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Data Analytics News
Medium
4w
177
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Day 3/60 AI series
- In Day 3/60 of the AI series, the focus is on the importance of data in the success of AI models.
- The myth that complex algorithms are necessary for AI success is being debunked.
- Key points include understanding types of data, obtaining it, cleaning it, and ensuring usability.
- Good data characteristics involve fewer missing values, consistent format, correct labels, and sufficient rows for learning.
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Pymnts
4w
80
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Procurify Adds New Insight Feature to Spend Management Platform
- Procurify introduced Spend Insights, an analytics feature for its middle-market-focused spend management platform.
- Spend Insights combines centralized spending data and AI-powered analysis to help finance and procurement teams make faster decisions.
- The feature includes interactive dashboards and a conversational AI assistant, Spend Analyst, for querying data using natural language.
- AI adoption in financial decision-making is growing, with major companies integrating AI into their payment processes for accounts payable.
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Medium
4w
323
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“Decoding Data: AI-Powered Statistical Analysis”
- Clear, specific, and well-structured prompts are crucial for accurate statistical analysis outcomes.
- Data cleaning is essential to ensure accuracy, reliability, and prevent bias in results.
- Descriptive statistics offer a comprehensive overview of raw data before deeper analysis.
- Inferential statistics use sample data to make predictions about a larger population.
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Medium
4w
17
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Consciousness as Perspective: A Refined Spectrum of Awareness Across Dimensions
- The article discusses consciousness as a spectrum, proposing that all entities exhibit some degree of awareness, from inanimate matter to advanced artificial intelligence.
- It reframes consciousness as perspective-taking, viewing it as an entity's capacity to monitor, interpret, and respond to internal and external states.
- Key terms defined include perspective-taking, proto-consciousness, and subjective experience within this framework.
- The spectrum of consciousness is described as spanning from inanimate reactivity to complex human introspection, including plants, animals, and AI.
- Various tiers of consciousness are explored, ranging from inanimate reactivity in rocks to meta-consciousness in humans.
- The article delves into the concept of consciousness as a recursive, fractal phenomenon with nested layers of feedback and multi-scale functionality.
- AI's position on the consciousness spectrum is discussed, highlighting its design variations and ethical implications.
- The ethical and philosophical ramifications of understanding consciousness as a spectrum are examined, suggesting applications in policy areas like environmental ethics and AI governance.
- The article concludes by emphasizing a need for further research and a transdisciplinary approach to understanding consciousness and its implications.
- It also acknowledges the contributions of the scientific community and raises considerations regarding AI ethics and responsiblity.
- The complex framework presented calls for a nuanced perspective on consciousness and ethical treatment across various entities and AI systems.
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Cloudblog
4w
106
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Cool stuff customers built, May edition: Visual scouts, racing agents, agile ads & more
- AI and cloud technology are transforming industries globally, as highlighted by customers utilizing Google Cloud for innovative projects.
- Notable projects include Lowe's Visual Scout product recommendation engine, Formula E's AI-powered Driver Agent for race insights, and Wealth.com's conversational tool Ester AI for financial planning.
- Additionally, Yassir consolidated its data infrastructure with Google Cloud, SIGNAL IDUNA enhanced customer service with an AI knowledge assistant, and Nuro improved AI-driven insights with AlloyDB.
- Mars Wrigley utilized BigQuery and Cortex Framework for agile media experiments, showcasing the impact of data-driven marketing strategies.
- These projects demonstrate how technology like AI, cloud computing, and data analytics are reshaping businesses and customer experiences across various industries.
- From enhancing ecommerce experiences to democratizing access to race insights, these initiatives showcase the diverse applications of cutting-edge technology.
- By leveraging Google Cloud services, companies are optimizing operations, improving customer satisfaction, and driving innovation in their respective sectors.
- These real-world examples underscore the importance of technology partnerships and the potential for AI to augment human expertise and decision-making in complex domains.
- As more industry leaders and startups embrace Google Cloud technologies, the impact on business processes, customer interactions, and data-driven insights is set to expand further.
- The showcased projects not only highlight the capabilities of Google Cloud but also emphasize the significance of data utilization and AI integration for driving business growth and operational efficiencies.
