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Data Analytics News
Medium
2w
141
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My 15-day SQL Challenge Entry Highlights
- This article highlights the author's 15-day SQL Challenge entry and their perfect score on three medium and three hard questions.
- The author utilized a recursive CTE in the day 12 question.
- The author shares their thought process and the steps taken to reach the final answers.
- The author also provides a real-world example of how this analysis could be beneficial for a job board like LinkedIn.
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Pymnts
2w
81
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Validating Checking Accounts Gives the Good Guys a Chance
- Many firms struggle with outdated risk assessment processes, putting them at risk for fraud and financial losses.
- ValidiFI, a banking solutions provider, offers a multilayered line of defense using AI and machine learning to validate bank accounts and detect fraud.
- By triangulating data points including payment performance, identity elements, and bank account level data, ValidiFI can identify fraudulent patterns and prevent adverse events.
- ValidiFI's solution helps banking clients improve operational efficiencies by eliminating invalid payments and fraudulent transactions while minimizing the impact on legitimate transactions.
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Medium
2w
380
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This Secret Excel Hack Has Made Me $3 Million (Sometimes More)
- Developing and using synthetic data in Excel has proven to be highly lucrative for the author.
- Synthetic data is a critical tool for generating and testing ideas in various applications and industries.
- The author highlights the importance of using synthetic data to fill gaps in mental models and avoid costly mistakes.
- The Excel plugin discussed in the article aims to make synthetic data generation more accessible to business users and developers.
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Medium
2w
312
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Harnessing Data for Strategic Innovation: Methods to Drive Product Initiatives and Digital…
- Enterprises can leverage data to gain a strategic advantage and foster a data-driven culture.
- Data quality, governance, and integration are crucial for data-driven decision making.
- Advanced analytical tools like predictive and prescriptive analytics unlock actionable insights.
- Companies like Netflix and Starbucks use data to drive product innovation and enhance customer experiences.
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Medium
2w
230
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From AI to Data Engineering: How My Journey Taught Me That ‘Data is Everything’
- The author's journey from studying AI & ML in B.Tech to realizing the importance of data engineering is highlighted.
- Despite the lack of AI focus in the B.Tech syllabus, the author self-taught AI concepts but skipped mathematical fundamentals initially.
- The challenges faced during internships at KHMDL and RBG.AI led to the realization that deeper understanding of AI was necessary.
- The turning point came with a lecture on traditional AI teaching methods focusing on math and statistics by a Phosphene AI Co-Founder.
- This led to a shift in approach: creating custom activation functions, analyzing data distributions, and thinking more like a mathematician.
- The author participated in hackathons, like the Smart India Hackathon, where a Mental Health Web Application named ReboundX was developed.
- Venturing into Frontend Development, the author used React.js to enhance AI models and later collaborated on a Facial Emotion Detection Dashboard.
- The experience with real-world projects like Revealix.ai showed the importance of frontend in enhancing AI usability and user experience.
- A pivotal moment led the author to pivot from AI to Data Engineering, understanding the critical role of structured data in successful AI models.
- By transitioning to Data Engineering, the author grasped the importance of data pipelines, data structuring, and the convergence of engineering and analytics.
- Ultimately, the journey emphasizes that irrespective of AI or Data Engineering, mastering data is fundamental for success in both fields.
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Medium
2w
387
Top 10 AI Skills to Learn in the Age of Artificial Intelligence
- Platforms like Google’s Vertex Explainable AI and IBM’s AI Fundamentals offer beginner-friendly approaches to understanding key AI concepts.
- No-code machine learning tools like Obviously AI and Google’s Teachable Machine are enabling easy construction of predictive models without coding.
- Data storytelling and visualization skills are crucial for making AI insights actionable and impactful in decision-making.
- AI-augmented decision-making involves understanding how to incorporate AI recommendations into decision processes effectively.
- Learning basic principles of prompt engineering can significantly boost the productivity of AI applications.
- Domain expertise is valuable in successfully executing AI projects, especially in industries like healthcare, finance, and marketing.
- AI project management skills are essential for leading successful AI implementations without technical expertise.
- Understanding AI ethics and responsible implementation is crucial in addressing biases and ethical issues in AI systems.
- Being able to select and evaluate AI tools based on business requirements is a key skill in AI adoption decisions.
- Adopting an eternal learning mindset for AI is essential to keep up with the rapid pace of advancements in the field.
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Medium
2w
348
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CTEs vs. Correlated Subqueries: Unraveling SQL’s Hidden Gems with Practical Examples
- A Common Table Expression (CTE) is a temporary workspace that keeps things organized in SQL queries.
- A correlated subquery is a subquery that relies on the outer query for help and gets executed repeatedly for each row in the outer query.
