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Data Analytics News
Medium
4d
203
Struggling with DSA as a bio student? Here is your perfect guide:
- Key prerequisites for learning Data Structures and Algorithms (DSA) as a biology student include understanding variables, data types, conditional statements, loops, functions, arrays, basic error handling, etc.
- Starting with Python is recommended for beginners due to its simple syntax and beginner-friendly nature. You can begin with basics like printing patterns, calculating factorial, checking prime numbers, and reversing strings or numbers.
- Beginner resources to start programming include FreeCodeCamp Python Tutorial on YouTube, W3Schools Python, and CS50 by Harvard on edX, which is excellent for beginners even from non-CS backgrounds.
- Once comfortable with programming, you can progress to learning DSA topics like arrays, strings, linked lists, stacks, queues, recursion, trees, graphs, and searching/sorting algorithms through platforms like GeeksforGeeks, LeetCode, and CodeStudio by Coding Ninjas.
- The final tip emphasizes that coming from a biology background does not limit one's ability to learn coding or DSA. Success in this field is achievable with a step-by-step approach, consistency, and regular practice.
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Medium
4d
260
Image Credit: Medium
Building Furthur: Notes from an AI-Native Frontier
- Furthur is a platform that aims to turn prompt insights into shared infrastructure, creating a dynamic ecosystem of insight through a social graph.
- Using a new development stack that responds to natural language commands, Furthur enables the user to describe intent and have a functional prototype emerge.
- The platform positions AI not just as a copilot but as a collaborator, bridging the gap between the user's intent and the actual implementation.
- Furthur accelerates cognitive processes, allowing for faster product development and enabling users to remix prompts across models, pushing the boundaries of language use.
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Medium
6d
308
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The Quantified User
- Designing in a data-driven world raises questions about empathy and user experience, with metrics often overshadowing human stories and experiences.
- Algorithms and metrics can reduce the complexity of human experiences to simple numbers, creating ethical dilemmas in product design and user interaction.
- An overemphasis on metrics can lead to a loss of trust, delight, and genuine human connections, eroding long-term loyalty and dignity.
- Balancing quantitative data with qualitative insights is crucial to prevent design decisions from becoming detached from real user needs.
- The story behind the data is essential, as every user interaction signifies a unique human experience that cannot be fully captured by metrics alone.
- In the pursuit of optimization and engagement, there's a risk of neglecting the human element, leading to unintended negative consequences on mental health and autonomy.
- Designers must navigate the tension between data-driven decisions and human-centered design to create products that are both effective and empathetic.
- Relying solely on metrics can obscure genuine understanding and compromise user trust and satisfaction over time.
- Successful design requires a delicate balance between data-driven insights and human empathy, with both elements informing and enriching each other.
- The future of design will be judged on its ability to protect human values and experiences, not just on achieving numerical targets.
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Designveloper
6d
152
Image Credit: Designveloper
Data-Driven Upskilling: How I Use Skill Analytics to Future-Proof Development Teams
- Data-driven upskilling uses analytics and metrics to identify valuable skills for development teams, ensuring investments align with growth goals.
- By treating skill gaps like product bugs, the approach at Designveloper emphasizes structured skills matrix, analytics pipeline, and targeted micro-learning tracks.
- Mapping talent to results through data-driven upskilling saves costs and prevents delays in product roadmaps by focusing training efforts on essential skills.
- Collecting skills data involves gathering narratives to create a structured skills matrix, ensuring each competency is accurately assessed.
- Analytics tools like GenAI help personalize learning paths based on skills data, while tracking software enables automation and data consistency.
- Clustering skills data reveals career paths, enabling guilds to plan targeted training programs and mentorship initiatives for skill development.
- By leveraging guild systems for micro-learning and feedback loops, Designveloper enhances skill development through practical application and continuous improvement.
- Proving the ROI of skill analytics shows significant productivity gains and risk reduction, contributing to better team performance and adaptability.
- Continuous measurement and data-driven decision-making future-proof development teams, aligning skills with industry demands and enhancing team agility.
- By incorporating skill analytics into the team's culture, organizations can navigate technology shifts effectively and optimize skill development strategies.
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Cloudblog
6d
281
Image Credit: Cloudblog
Maximize BigQuery performance with enhanced workload management
- BigQuery workload management offers control mechanisms for optimizing workloads and resource allocation to prevent performance issues and resource contention in high-volume environments.
- It allows prioritization, isolation, and management of queries and operations within a BigQuery project, ensuring critical workloads receive necessary resources.
- Features like reservations, slot commitments, and auto-scaling enhance cost control and resource allocation.
- Workload management promotes reliability and availability through dedicated reservations and commitments.
- Implementing BigQuery workload management is crucial for organizations seeking efficiency, reliability, and cost-effectiveness in cloud-based data analytics.