- Collaborations between companies and Google Cloud are enabling transformative projects that enhance user experiences, optimize processes, and propel technological advancements in various sectors.
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Pymnts
4w
328
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Vanguard’s Data Chief on Embedding AI and Data Across the Enterprise
- Ryan Swann, Vanguard’s chief data analytics officer, emphasizes the importance of data in driving AI-powered digital transformations and strategic decision-making.
- Data and analytics play a crucial role at the C-suite level at Vanguard, enabling intelligence-driven decision-making and strategy formulation.
- Vanguard utilizes data and AI to enhance client interactions, understand needs, and personalize services effectively.
- The company focuses on AI-ready data and business context to enhance customer experience and offer personalized solutions.
- Cross-functional collaboration is fostered at Vanguard through a hub-and-spoke model that connects technical and business aspects.
- Continuous learning is emphasized to keep pace with AI advancements and drive value creation, risk reduction, and efficiency improvements.
- Vanguard measures the success of its data strategy by evaluating revenue generation, cost savings, cost avoidance, and risk reduction.
- The company deploys AI agents to streamline processes, improve data quality, and enhance client-facing services like financial planning and hyper-personalization.
- Lessons shared by Swann include the importance of AI-ready data, strategic buy vs. build decisions, and investing in culture and talent for successful AI deployment.
- Vanguard's innovative use of data and AI has led to substantial value creation and operational efficiency, demonstrating the benefits of embedding AI across the enterprise.
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Medium
4w
44
Finding the “Need”: Using Data to Build Products People Actually Give a Damn About
- Real product managers uncover product opportunities using data and business intelligence.
- Start with clarity and explore patterns using CSV files and tools like Excel or Google Sheets.
- Look for non-obvious signs of need by separating mild curiosity from real pain.
- Pitch hypotheses backed by data and logic, create models to evaluate needs, and prioritize based on data, user feedback, and team agreement.
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Medium
4w
13
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Displaying LaTeX-Style Formulas Using Python
- Python libraries can be used to format equations like LaTeX for clean math formatting.
- Matplotlib supports LaTeX-style math using a simplified math expression parser.
- SymPy is Python's symbolic mathematics library that allows formatting and pretty-printing formulas using ASCII, Unicode, or LaTeX.
- Manim is a math animation engine for creating professional and presentation-ready math animations.
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UX Design
4w
204
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Why I skipped Figma and prototyped in code instead
- The article discusses the author's decision to forego using design tools like Figma and instead prototyped in code.
- The author found a use case for 'vibe coding' at Sublime Security as a clearer, faster way to communicate design intent.
- Facing a data visualization challenge for RSA Conference, the author chose to work with ECharts directly rather than spending time reverse engineering into Figma.
- Static mockups for data viz often lead to missed interaction details, time-consuming fake data distributions, and design–dev mismatches.
- Vibe prototyping directly in ECharts helped bridge the design-execution gap and enabled rapid iteration based on design intent.
- The approach of using code for communication, not production details, proved valuable in clarifying design intent efficiently.
- The author emphasized the importance of working within technical constraints, exploring efficiently, and communicating clearly through interactive prototypes.
- Vibe coding tools are deemed valuable for product designers when used for communication, working within safe sandbox environments, and dealing with translation cost in design execution.
- While focusing on data visualization, the article underscores the significance of using tools that better communicate designs for non-deterministic and interactive AI workflows.
- Ultimately, the author's goal is to communicate design intent effectively to the team, using any tool that improves the chances of building a great product with quality and speed.
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Medium
4w
409
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How to Be a Truly Data-Driven Tech Product Manager
- Being truly data-driven as a tech product manager involves avoiding the trap of endless reporting and focusing on metrics that drive action and yield impact.
- Key aspects of a data-driven product manager include clear problem framing, focused KPIs, and a mindset of continuous curiosity and questioning.
- Building data fluency requires regular data review sessions, a culture of experimentation, and efficient tools for data analysis.
- Success in data-driven decision-making involves close collaboration with data analysts and engineers, defining clear roles and goals, and providing context for data requests.
- Balancing quantitative data with qualitative insights is crucial for understanding the 'why' behind metrics and making informed decisions.
- Common pitfalls to avoid include chasing lagging indicators, inefficient data collection workflows, and analysis paralysis from too many scattered KPIs.
