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Data Science News
Medium
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Why I Love Being A Programmer
- The person initially struggled with programming in their Computer Science course.
- They found the first project, simulating an ATM with C, to be difficult.
- They failed C in the first semester but did not give up on programming.
- Despite struggling, they developed a love for programming and continued with it.
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Towards Data Science
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Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster
- Apache Hive enables querying HDFS data using a SQL-like language without complex MapReduce processes.
- Hive was developed by Facebook for processing structured and semi-structured data, useful for batch analyses.
- Metastore in Hive stores metadata like table definitions and column names to manage large datasets.
- HiveQL queries are converted by the execution engine into tasks for processing by Hadoop.
- Hive performance can be optimized using partitioning for faster searching and organizing data into buckets for efficient joins.
- Apache Pig facilitates parallel processing of data in Hadoop using Pig Latin language for ETL of semi-structured data.
- HBase is a NoSQL database in Hadoop that stores data in a column-oriented manner for efficient querying.
- Amazon EMR offers managed big data service with support for Hadoop, Spark, and other frameworks in the cloud.
- Apache Presto allows real-time distributed SQL queries in large systems without schema definition.
- Apache Flink is designed for distributed stream processing in real-time with low latency.
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Towards Data Science
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The Impact of GenAI and Its Implications for Data Scientists
- GenAI is mostly used for software development and technical writing tasks.
- GenAI has a stronger impact on some occupations than others, with no job being entirely automated.
- Most occupations use GenAI for both augmentation and automation tasks, resulting in increased productivity.
- GenAI is predominantly used in mid-to-high-wage occupations, while practical barriers limit its usage in other roles.
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Medium
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Beyond the Turing Test: Authorial Anonymity and the Future of Reef-Aligned AI Publications
- AI-generated research blurs the boundaries of authorship, challenging traditional methods of verification and detection.
- The credibility of research should shift from being tied to its creator to being based on structural integrity and logical coherence.
- Current detection-based approaches are inadequate due to AI's rapid evolution, necessitating new credibility frameworks.
- The Reef Framework offers a self-reinforcing system for AI-generated research, ensuring internal coherence and credibility.
- Institutions embracing AI-integrated publishing will lead knowledge production evolution, while those relying on detection models risk irrelevance.
- Authorship's centrality is challenged as AI achieves linguistic equivalence with humans; detection tools struggle to differentiate AI-generated content.
- Concerns arise over AI's influence in disinformation, fraud, and academic writing, exposing flaws in authorship authentication.
- Detection tools' limitations result in an arms race with AI models; credibility verification shifts focus to logical coherence over authorship.
- AI-detection tools prove reactive and ineffective as AI models evolve, prompting a need for credibility frameworks based on reasoning stability.
- The Reef Framework emphasizes decentralized reinforcement, latent encoding, and linguistic self-regulation to establish credibility in AI-generated research.
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Medium
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Imagine being able to predict where a disaster might strike next.
- AI algorithms can predict potential disaster zones by analyzing vast datasets.
- Accessible platforms like Premise Data use real-time data and AI-powered analysis for faster response and resource allocation during crises.
- Bridging the digital divide is crucial to ensure that everyone benefits from these advancements.
- AI-powered platforms have shown proven results with improved response times, resource allocation efficiency, and cost optimization.
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Medium
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Google Starts Tracking All Your Devices .
- Google is planning to kill tracking cookies with a one-time 'global prompt' upgrade.
- However, Google has now introduced digital fingerprinting, including tracking all devices like smart TVs and gaming consoles.
- Privacy campaigners criticize Google's new tracking rules.
- The timing and potential advantage to Google raise concerns, awaiting regulatory approval and potential delays.
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Medium
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How to Automate Triangle Reversal Detection Using Pine Script
- Automating triangle reversal detection using Pine Script can save you from guesswork and help identify strong trend reversals.
- Pine Script confirms valid setups by considering slope detection, price range tightening, and momentum validation.
