
    Avatar of collectionsCollections·1y

    Comprehensive Course on Building AI Agents

    Gain a thorough understanding of building AI agents through this in-depth guide. Learn about essential concepts, practical workflows, memory mechanisms, agentic flows, and safety guardrails. Explore design patterns, agentic frameworks, and multi-agent systems while optimizing AI agents for production environments. Develop key skills like prompt engineering to create responsive AI agents.

  7. 7
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    AI Agent Crash Course—Part 1

    In this crash course, learn about AI agents and their implementation. It covers the fundamentals, memory for agents, agentic flows, guardrails, implementing agentic design patterns, and optimizing agents for production. The aim is to build autonomous systems that can reason, plan, take actions, and correct themselves, going beyond the capabilities of standalone generative models.

  8. 8
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    Pandas Mind Map

    A detailed mind map of various Pandas methods categorized by their operation types, including I/O methods, DataFrame creation, statistical information, renaming, plotting, time-series, grouping, pivot, and categorical data methods. Additional ML resources and techniques are also provided for developing industry-relevant skills.

  9. 9
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    16 Techniques to Build Real-world RAG Systems

    Scaling a prototype RAG system for real-world use presents significant challenges, such as performance bottlenecks and inefficient retrieval. This guide offers 16 practical techniques to help developers overcome these issues across five key pillars. It also highlights five agentic AI design patterns, including reflection, tool use, ReAct, planning, and multi-agent patterns, which enable LLMs to refine outputs, gather information, and subdivide tasks more effectively.

  10. 10
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    12 Powerful Tools For AI Agents

    A comprehensive guide listing 12 powerful tools included in the CrewAI framework for building AI agents. The tools range from file reading and writing, code interpreting, and web scraping to advanced functionalities like RAG-powered searches and natural language to SQL conversion. Additionally, the post highlights a full crash course on AI agents, covering everything from fundamentals to production optimization.

  11. 11
    Article
    Avatar of detlifeData Engineer Things·1y

    End to End Data Engineering

    This post details the tools, technologies, and concepts essential for data engineering, emphasizing different paths for success based on roles and backgrounds. It highlights the importance of both analytics and infrastructure sides and mentions popular tools like Airflow and Snowflake. The significance of software engineering principles and specific data engineering roles is also discussed.

  12. 12
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    Build an MCP Server in 3 Steps

    This post describes a simple three-step process to build an MCP server using tools like Gitingest and Google AI Studio, enabling the transformation of FastMCP repository data into LLM-readable text. It also highlights the capabilities of the Firecrawl framework, which converts websites into structured formats for AI applications.

  13. 13
    Video
    Avatar of TechWithTimTech With Tim·1y

    How I'd Learn ML/AI FAST If I Had to Start Over

    Advocates a strategic approach to learning AI and ML swiftly in the rapidly evolving landscape of 2025. Emphasizes the importance of critical thinking and practical coding skills, particularly in Python, for effective AI/ML projects. Encourages data literacy as foundational and promotes hands-on experience with AI models, APIs, and machine learning techniques before transitioning into advanced concepts like LLMs and AI agents.

  14. 14
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·51w

    48 Most Popular Open ML Datasets

    A comprehensive compilation of 48 widely-used open machine learning datasets organized by domain including computer vision (ImageNet, COCO), natural language processing (SQuAD, GLUE), recommendation systems (MovieLens, new Yambda-5B), tabular data (UCI datasets, Titanic), reinforcement learning (OpenAI Gym), and multimodal learning (LAION-5B, VQA). Each dataset is briefly described with its primary use case and key characteristics, serving as a reference guide for researchers and practitioners selecting appropriate datasets for their ML projects.

  15. 15
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    25 Most Important Mathematical Definitions in DS

    A visual presentation of crucial mathematical definitions used in Data Science and Statistics, such as Gradient Descent, Normal Distribution, MLE, Z-score, and SVD. The post explains these terms and their significance in various applications like dimensionality reduction, optimization, and data modeling.