- Updates to BigQuery workload management focus on resource allocation, performance optimization, reservation fairness, predictability, flexibility, visibility labels, and autoscaler improvements.
- Reservation fairness ensures slots are distributed equally among reservations, enhancing performance predictability.
- Reservation predictability allows setting an absolute maximum number of consumed slots for better cost and performance control.
- Enhanced flexibility and security allow specifying reservations at runtime and grant role-based access for improved resource allocation.
- Reservation labels provide granular visibility into slot consumption, aiding tracking and optimization of spending.
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Cloudblog
6d
28
Image Credit: Cloudblog
Google is a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms report
- Google has been recognized as a Leader in the 2025 Gartner Magic Quadrant for Data Science and Machine Learning Platforms report.
- This reflects Google's ongoing innovations to meet the needs of data science and machine learning teams, including generative AI.
- AI is driving significant transformations in how organizations operate, compete, and innovate.
- Google Cloud offers a comprehensive suite of AI capabilities, supported by cutting-edge AI research and development.
- Vertex AI is Google's unified AI platform, covering data engineering, analysis tools, MLOps, and gen AI application development.
- The Vertex AI Model Garden provides a selection of over 200 enterprise-ready models for customers to access and customize.
- Google recently launched Gemini 2.5, a highly intelligent AI model capable of transparent step-by-step reasoning.
- Vertex AI is the sole platform with generative media models across various modalities like video, image, speech, and music.
- Google Cloud has helped customers like Radisson Hotel Group improve productivity and revenue through AI-driven personalized marketing.
- Google is focused on multi-agent management and connection of enterprise agents to relevant data for better performance.
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Medium
7d
30
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The Invisible Hand: What I Learned Building a Pricing Automation Agent That Thinks Like a Human…
- Pricing teams struggle with data overload and inefficiencies in translating mental models into pricing decisions.
- Strategic data reduction led to more accurate pricing decisions by cutting token usage and computational overhead.
- Integrating sentiment analysis of customer feedback into pricing workflows enhanced pricing elasticity models, resulting in higher price points for positively perceived products.
- Implementing cost-saving measures in cloud data querying and ensuring a cost-conscious data architecture are essential for economic sustainability in pricing automation.
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Medium
7d
62
Image Credit: Medium
Why Python Is the Best Language to Start Your Data Analytics Journey
- Python is considered the best language to start your data analytics journey due to its beginner-friendly nature and simplicity.
- It is recommended even for those who have never written a line of code before, making it accessible for total beginners.
- Python is highlighted as the easiest and most approachable way to begin learning data analytics, providing a smooth entry point into the field.
- The article also offers a list of free resources for readers to start learning Python immediately after reading.
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Insider
7d
328
Image Credit: Insider
Inside KPMG's $100 million AI investment: How Google Cloud's partnership is fueling the firm's new AI services
- KPMG is expanding its Google Cloud partnership to enhance AI services for clients, with a $100 million investment in KPMG's Google Cloud practice.
- The goal is to tailor AI services to specific customers in industries like retail, healthcare, and financial services, enabling organizations to improve their businesses through faster data analysis.
- KPMG has been incorporating Google Cloud's AI technologies into its operations, developing AI tools like chatbots for answering questions and automating tasks for clients in various industries.
- The partnership with Google Cloud is expected to drive $1 billion incremental growth for KPMG through the expansion of AI services to new clients and industries over the next few years.
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Cloudblog
7d
345
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From data lakes to user applications: How Bigtable works with Apache Iceberg
- The latest Bigtable Spark connector version offers enhanced support for Bigtable and Apache Iceberg, enabling direct interaction with operational data for various use cases.
- Users can leverage the Bigtable Spark connector to build data pipelines, support ML model training, ETL/ELT, and real-time dashboards accessing Bigtable data directly from Apache Spark.
- Integration with Apache Iceberg facilitates working with open table formats, optimizing queries and supporting dynamic column filtering.
- Through Data Boost, high-throughput read jobs can be executed on operational data without affecting Bigtable's performance.
- Use cases include accelerated data science by enabling data scientists to work on operational data within Apache Spark environments, and low-latency serving for real-time updates and serving predictions.
- The Bigtable Spark connector simplifies reading and writing data from Bigtable using Apache Spark, with the option to create new tables and perform batch mutations for higher throughput.
- Apache Iceberg's table format simplifies analytical data storage and sharing across engines like Apache Spark and BigQuery, complementing Bigtable's capabilities.
- Combining advanced analytics with both Bigtable and Iceberg enables powerful insights and machine learning models while ensuring high availability and real-time data access.
- User applications like fraud detection and predictive maintenance can benefit from utilizing Bigtable Spark connector in combination with Iceberg tables for efficient data processing.
