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Data Analytics News
Medium
1w
130
How to Handle Unseen Data with One-Hot Encoding in Machine Learning
- This error occurs because your test data contains categories that weren’t present in the training data.
- One-hot encoding is a method used to convert categorical variables into a format that can be provided to machine learning algorithms.
- One-Hot Encoding transforms each category of a feature into a binary column (0 or 1).
- handle_unknown=’ignore’ can be used in OneHotEncoder from scikit-learn to handle unseen categories gracefully.
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Medium
1w
392
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Why Your SaaS Dashboard Is Overwhelming Users (And How to Fix It)
- Users often find SaaS dashboards overwhelming with excessive data and functionalities.
- To fix this, prioritize key actions, implement progressive disclosure, and provide role-based views.
- Annotate data, add goal benchmarks and use plain language to make it more user-friendly.
- Include a tutorial, tooltips, and celebration of wins to enhance user experience.
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TechBullion
1w
126
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Balancing Innovation and Ethics in Advanced Data Analytics
- The ethical implications of advanced analytics are more significant than ever in the age of data-driven decision-making across industries.
- Organizations must adopt privacy-preserving techniques to maintain user trust, such as decentralized data architectures and anonymization methods.
- Challenges in data analytics include biased algorithms, which can be addressed through bias detection frameworks and fairness-enhancing algorithms.
- Transparency in data collection and usage is critical for maintaining public confidence, along with the establishment of dedicated data ethics committees to align with ethical and regulatory standards.
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Cloudblog
1w
249
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Next 25 developer keynote: From prompt, to agent, to work, to fun
- The developer keynote from the Google Cloud Next event was focused on using AI to remodel a kitchen, showcasing the use of generative AI and Gemini models to suggest designs and materials.
- The keynote highlighted the importance of prompts and agents in AI queries and demonstrated tools like Vertex AI and Agent Development Kit (ADK) that simplify the process of building AI agents.
- Developers were shown how to create multi-agent ecosystems for complex tasks like kitchen remodeling, deploying them with Vertex AI Agent Engine for effective management.
- New advancements such as ADK support for Model Context Protocol and Cloud Assist Investigations were introduced to streamline agent development and diagnostics.
- Gemini models like 2.5 Pro and Model Garden were showcased, along with various IDE options, empowering developers to choose tools that suit their preferences.
- The keynote also featured projects like analyzing pitching techniques in baseball using AI, transforming raw data into sales forecasts, and utilizing specialized agents for different data tasks.
- Exciting tools like the Gemini Code Assist Kanban board were introduced to automate tasks, streamline development processes, and allow developers to focus on creative work.
- The demonstrations emphasized the power and versatility of AI, along with the convenience of tools like Gemini models and Vertex AI services for developers.
- The keynote wrapped up by encouraging developers to explore the full presentation for a deeper understanding of the innovations showcased during the event.
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Cloudblog
1w
246
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Accelerate your analytics: New Bigtable SQL capabilities drive real-time insights
- Bigtable, Google Cloud's NoSQL database, offers real-time insights for massive-scale applications like YouTube and Ads.
- Continuous materialized views expand Bigtable's SQL capabilities, enabling real-time application backends using SQL syntax.
- Bigtable SQL interface is now generally available, enhancing accessibility and streamlining development for various use cases.
- Customers like Augment and Equifax are leveraging Bigtable's SQL capabilities for improved workflows and efficiency.
- Preview functionalities in Bigtable's SQL language include GROUP BYs, aggregations, and structured row keys for data manipulation.
- Continuous materialized views in Bigtable enable real-time aggregation and analysis across various applications.
- Ecosystem integrations like Apache Kafka Bigtable Sink and Apache Flink Connector enhance data streaming and analysis capabilities.
- The launch of Bigtable CQL Client allows developers to migrate applications from Apache Cassandra to Bigtable with ease.
- BigQuery continuous queries, Bigtable CQL Client, and ecosystem integrations offer streamlined real-time analytics with familiar tools.
- The convergence of NoSQL and SQL power in Bigtable empowers developers to leverage SQL for real-time analytics and insights.
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Cloudblog
1w
259
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Looker adds AI-fueled visual, conversational data exploration, continuous integration
- Looker introduces powerful AI capabilities and enhanced reporting experience at Google Cloud Next '25, built on a trusted semantic model.
- Users can now utilize conversational analytics and Google's Gemini models, along with a new reporting interface within Looker.
- AI-driven insights, hidden pattern identification, and predictive capabilities empower users across organizations, reducing data team burdens and enabling focus on strategic work.
- Looker's semantic layer combined with Google's AI provides intelligent insights and automation for data-driven decision-making.
- Gemini in Looker offers conversational analytics, visualization assistant, automated slide generation, and more to simplify data analysis.