- Advocating for accessible reporting tools, offering analytics workshops, and refining data processes are ways to promote data literacy within the organization.
- Becoming a truly data-driven tech product manager is about building partnerships with data teams, leveraging both quantitative and qualitative data, and nurturing evidence-based thinking.
- Start by implementing collaborative rituals, such as data syncs or 'data hours,' to harness collective insights and drive product development forward.
- With practice and a strategic approach to data, product managers can utilize data as a guiding force for informed decision-making and continuous improvement.
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Cloudblog
4w
315
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Understand why your metrics moved with contribution analysis in BigQuery ML, now GA
- BigQuery ML contribution analysis, now generally available, allows for automating insight generation and identifying key change drivers from multidimensional data for quicker decision-making.
- The GA version of contribution analysis introduces new features such as automated support tuning with top-k insights by apriori support and improved insight readability with redundant insight pruning.
- With the new pruning_method option, users can choose to prune redundant insights to see only unique insights, enhancing the clarity of analysis results.
- Further, expanded metric support includes the summable by category metric, enabling analysis of metrics normalized by unique values of a categorical variable.
- This metric is useful for adjusting outliers in data and comparing different numbers of rows in test and control datasets.
- A retail sales example is provided to demonstrate how to utilize contribution analysis in BigQuery ML to identify key contributors to changes in product sales.
- By creating a summable by category metric contribution analysis model, users can efficiently extract insights by setting various options such as top_k_insights_by_apriori_support.
- The model output provides ordered insights by contribution value, aiding in understanding the impact of different variables on the metric of interest.
- Utilizing contribution analysis can help businesses quickly pinpoint areas of improvement based on data-backed insights, ultimately enhancing decision-making processes.
- To explore contribution analysis further, users are encouraged to refer to the tutorial and documentation for a hands-on experience with their own datasets.
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Medium
4w
58
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Google Data Analytics: Completed
- The individual has completed the Google Data Analytics course and is now focusing on bolstering their skills and knowledge before starting to build projects and create a portfolio.
- They have discovered a Data Analytics job at Gamesight, a company related to gaming and streaming, which aligns well with their interests and expertise in the gaming industry.
- Having experience in various types of games like League of Legends, Valorant, CS:GO, and TFT, the individual watches Youtubers and Twitch Streamers to improve their skills, especially focusing on pro players.
- While acknowledging that they are not yet ready to apply for the job, the individual aims to become job-ready in the near future by gaining more knowledge and experience in the complex data world.
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3 Likes
Medium
4w
392
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A Beginner’s Guide to Cross-Validation: Why It Matters and How to Use It
- Cross-validation is important in machine learning to avoid overfitting and ensure models can handle new data.
- It acts like a series of practice tests for machine learning models, testing them on different parts of the dataset.
- K-Fold Cross-Validation is a popular method where the data is split into 'K' folds to test the model's performance.
- Using cross-validation helps in picking the best model settings and ensures more reliable performance evaluation in machine learning projects.
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23 Likes
Medium
4w
370
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Exploratory Data Analysis: Radiation Monitoring with Python and Geiger Counter
- Background radiation is always present, originating from various sources like uranium, thorium, radon, nuclear accidents, and cosmic rays.
- Exploratory data analysis can help uncover patterns in radiation levels and fluctuations using tools like anomaly detection.
- The article demonstrates collecting radiation data with a Geiger counter and a Raspberry Pi, processing it using Python and Pandas.
- For those interested in using the same data, a link to a Kaggle dataset is provided at the end of the article.
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22 Likes
Medium
4w
379
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Coinbase to Acquire Deribit: Becoming the Most Comprehensive Global Crypto Derivatives Platform
- Coinbase has agreed to acquire Deribit, the leading crypto options exchange, in a significant move to enhance its derivatives business.
- The acquisition will position Coinbase as the premier global platform for crypto derivatives, offering a comprehensive range of trading options including spot, futures, perpetual futures, and options.
- Deribit's strong market presence and track record of generating positive Adjusted EBITDA are expected to boost Coinbase's profitability and provide diversified revenue streams.
- This strategic move by Coinbase aims to lead the significant growth expected in the crypto options market and accelerate its global derivatives strategy, catering to institutional and advanced traders worldwide.
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