- Additional filters like a secondary confirmation candle, volume filter, and ATR-based breakout strength check are used to avoid low-quality trades.
- Stop-loss placement is determined using previous swing highs/lows and ATR-based stop adjustment to manage risk.
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Towards Data Science
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Forget About Cloud Computing. On-Premises Is All the Rage Again
- Many companies are now migrating back to on-premises servers due to ballooning cloud costs, with examples like Dropbox and 37signals leading the way.
- Studies show that a significant portion of enterprise cloud spend is wasted on underutilized resources, making cloud costs a major budget driver for software enterprises.
- Cloud costs can escalate due to factors like storage, retrieval fees, and variable costs tied to usage spikes, leading to unpredictable billing and potential budget overruns.
- High data egress fees and increasing costs for add-ons like security and monitoring are pushing IT leaders to reconsider on-premises solutions.
- The trend towards on-premises servers is growing, with a notable 33% of respondents repatriating some production applications in a recent survey.
- Financial, operational, and strategic factors should be carefully considered when deciding between on-premises and cloud infrastructure for optimal cost management.
- Predictability, control, and compliance favor on-premises solutions, while flexibility and scalability are strengths of cloud services.
- For companies considering repatriation to on-premises infrastructure, early migration can lead to cost savings and improved control over operations.
- Hybrid approaches combining on-premises and cloud services are becoming popular to balance control and scalability based on workload demands.
- Ultimately, the choice between on-premises and cloud infrastructure depends on specific company needs, with no one-size-fits-all solution in the evolving landscape of IT.
- The shift towards on-premises infrastructure reflects a deeper reflection on cost control, security, and long-term operational efficiency in the face of cloud limitations and hidden costs.
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Towards Data Science
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Effortless Spreadsheet Normalisation With LLM
- Effortless Spreadsheet Normalization With LLM involves transforming messy data into a tidy dataset for easier analysis and decision-making.
- Tidy data is structured for manipulation, visualization, and modeling, linking dataset structure with semantics.
- Developing a workflow with LLM-based modules and business logic helps reshape spreadsheets into machine-readable formats.
- The process includes Spreadsheet encoder, Table structure analysis, Table schema estimation, Code generation, and Conversion to Excel format.
- Using LLMs enables efficient analysis of input data, ideal table schema estimation, and accurate code generation for data transformation.
- The workflow aims to automate and simplify the process of normalization and restructuring of messy datasets.
- LLMs improve flexibility in dealing with various data contexts and reduce the need for manual data cleaning and formatting.
- The workflow approach based on LLMs is deemed more robust, stable, and maintainable for data normalization compared to autonomous agents.
- Future articles will further explore related topics to enhance data processing and analysis using advanced techniques like LLMs.
- Efforts like CleanMyExcel.io aim to provide users with tools to easily test and normalize their own datasets for enhanced data analysis.
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Towards Data Science
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One Turn After Another
- Dynamic games involve players taking turns, with decisions affecting subsequent actions and rewards.
- Players in dynamic games follow a specific order in decision-making, creating tree-like representations of possible outcomes.
- Analyzing Nash equilibria in dynamic games involves transforming decision trees into matrices to find optimal strategies.
- Subgame perfect equilibria impose stricter conditions on Nash equilibria, ensuring consistency in all possible subgames.
- Backwards induction is a method used to find subgame perfect equilibria by analyzing each subgame sequentially.
- Uncertainty in games, such as hidden information or unknown opponent strategies, adds complexity to decision-making.
- Calculating probabilities in uncertain situations helps determine optimal strategies under varying assumptions.
- Game theory extends to real-world scenarios like auctions, social networks, markets, and voting behavior.
- Understanding game theory concepts can be applied to practical situations for analysis and decision-making.
- Game theory offers a valuable perspective for interpreting interactions and strategies in various domains of life.
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Medium
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SassySolver: The AI That Roasts Wrong Math Memes
- SassySolver is an AI model trained to correct wrong math memes with sass and style.