- The Conversational Analytics API allows embedding natural language queries into custom applications or workflows for advanced analytics.
- Looker reports enhance data storytelling, exploration, and connectivity, offering drag-and-drop interface, collaboration capabilities, and broad data source access.
- The new reporting environment integrates seamlessly with Gemini in Looker for leveraging conversational analytics.
- Continuous integration in Looker, post-acquisition of Spectacles.dev, streamlines testing and validation processes, ensuring data trust and reliability.
- These advancements signify a substantial progress in delivering an AI-for-BI platform, facilitating easy access to trusted insights and fostering a data-driven culture.
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Cloudblog
1w
225
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What's new with BigQuery — the autonomous data-to-AI platform
- BigQuery is leading the way as an autonomous data-to-AI platform by infusing AI, handling unstructured data, and embedding governance.
- Gemini in BigQuery offers AI-assisted capabilities for data discovery, preparation, analysis, with significant growth in code assist usage.
- New features in Gemini assist with data preparation, data canvas visualization, coding assistance for DataFrames, and improving data and AI governance.
- BigQuery supports an autonomous data foundation, unified analytics for structured and unstructured data, and native integration with Vertex AI.
- Recent innovations in BigQuery include support for Iceberg tables, multimodal capabilities for Python users, enhanced BigQuery ML capabilities, and faster vector search.
- New features like TimesFM model for time-series forecasting, contribution analysis, and governance enhancements help organizations drive data-driven decisions effectively.
- The article also mentions improvements in disaster recovery, workload management, query performance optimizations, and the introduction of new analytics capabilities in BigQuery.
- BigQuery's partner ecosystem contributes significantly through AI integrations and solutions, enabling expanded functionality and improved operational control.
- BigQuery is evolving into an autonomous data-to-AI platform, focusing on lowering barriers to AI-powered analytics and offering unified commercials for easy consumption.
- With a commitment to open standards and innovative features, BigQuery aims to transform organizations by providing advanced data-to-AI capabilities.
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Cloudblog
1w
208
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Introducing BigQuery unified governance: universal, intelligent, and open
- Data quality remains a top barrier for enterprises to tap into the untapped potential of data for AI-driven decisions and innovation.
- Google Cloud Next 25 introduces BigQuery unified governance to address governance complexities and empower organizations with data governance capabilities.
- BigQuery unified governance offers services and tools for simplifying data management, unlocking insights, and fostering innovation by sharing, cataloging, and enforcing quality assurance.
- It integrates metadata management, policy enforcement, and end-to-end data-to-AI lineage using the BigQuery universal catalog, an AI-powered data catalog.
- The BigQuery governance capabilities are unified, intelligent, and open, providing robust tools for metadata management, policy enforcement, and data utilization.
- Noteworthy features of BigQuery unified governance include advanced search capabilities, automated metadata curation, AI-powered knowledge engine, and data products for sharing and governance.
- Security enhancements like data policies on columns and subquery support with row-level security are introduced to ensure secure and controlled data access.
- BigQuery universal catalog aids businesses in moving beyond data silos and operational inefficiencies by automating metadata management and governance.
- Partnerships with third-party catalog providers like Collibra enhance governance capabilities to provide end-to-end visibility, quality, and stewardship across hybrid and multicloud environments.
- By embedding governance in BigQuery and automating metadata management, organizations can drive innovation, accelerate business impact, and strengthen data trust and transparency.
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Cloudblog
1w
131
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Simplify your data platform migration with AI-powered BigQuery Migration Services
- Migrating data workloads to BigQuery, Google's unified Data to AI platform, has been made easier with BigQuery Migration Services.
- BigQuery Migration Services offer cloud-native services that facilitate large-scale transformations for data warehouses and lakes by breaking down migrations into manageable steps.
- New innovations in the service include automated assessment, code translation, data migration, and validation for different data platforms, enhancing the migration process.
- Automated assessments provide insights into existing environments and guide migration planning, supporting platforms like Teradata, Snowflake, Redshift, Oracle/Exadata, and Cloudera/Hive.
- Gemini-enhanced code translations now available in batch and API modes streamline code migration processes from various sources.
- BigQuery Migration Services offer support for incremental updates, permissions migration, and intelligent end-to-end validation, ensuring efficient and accurate data migration.
- Customer successes using BigQuery Migration Services include improved performance, efficiency, scalability, and cost reduction in data platforms.
- To start migrating to BigQuery, one can access BigQuery Migration Services for free, utilize Google Cloud Consulting services, and sign up for special migration incentives.
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7 Likes
TechBullion
1w
63
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The Role of a Degree in a Data Analytics Career Path
- The field of data analytics is highly promising and impactful in today's data-driven economy.