- The integration of Bigtable, Apache Spark, and Iceberg allows for accelerated data processing, efficient data pipelines handling large workloads, and low-latency analytics for user-facing applications.
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Pymnts
1w
267
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Top Performing Card Issuers Ace the Cardholder Personalization Test
- Customer lifetime value (CLTV) is crucial for issuers, reflecting cardholder profitability and enabling them to prioritize retention strategies and attract high-value customers.
- High-CLTV issuers achieved success by using multiple monetization strategies, offering personalized financial products, and leveraging data analytics for user engagement.
- Advanced customization and data analytics are key for issuers to drive consumer engagement and provide a seamless, personalized user experience.
- Personalization, user experience enhancements, and strategic investments in platform upgrades are priorities for card issuers to increase retention, profitability, and long-term customer loyalty.
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Siliconangle
1w
351
Image Credit: Siliconangle
Data analytics chip startup Speedata closes $44M funding round
- Data analytics chip startup Speedata has secured $44 million in Series B funding, with participation from various investors including Intel Corp. CEO Lip-Bu Tan and Mellanox Technologies co-founder Eyal Waldman.
- Speedata's accelerator card, the C200, based on a custom chip called Callisto, is designed to optimize data analytics workloads and can be attached to servers via a standard PCIe port.
- The Callisto chip, utilizing a CGRA chip architecture, can perform analytics tasks more efficiently, especially handling branching logic in queries, leading to improved query performance.
- The funding will be used by Speedata to support its go-to-market efforts, with the company highlighting the chip's significant speed advantages in various sectors including healthcare, finance, insurance, and advertising technology.
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Siliconangle
1w
360
Image Credit: Siliconangle
RelationalAI introduces new graph processing features for its Snowflake app
- RelationalAI Inc. introduced new graph processing features for its software applications, aimed at enabling more efficient data analysis.
- These capabilities were unveiled at the annual Snowflake Summit in San Francisco, focused on enhancing the functionality of Snowflake's cloud data platform.
- The update includes new algorithms for graph analysis, such as path finding and egonet analysis, while also introducing support for graph neural networks (GNNs) for tasks like demand forecasting.
- Other added features comprise text-to-reasoner capability, mathematical optimization solvers, and semantic views to enhance data analysis and decision-making within the Snowflake environment.
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TechCrunch
1w
46
Image Credit: TechCrunch
Speedata, a chip startup competing with Nvidia, raises a $44M Series B
- Speedata, a Tel Aviv-based chip startup, has raised a $44M Series B funding round for its analytics processing unit (APU) designed to accelerate big data analytic and AI workloads.
- The Series B round was led by existing investors and strategic investors, totaling Speedata's capital raised to $114M.
- The APU architecture aims to address analytic bottlenecks at the computing level and outperform GPUs in data processing tasks.
- Speedata plans to launch its APU product at Databricks' Data & AI Summit, with claims of significant speed improvements in data processing tasks.
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Medium
1w
226
Image Credit: Medium
Why Self-Service BI Fails (and How AI-Powered Analytics Is Fixing It)
- Despite the promise of self-service BI tools to empower users, many platforms fail to account for varied data fluency, trust issues, and fragmented tooling, leading to underuse and abandonment.
- Users often feel overwhelmed by complex steps required to create reports, resulting in low confidence in interpreting data without assistance from data teams.
- Discrepancies in metrics and definitions lead to a lack of trust in BI tools, with only 3% of employees trusting their company's data, highlighting the pervasive issue.
- AI-powered analytics is transforming the self-service BI landscape by enabling Natural Language Processing (NLP) for easier data querying and providing proactive insights to enhance decision-making.
- Anomaly detection and automated governance through AI help ensure consistency, accuracy, and compliance, addressing governance challenges in self-service BI.
- Moving towards AI-assisted decision intelligence shifts the focus from 'DIY BI' to active collaborations between tools and users, enhancing the data consumption and decision-making process.
- Organizations embracing AI-powered BI must set clear, measurable outcomes, invest in data literacy training, address data quality issues, and foster collaboration to maximize the benefits.
- AI-enhanced BI tools can help organizations make faster decisions, with a 5x greater likelihood of speeding up decision-making than competitors, leading to measurable gains in revenue and operational efficiency.
- While technology plays a vital role, building a strong BI culture and combining AI advancements with human expertise is crucial for successful self-service BI implementation.
- The future of business intelligence lies in leveraging AI to bridge gaps, enhance data-driven decisions, and streamline processes for faster, smarter insights and actions.
- In conclusion, the integration of AI into self-service BI is reshaping the way organizations interact with data, offering a path to more informed, efficient, and successful decision-making in the modern business landscape.
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