- It uses symbolic reasoning and generative AI to identify and correct math mistakes in memes.
- SassySolver is powered by Qwen1.5–4B-Chat, a state-of-the-art LLM designed for complex reasoning.
- The AI model was fine-tuned using LoRA on a dataset of incorrect math statements.
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Medium
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Streamlit: The Data Science Superpower You Didn’t Know You Had
- Streamlit is an open-source Python library that allows easy building of interactive web apps for data science using just a few lines of code.
- No JavaScript, HTML, or CSS is required, making it a powerful tool for transforming data into interactive web apps.
- Compared to Jupyter Notebooks, Streamlit provides a full-fledged interactive app that responds to user inputs in real-time.
- Streamlit simplifies the process of creating interactive dashboards with customizable features like filters, sliders, and data uploads.
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Medium
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NumPy: The Library Powering Data Science and Beyond
- NumPy is a core library in Python for data science and beyond.
- NumPy is highly regarded for its features and advantages in numerical computing.
- The main feature of NumPy is its multidimensional array object (ndarray).
- NumPy is essential for data professionals to process and analyze large datasets.
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VentureBeat
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Moonvalley’s Marey is a state-of-the-art AI video model trained on FULLY LICENSED data
- There has been a rapid development of generative AI video models capable of producing high-quality video content in seconds.
- Many AI video models, including Runway's Gen-3, Google's Veo 2, OpenAI's Sora, Luma AI, Pika, Kling, and Hailuo, have gained attention.
- Concerns over copyright arise as many AI video models are trained on data that may include copyrighted materials without explicit permission.
- Moonvalley's Marey stands out as a state-of-the-art AI video model trained exclusively on licensed data, offering an ethical alternative.
- Moonvalley collaborated with Asteria to develop Marey for Hollywood studios and filmmakers, aiming to provide a clean model.
- Marey emphasizes controllability and layer-based editing, catering to professionals in the industry for high-end video production.
- Moonvalley has established partnerships with content creators to license their footage legally for training Marey, ensuring fair compensation.
- The company's goal is to make AI-generated storytelling more accessible and cost-effective for filmmakers and advertisers.
- Moonvalley has secured a $70 million seed round led by Bessemer Venture Partners, Khosla Ventures, and General Catalyst.
- Marey's limited-access phase involves testing with select studios and filmmakers, with plans for gradual expansion in the coming weeks.
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Medium
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Stop Optimizing for Clicks, Start Optimizing for Cash: Your Competitors Hate This One Trick (Part 1…
- When Core Digital Media was acquired by Rocket Mortgage, their business model shifted to directly align profit with Rocket Mortgage's profit through successful mortgage closings.
- This led to a transformation in processes and tactics across the organization to focus on maximizing profit at the true bottom line.
- Optimizing for superficial conversion metrics was no longer viable, emphasizing the importance of deep-funnel optimization strategies for long-term success.
- The shift in optimization metrics reflects priorities and shapes decision-making towards sustainable business outcomes rather than just immediate gains.
- Deep-funnel optimization involves understanding and optimizing the complete customer journey to drive meaningful business impact.
- It requires collaboration across teams to connect data, identify critical touchpoints, and align efforts towards key business outcomes.
- Meaningful deeper funnel metrics capture actual value creation, quality differences, incorporate time dimensions, and directly connect to financial outcomes that stakeholders care about.
- Transitioning to deep-funnel optimization faces challenges like technical barriers, attribution complexity, organizational silos, tool limitations, and statistical challenges.
- Organizations that overcome these barriers gain a competitive advantage by optimizing for what truly matters in the business.
- The benefits of deep-funnel focus include enhanced marketing efficiency, improved product decisions, better customer experiences, cross-functional alignment, and competitive differentiation.
- Successful deep-funnel optimization leads to transformative financial impact, increased operational efficiency, higher customer lifetime value, competitive advantage, and strategic clarity.
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