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Siliconangle
4w
213
Image Credit: Siliconangle
Cyber resilience redefined: Commvault’s Cloud Rewind and recovery capabilities shape the future of cybersecurity
- At AWS re:Invent, experts from Commvault Systems shared advancements in data protection, cloud recovery, and modern cyber resilience.
- Commvault's SaaS solutions now deeply integrate with AWS' cloud-native capabilities, enabling organizations to streamline data protection processes.
- Air Gapped Protect is one of Commvault's latest technologies that offers an isolated environment for secure data storage and recovery.
- Commvault recently debuted its Cloud Rewind capability that provides comprehensive protection for the entire cloud environment, including critical components.
- By replicating an application's environment across AWS regions, organizations can achieve near-high availability and quickly recover from regional failures.
- Recovery-as-Code, a feature of Cloud Rewind, automates the process and enables businesses to resume operations with minimal downtime.
- Configuration drift, where changes in settings accumulate over time, poses a challenge for automated solutions.
- Commvault's ability to address these challenges ensures that enterprises can rely on a unified platform for seamless data management and resilience.
- The technology allows anyone to be able to bring any of their workloads.
- Commvault Systems sponsored this segment of theCUBE, an exclusive interview on theCUBE, SiliconANGLE Media's livestreaming studio.
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Medium
4w
347
Image Credit: Medium
Title: Realty on Chain (ROC) & $ROC Presale: Revolutionizing Real Estate or Another Crypto Gamble?
- Realty on Chain (ROC) aims to democratize real estate by using blockchain technology to tokenize properties, allowing individuals to invest in fractions of prime properties.
- Key features include fractional ownership, a secondary marketplace for trading tokenized fractions, multiple revenue streams, multichain support, and a user-friendly interface.
- The $ROC token presale provides an opportunity to invest in the platform, but it comes with risks such as price volatility, regulatory uncertainty, and smart contract vulnerabilities.
- Investors are advised to conduct thorough research, assess their risk tolerance, understand tokenomics, ask questions, and stay updated before participating in the presale.
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Pymnts
4w
53
Image Credit: Pymnts
AI’s Growing Role Across B2B Payments Will Be Impossible to Ignore in 2025
- Artificial intelligence (AI) is becoming an integral part of business processes, specifically in the back office and finance departments.
- AI-powered tools are automating tasks such as invoice processing, fraud detection, data entry, routing, reconciliation, and reporting.
- AI's scalability is valuable for businesses experiencing growth or fluctuations in transaction volumes.
- While AI adoption faces challenges such as legacy systems and data quality, its benefits include improved operational efficiency, strategic decision-making, accuracy, compliance, and customer experiences.
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Medium
4w
57
Image Credit: Medium
Understanding Data Preprocessing: A Critical Step in Data Science
- Data preprocessing refers to the set of techniques used to prepare raw data for analysis or model training.
- Preprocessing involves cleaning, transforming, and organizing the data to improve its quality and ensure it meets the needs of analytical methods or machine learning models.
- Data preprocessing is essential, not optional. It improves data quality, reduces noise and inconsistencies, handles missing values, and ensures consistency across the entire data pipeline.
- Skipping data preprocessing can lead to problems such as difficulty in detecting patterns, biased results, increased training time, runtime errors, and decreased trust in the model.
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Medium
4w
405
Image Credit: Medium
How to get Real-World Financial Data
- When creating portfolio projects, industry experts recommend using real-world data to showcase additional expertise.
- Yahoo Finance is a popular website for obtaining financial data on various assets like cryptocurrencies, stocks, bonds, and currencies.
- This article demonstrates how to retrieve stock data from Yahoo Finance, focusing on Microsoft as an example.
- The data obtained from Yahoo Finance can be used for various purposes such as analyzing stock information, determining trends, and assessing ESG scores.
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24 Likes
Medium
4w
196
Image Credit: Medium
Mastering Seaborn: The Art of Data Visualization
- Scatter Plots: Perfect for bivariate analysis and uncovering correlations. You can add hue, style, and size to represent multiple dimensions in a single plot.
- Line Plots: The go-to choice for analyzing time-series data or showcasing trends across continuous variables.
- Facet Grids: Designed for categorical data, these plots make it easy to split and organize data, revealing patterns across multiple categories.
- Seaborn is a versatile tool for crafting data stories through visualization techniques.
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11 Likes
Medium
4w
357
Image Credit: Medium
Data Visualization in Python: Unlocking Insights from Your Data
- Matplotlib is the foundation of Python’s plotting ecosystem. It offers a wide range of plot types and high customizability.
- Seaborn builds on Matplotlib and provides higher-level interfaces for statistical graphics, allowing for aesthetically pleasing plots.
- Plotly is great for creating engaging and interactive plots that update in real time, making it suitable for web-based dashboards and presentations.
- Data visualization in Python is essential as it helps convert complex information into intuitive visuals, enabling decision-makers to identify trends and correlations quickly.
- Python is suitable for data visualization due to its rich ecosystem of plotting libraries, readability, extensive community support, and integration with data-focused libraries like pandas.
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Medium
4w
415
Image Credit: Medium
What is Data Science
- Data science combines statistics, computer science, and specialized knowledge to extract insights from data. It helps businesses, governments, and organizations make informed decisions backed by evidence.
- Data analysis has evolved into data science, especially with the growth of big data and advancements in machine learning. Data science uses rich, dynamic datasets that can change at any moment.