- While a degree in statistics, computer science, or mathematics has been the traditional route into data analytics, the industry requirements are evolving.
- Employers are increasingly valuing skills and real-world experience over academic credentials in the field of data analytics.
- Alternative pathways such as bootcamps, certifications, and self-learning through online platforms are becoming popular choices for individuals pursuing a data analytics career.
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3 Likes
TechBullion
1w
59
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How a Master’s in Data Analytics Can Elevate Your Career
- Pursuing a master’s degree in data analytics can significantly impact career trajectories and open up opportunities in leadership and specialized roles.
- An online analytics master’s program covers a wide range of skills, including statistical analysis, data visualization, predictive modeling, and machine learning.
- Data analytics is widely applicable in various industries such as healthcare and finance, enabling optimization of operations and improved outcomes.
- When considering a master’s in data analytics, key factors to consider include program accreditation, curriculum, faculty expertise, and industry engagement.
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Cloudblog
1w
166
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Power up your BigQuery analysis with Google's new geospatial datasets
- Google Cloud Next 25 unveils new geospatial analytics datasets and capabilities integrated into BigQuery.
- Challenges in geospatial analytics include finding accurate data, integration issues, and scaling programs.
- Google Maps Platform and Earth Engine datasets are now directly accessible in BigQuery for comprehensive geospatial analysis.
- Users can tap into global geospatial data, integrate diverse datasets, and simplify data access within BigQuery.
- New datasets offer fresh insights, data integration, and simplified access without the need for advanced expertise.
- Imagery Insights dataset combines Street View data with AI analysis in BigQuery for infrastructure asset management.
- Places Insights provides aggregate insights for businesses, aiding in location-based decision making.
- Roads Management Insights helps improve road network efficiency and safety through data-driven traffic management.
- Earth Engine in BigQuery enables advanced geospatial analysis of satellite imagery, accessible to SQL users.
- Using Google's geospatial datasets in BigQuery can drive business decisions, optimize operations, and enhance sustainability.
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Medium
1w
269
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The Impact of Real-Time Data Mining on Financial Decision-Making
- Real-time data mining has revolutionized financial decision-making by processing data as soon as it is received, enabling companies to control risks, optimize trades, identify fraud promptly, and enhance customer experiences.
- Applications of real-time data mining in finance include fraud detection, high-frequency trading (HFT) strategies, customer behavior analysis for personalized services, and insurance risk assessment for faster claim settlements.
- For fraud detection, banks utilize real-time data like credit card transactions and AI-powered models trained to flag fraudulent transactions instantly, helping in maintaining customer trust and reducing data breaches.
- In high-frequency trading, AI models leverage massive real-time data to predict stock price movements and help with smarter buying and selling strategies based on short-term changes.
- Customer behavior analysis integrates traditional and real-time data to offer up-to-date views, enabling AI models to create risk profiles, personalize loan products, and make instant credit decisions.
- Insurers use real-time data to assess risks and process claims faster by analyzing bank transactions, weather alerts, crime rates, and historical customer profiles using predictive analytics and graph-based data mining.
- Implementing a successful real-time data mining system in finance requires firms to identify use cases, select appropriate infrastructure, build scalable data pipelines, ensure regulatory compliance, and consider outsourcing services for specialized expertise.
- The decision to opt for outsourcing data mining services is driven by the need for advanced capabilities, scalability, compliance, and performance that may not be feasible with in-house systems for many financial institutions.
- While real-time data mining is essential for financial institutions to mitigate risks, enhance efficiency, and deliver superior customer experiences, strategic planning and adoption are crucial for successful implementation and reaping the benefits.
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16 Likes
Hackernoon
1w
98
Image Credit: Hackernoon
Mastering Exposure Points for Accurate Mobile A/B Testing
- A/B testing is a crucial method for verifying ideas in mobile apps.
- Setting the correct exposure points is important in A/B testing to obtain accurate results.
- Loose exposure points occur before users experience the tested feature, while tight exposure points occur at the exact moment of user experience.
- Avoid stacking changes and plan ahead to improve the accuracy of A/B test results.
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5 Likes
Siliconangle
2w
141
Image Credit: Siliconangle
Riverbed rolls out new AI-powered observability features
- Riverbed Technology is updating its observability platform with new artificial intelligence tools that will help companies fix technical issues quicker.
- The enhancements include the addition of tools like IQ Assist, Predictive AI, and Agentic AI, which provide visualizations of technical issues, offer remediation methods, and enable automation of manual troubleshooting processes through AI agents.
- The update also includes the addition of new modules to the Unified Agent, allowing it to collect data from collaboration services, computer peripherals, and monitor network traffic from employees' computers.
- Most of the enhancements are immediately available, while some tools will launch in the second and third quarters.
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