- CTEs are useful for reusing data and improving query clarity, while correlated subqueries are suitable for quick, row-specific checks with small data sets.
- When choosing between CTEs and correlated subqueries, consider factors such as query clarity, data size, reusability, and performance.
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Edsurge
2w
387
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How Open Standards Are Breaking Down Data Barriers
- Colleges and universities face challenges in accessing and leveraging student data for impact, hindered by data silos and unstructured information.
- Institutions also grapple with cultural and organizational barriers, such as faculty mistrust and privacy concerns.
- Adopting open standards like Caliper Analytics and Learning Tools Interoperability can help institutions make data actionable and interconnected.
- Improved data access has positively impacted learner success, like identifying struggling students and empowering learners to track their progress.
- Open standards enable real-time insights and cohesive data pipelines, as seen in projects like the NSF grant with Georgia Tech for AI assistants.
- To enhance data access, institutions should demand open standards, focus on real-time data, and ask relevant questions about learners.
- Challenges remain in fostering trust in analytics, providing professional development, maintaining privacy standards, and scaling solutions like LDRA.
- 1EdTech aims to create an open, trusted, and innovative education technology ecosystem by uniting the education community through open standards.
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23 Likes
Medium
2w
300
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AI-Driven Gameplay Analytics: Unlocking Player Data Value Without Native Integration
- AI-driven gameplay analytics focus on extracting valuable insights from gameplay data without requiring native integration with game code.
- Traditional gaming ecosystems have treated player data as a one-way extraction mechanism, leading to inefficiencies and disconnection between players and the value of their gameplay.
- Non-integrated, AI-powered analytics platforms are revolutionizing how gameplay data is collected, analyzed, and monetized, offering a shift towards dynamic data marketplaces.
- Utilizing advanced machine learning techniques, these systems can extract meaningful insights by analyzing game outputs through sophisticated AI algorithms without direct code-level access.
- The application of technologies like Computer Vision, Natural Language Processing, Behavioral Pattern Recognition, and Edge Computing play a crucial role in enabling non-integrated analytics.
- TURF.GG's approach to gameplay data sovereignty promises to transform gaming economies into decentralized data marketplaces, offering permissionless access and value extraction for players.
- Decentralized systems can tokenize gameplay insights, enable comparative value analysis, provide personalized coaching, and assess cross-game skills, revolutionizing gaming monetization models.
- The emergence of real-time data markets on blockchain infrastructure, like Avalanche, showcases the feasibility of AI-driven analytics and data monetization in gaming.
- Privacy considerations, such as selective disclosure protocols and compliance across jurisdictions, are essential in the collection and monetization of gameplay data.
- The transformation of gaming economies through AI-driven analytics is inevitable, paving the way for predictive game design modeling and cross-reality data integration.
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18 Likes
Medium
2w
374
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How to Scale AI in the Retail Industry with Advanced Data Management
- Adopting AI in the retail industry has the potential to enhance customer retention, personalize experiences, and optimize operations.
- Scaling AI in retail requires a robust data management strategy including acquiring clean datasets and reducing technical debts.
- AI technologies like ML, NLP, and computer vision are reshaping the retail sector with real-time intelligence.
- AI in inventory management helps forecast demand, optimize inventory levels, and track inventory in real-time.
- Personalized marketing in retail utilizes AI for targeted campaigns based on customer preferences and behavior.
- Price optimization with AI involves analyzing market trends, competitor pricing, and customer behavior to set optimal prices.
- AI optimizes supply chain management by leveraging predictive analytics for streamlined operations and efficient production processes.
- Data management is crucial for scaling AI initiatives in retail by ensuring accessibility, quality, and compliance.
- Effective data management includes integration, quality assessment, governance, and infrastructure for AI scalability.
- AI-driven data analytics in retail enables organizations to derive insights, optimize pricing, and enhance customer experiences.
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22 Likes
Medium
2w
43
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BNB Chain’s Next Big Move? A Look at StakeStone (STO) and the Launchpool.
- StakeStone (STO) is set to launch on the BNB chain, promising liquidity infrastructure across multiple blockchains.
- The project aims to enhance liquidity distribution and offers yield-bearing $ETH and $BTC assets.
- Participants can stake $USDT or $BTC to earn $STO tokens, with rewards issued hourly based on staking amounts.
- Staking for StakeStone began on April 1st and will end on April 6th. It is an opportunity to explore liquidity protocols in the crypto space.
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2 Likes
Medium
2w
397
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Robustness in Optimal Transport Theory: Building Reliable AI Models
- Robustness in optimal transport theory focuses on creating AI models that perform reliably even when faced with different data, noise, changing conditions, or limited information.