- Data scientists are equipped with a toolbox of skills, including programming, statistical analysis, and data visualization. They collaborate with domain experts, software engineers, and business analysts to ensure data-driven decisions are effective.
- Data collection involves gathering structured or unstructured data from sources like surveys, sensors, and public records. For accuracy, data cleaning is crucial.
- Data analysis comes in different types, including descriptive, predictive, and prescriptive analytics. Data visualization through tools like Tableau and Matplotlib simplifies results.
- Machine learning involves teaching machines how to learn from data. Supervised learning teaches the model from known outcomes; unsupervised learning identifies patterns without prior labeling techniques like decision trees and neural networks.
- Data science has practical applications in businesses, healthcare, and social impact, enhancing efficiency, developing innovative solutions, and addressing societal challenges.
- Data quality and ethics are crucial in data science. Maintaining high-quality data through regular audits and validation checks while upholding ethical data collection and usage is important.
- Data science is ever-changing, with new tech emerging regularly. Continuous learning through online courses, workshops, and community forums is essential.
- The future of data science includes rising trends such as automated data science tools and the growing power and potential of data science across various sectors. Professionals can explore various career options through specializations like data engineering or machine learning.
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Hackernoon
4w
41
Image Credit: Hackernoon
How to Integrate AI Into Startup Operations for Enhanced Productivity
- Startups can integrate AI to enhance productivity and data-driven decision-making.
- Automating routine tasks can increase productivity, but AI is less effective for highly creative jobs.
- AI can enhance data analysis, help with employee retention, improve customer service, and create personalized experiences.
- Using AI to measure impact and implement it in areas that boost growth is essential for startups.
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2 Likes
Tech Story
4w
165
Accenture Hires 24,000 Employees in 90 Days, Expanding AI Workforce to 69,000
- Accenture has hired 24,000 employees in just 90 days, expanding its AI workforce to 69,000.
- The rapid workforce expansion is a part of Accenture's commitment to growth and innovation.
- Accenture has set up a center for large language models and generative AI to improve its AI capabilities.
- The company aims to provide cutting-edge AI solutions and facilitate digital transformation.
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9 Likes
Medium
4w
62
Image Credit: Medium
Artificial Intelligence: Transforming the Future of Technology
- Artificial intelligence (AI) involves creating computer systems capable of performing tasks that usually require human intelligence.
- AI can analyze vast amounts of data and identify patterns to make autonomous decisions.
- Machine learning algorithms and neural networks enable AI to continuously improve its performance and make more accurate predictions.
- By leveraging AI’s potential for innovation and efficiency, we can unlock new possibilities and drive progress in various industries.
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3 Likes
Medium
4w
237
How to do back office work on 1st day?
- Understand the role and review the job description.
- Dress appropriately and follow the office dress code.
- Arrive on time, at least 15 minutes before the scheduled hour.
- Get acquainted with the working environment, your desk, and office devices.
- Interact with your team, smile, and greet colleagues with respect.
- Attend training sessions and ask questions to show willingness to learn.
- Manage time effectively and prioritize tasks as per supervisor's instructions.
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14 Likes
Medium
1M
174
What is Brain.js?
- Brain.js is a library that provides a simple and intuitive API for creating and training neural networks.
- Key features of Brain.js include support for both browser and Node.js environments, GPU acceleration using WebGL, and pre-built networks for tasks like image classification and text analysis.
- Some common uses of Brain.js include image classification, text analysis, time series prediction, game development, and robotics.
- Advantages of Brain.js include its easy-to-use API, fast computations with GPU acceleration, and flexibility for various tasks. However, it may have limited scalability and support compared to commercial libraries.
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10 Likes
Cloudblog
1M
179
Image Credit: Cloudblog
Cloud Pub/Sub 2024 highlights: Native integrations, sharing and more
- Pub/Sub has launched several new features and enhancements to help meet the demands of modern streaming workloads, across three key data analytics patterns - Streaming ingestion, Streaming analytics, and Stream sharing and export, to help leverage real-time data for actionable insights and improved decision-making.
- This year, we brought simplification at every step of the journey to ingest streaming data directly into Pub/Sub from various sources, including AWS Kinesis Data Streams.
- Another typical streaming ingestion use case is to ingest batch data into Pub/Sub. Now you can create a subscription to get the data to your choice of sink for downstream processing.
- Customers use Pub/Sub in conjunction with stream processing engines to power streaming-analytics use cases such as anomaly detection, personalization, etc.
- To simplify streaming real-time data from BigQuery to external systems and vendors, you can use BigQuery continuous queries with Pub/Sub.
- New support for OpenTelemetry in Pub/Sub provides a detailed trace of your message lifecycle, including the ability to see a distributed trace.
- We will be launching cross-cloud Kafka sources with Import Topics in early 2025 and plan to further simplify streaming analytics architectures by providing native, lightweight, single-message transformations.
- Get started with Pub/Sub today and start exploring these new features to solve your hardest business challenges.
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Medium
1M
179
Image Credit: Medium
5 Mistakes to Avoid As a Product Analyst
- Refine the questions to be more specific and actionable.
- Begin with the story your data tells.
- Ensure your data is clean and validated.
- Align your bottom line with business goals early on.
- Tailor your presentation to your audience.
- As a product analyst, your role extends beyond just crunching numbers.
- Effective communication is key to making your data work for you.
- You’re telling a storying using data, not just spouting off data or explaining technical details of what you did.
- By avoiding these common pitfalls, you can ensure that your analysis is not only heard but also valued and acted upon.
- Remember, effective communication is key to making your data work for you and your organization.
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