  16. 16
    Article
    Avatar of mlmMachine Learning Mastery·1y

    Roadmap to Python in 2025

    Python remains a cornerstone for data science and machine learning in 2025. The post provides a roadmap for learning Python, from basics to advanced machine learning applications, tailored to different proficiency levels. It emphasizes the importance of mastering modern Python features, foundational data science libraries such as NumPy and Pandas, and machine learning frameworks like TensorFlow and PyTorch. The roadmap also highlights specialized tracks for data engineering, AI, web development, and emerging technologies. Staying updated with Python's evolution and leveraging AI tools can further enhance development efficiency and effectiveness.

  17. 17
    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.

  18. 18
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    Time Complexity of 10 ML Algorithms

    Understanding the run-time complexity of machine learning algorithms is essential for efficient model implementation. Popular algorithms like SVM and t-SNE have limitations with large datasets due to their cubic and quadratic time complexities, respectively. Accurate knowledge of these complexities helps in selecting the right algorithm and optimizing performance.

  19. 19
    Article
    Avatar of medium_jsMedium·1y

    0$ to 70.000$ Freelance Journey

    The journey from aspiring basketball player to a successful freelancer that earned $70,000 by completing software development projects globally began during the pandemic in May 2020. After starting with YouTube tutorials and courses on platforms like Coursera, the author eventually found success on Upwork. Key takeaways include the importance of patience, selecting a niche, professional communication, and constant experimentation and learning.

  20. 20
    Article
    Avatar of hnHacker News·1y

    HandsOnLLM/Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book

    The Hands-On Large Language Models repository provides code examples from the book by Jay Alammar and Maarten Grootendorst. The book, known for its visual educational approach with almost 300 custom-made figures, covers practical tools and concepts needed to use Large Language Models. The authors recommend using Google Colab for running examples, but any cloud provider should work. Additional visual guides related to LLMs are also available. The book is a valuable resource for understanding and working with state-of-the-art language models.

  21. 21
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    Open-source Python Development Landscape

    Explore the essential tools for various stages of Python development, including dependency and package managers, monitoring and profiling, virtual environments, linters and style checkers, type checkers, logging, testing, debugging, code refactoring, and code security. These tools are crucial for improving development workflow and code quality.

  22. 22
    Article
    Avatar of spotifySpotify Labs·28w

    Shuffle: Making Random Feel More Human

    Spotify redesigned its shuffle feature to balance statistical randomness with user perception. While the previous implementation used pure randomization (Mersenne Twister), users complained about repetitive patterns. The new 'Fewer Repeats' system generates multiple random sequences, scores them based on listening history and recency, then selects the freshest option. This approach maintains mathematical randomness while reducing perceived repetition. Premium users now get this as default, with classic random shuffle still available as 'Standard Shuffle'.

  23. 23
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    Learn Linear Algebra for Machine Learning

    Linear algebra is a crucial component of machine learning, offering a mathematical foundation for understanding models and algorithms. A new course by Tatev Aslanyan from Lunar Tech on the freeCodeCamp.org YouTube channel covers essential concepts such as vectors, matrices, transformations, and more. This course is suitable for beginners, data scientists, and AI practitioners looking to strengthen their knowledge of linear algebra in machine learning.

  24. 24
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    MCP-powered RAG Over Complex Docs

    Learn how to use MCP to power an RAG application for processing and retrieving information from complex documents. The post details the setup of an MCP server, the creation of GroundX clients and tools, and how to implement these within the Cursor IDE. A comprehensive video walkthrough and a GitHub repository link are also provided for hands-on implementation.

  25. 25
    Article
    Avatar of simplethreadSimple Thread·25w

    Getting Back to Basics

    A hands-on exploration of building machine learning models from scratch, starting with a trading algorithm using regression trees that achieved 220% returns on historical stock data. The author then tackles energy demand forecasting by implementing a feed-forward neural network with backpropagation before upgrading to LSTM networks to handle temporal patterns. Key challenges include addressing gradient explosion through data scaling, switching from ReLU to tanh activation functions, and implementing the Adam optimizer. The final LSTM model with 50 neurons successfully predicts hourly energy interconnection flows without overfitting, demonstrating that foundational ML techniques remain powerful tools for practical time-series forecasting problems.

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