- It is crucial for AI systems in critical areas like healthcare, transportation, and finance to ensure reliability when faced with unexpected scenarios.
- Optimal transport theory deals with efficiently moving resources while minimizing costs, often involving comparing and transforming probability distributions in AI.
- Robustness is necessary due to data noise, changing environments, and discrepancies between training and real-world data in machine learning models.
- Adapting to unexpected scenarios is a key aspect of robustness, such as optimizing delivery routes accounting for disruptions like road construction.
- The robust Wasserstein distance is a measure of maximum possible distance between distributions in uncertainty sets, aiding in conservative estimates for robustness.
- DRO (Distributionally Robust Optimization) optimizes AI model parameters for worst-case expected loss across various data distributions to enhance robustness.
- Entropy regularization and data augmentation are common techniques used to improve robustness in optimal transport problems by smoothing solutions and introducing variations in training data.
- Robust optimal transport helps AI models perform consistently against adversarial examples, improve generalization across domains, and create more stable generative models in deep learning.
- Practical approaches to evaluate the robustness of AI models include exposing them to challenging conditions, quantifying robustness using metrics like worst-case accuracy, and testing performance under distribution shifts.
- The reliability and robustness provided by optimal transport theory play a critical role in building AI systems that can be trusted in crucial domains with real-world uncertainties.
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23 Likes
Medium
2w
21
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Optimal Transport Theory: From Mathematical Concepts to Real-World Applications
- Optimal transport theory tackles efficient resource movement from sources to destinations, utilizing mathematical frameworks to minimize costs.
- Real-world applications, like goods delivery and resource allocation, benefit from optimal transport theory's systematic approach.
- Game-based examples like the Candy Delivery Game illustrate how mathematical concepts optimize practical resource allocation problems.
- Using cost matrices, optimal paths can be determined by minimizing total transport costs in scenarios like candy delivery mazes.
- The Apple Distribution Game introduces capacity constraints, mirroring real-world resource allocation challenges.
- Mathematically, optimal transport problems aim to minimize total transport costs while ensuring resources reach their destinations efficiently.
- Leonid Kantorovich's linear programming reformulation in the 1940s made optimal transport problems more solvable in varied settings.
- Applications of optimal transport theory span supply chain optimization, market equilibrium, and image processing in diverse fields.
- Real-world applications may involve factors like varying costs, time constraints, and uncertain conditions, addressed by robust optimal transport solutions.
- Computational solutions for optimal transport problems often involve linear programming or specialized algorithms for efficiency in diverse scenarios.
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Medium
2w
52
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How to Design for Data-Heavy Products
- Designing for data-heavy products can be a balancing act, requiring user-friendly interfaces that simplify the experience while delivering essential data.
- Progressive disclosure is a strategy that organizes and reveals information in manageable layers, making the interface approachable and efficient.
- To design intuitive data-heavy products, consider visual hierarchy and prioritize important information to guide users' attention.
- By leveraging these strategies, designers can create actionable and effective data-heavy products that balance complexity and clarity.
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3 Likes
Cloudblog
2w
56
Image Credit: Cloudblog
Unlock AI with IT and OT data powered by Manufacturing Data Engine with Cortex Framework
- Breaking down data silos between IT and OT data is crucial for leveraging AI in manufacturing, as highlighted in Google Cloud's latest release of Manufacturing Data Engine (MDE) at Hannover Messe.
- Enhancements to MDE in 2024 focused on integrating OT and IT data, along with Cortex Framework, expanding its application across various data sources beyond ERP and CRM.
- The new MDE release introduces features like Development Mode, historical metadata linking, and Configuration Packages to drive faster AI outcomes by grounding IT and OT data.
- Development Mode allows flexibility in deleting configuration objects, facilitating experimentation with new data models for faster innovation cycles.
- Historical metadata linking enables accurate representation of historical data by inserting it correctly into the timeline, aiding in analyzing trends and optimizing operations.
- Configuration Packages streamline merging factory floor data with enterprise systems, bridging the IT and OT gap for transformative use-cases within Cortex Framework.
- The integration of multimodal data from machines with Cortex Framework provides a holistic view of operations, unlocking new Gen AI use cases and enhancing manufacturing intelligence.
- The partnership with Deloitte offers packaged services to facilitate customer success in leveraging MDE capabilities, while Google Cloud reaffirms its commitment to continuous innovation in empowering manufacturers.
- Manufacturers can witness the latest MDE release in action at Hannover Messe and Google Cloud Next '25, with live demonstrations showcasing how MDE drives industrial transformation.
- Google Cloud's efforts aim to empower manufacturers in thriving in the digital era, inviting them to experience MDE features at industry events to explore advancements in data and AI platforms.
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