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Scientificworldinfo
1w
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How Big Data Is Shaping the Future of Smart Cities
- Big Data plays a vital role in the development and functioning of smart cities.
- Big Data processes data from various sources such as sensors, cameras, social media, IoT devices to gain actionable insights.
- Big Data is essential for real-time decision-making and long-term strategic planning.
- Big Data can be used to transform urban transportation by optimizing traffic light patterns and predicting maintenance requirements.
- Smart grids that use Big Data ensure improved load balancing, integration of renewable energy sources, and detailed energy usage statistics to promote conservation.
- Big Data is essential in predicting disease outbreaks, optimizing healthcare delivery and resource allocation in smart cities.
- Big Data helps law enforcement agencies to deploy resources effectively and reduce crime.
- Smart waste management systems use Big Data to optimize collection routes, reducing fuel consumption and operational costs.
- Challenges in implementing Big Data include data privacy, integration of legacy systems, and high initial costs.
- Big Data holds transformative potential for the future of smart cities, making cities more adaptive, efficient, and personalized in their services.
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Medium
1w
132
Image Credit: Medium
How to Scrape Reddit — The Easy, Cheap Way
- To scrape Reddit without needing an expensive and complicated crawler, you can use AutoSERP.dev, a service that allows you to extract data from any website using a search query.
- Register for a free account on AutoSERP.dev, create an endpoint, and obtain an endpoint ID and an API key.
- Use a specific search query to limit the results to Reddit and include the keyword you're looking for, and you can also add filters like a date filter.
- Specify what data you want to extract, such as a short summary of the initial poster's problem, and use the requests library in Python to retrieve the data from AutoSERP.dev.
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Medium
1w
157
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Statistics: Part 1 — The Basics
- Statistics is a superpower for making sense of the world by uncovering patterns, trends, and relationships.
- There are two main types of statistics which are Descriptive and Inferential.
- Descriptive statistics helps us summarize and organize data to make it more understandable, and it includes three measures: measures of central tendency, measures of dispersion, and measures of frequency distribution.
- Inferential statistics allows us to make predictions or conclusions about a larger group based on a smaller sample and is used for estimation, hypothesis testing, and regression analysis.
- Statistics is used in various fields such as predicting the weather, improving medical treatments, and market research for businesses.
- The application of both types of statistics helps to make data-driven decisions and leads to improved outcomes in many industries.
- Understanding and applying these statistical tools are essential for tackling complex problems and advancing knowledge.
- Descriptive statistics is used in various industries, such as business, healthcare, engineering, to simplify and summarize data.
- Inferential statistics has practical uses in different fields for making predictions and decisions based on sample data.
- Statistics play a crucial role in supporting decision-making across various fields and enabling data-driven decisions.
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Medium
1w
213
Image Credit: Medium
Leveraging LLMs to Build Semantic Embeddings for BI
- Large Language Models (LLMs) help in generating semantic embeddings that turn unstructured data into numerical vectors for machines to understand.
- LLMs capture the true meaning of text in context to generate embeddings that understand relationships between words and phrases in high dimension space.
- These embeddings are enriched with metadata for fast retrieval based on semantic similarity rather than simple keyword matching.
- These embeddings enhance AI applications like smarter chatbots, recommendation systems, sentiment analysis and topic modelling.
- LLMs generate embeddings that group documents by meaning rather than just keywords, which powers smarter recommendations and improves customer experiences.
- Integrating LLM-generated embeddings into your BI systems can personalize dashboards, tailor content, and metrics to individual roles, and enhance knowledge retrieval through smarter semantic search.
- Before diving into implementation, creating the right development environment for experimentation, selecting flexible, scalable frameworks and tools, and identifying practical use cases provides a structured foundation for success.
- Implementing LLM generated semantic embeddings into the BI workflows involve structured steps of setting up pre-trained models and tools, testing with small data, leveraging flexible frameworks, integrating a Vector Database, enhancing workflow with auxiliary tools, and embedding in BI workflow.
- LLM Generated Semantic Embeddings can transform business intelligence strategy by enhancing search, improving recommendations and streamlining document management.
- By integrating LLM generated semantic embeddings into BI workflows organizations can minimize risks and comprehensively unlock the potential of semantic embeddings.
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Medium
1w
341
Image Credit: Medium
Types of Data Analysts: A Beginner-Friendly Guide Trending in 2025:
- Types of Data Analysts: A Beginner-Friendly Guide Trending in 2025
- Business analysts focus on understanding the needs of a business and using data to improve operations.
- Financial analysts specialize in analyzing financial data to support investment decisions and budgeting.
- Marketing analysts focus on understanding customer behavior and improving marketing strategies.
- Healthcare analysts use data to improve patient care and operational efficiency in healthcare organizations.
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Medium
1w
281
Image Credit: Medium
Understanding Principal Component Analysis
- PCA is a technique used to highlight variation and bring out strong patterns in a dataset.
- The main objective of PCA is to identify the directions (principal components) where the data varies the most and project the data onto these new axes to simplify it.
- By reducing the number of dimensions, PCA helps in focusing on the most important information without getting lost in the noise.
- PCA is a powerful tool for breaking down complex data and improving data analysis and insights.
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Medium
1w
42
Image Credit: Medium
AI Dev Tips #12: AI LLM Website Scraper review
- The LLM Scraper library simplifies data extraction across various content formats.
- This library supports multiple LLM providers and includes code-generation for re-usable scraping scripts.
- It leverages function calling for precise extraction and can be incorporated into AI Agents and other apps.
- HackerNews and GitHub Trending are used as examples in the tutorial provided in the repo.
- The tutorial highlights the importance of abiding by website terms and not abusing this service.
- A .env file needs to be created to put in the API key environment variables.
- The tutorial also highlights that html scraping may lead to a maximum token size issue that gives an error. Workarounds include changing the format setting in the code from html to another one like format: 'markdown'.
- The library works by defining a schema for extracting structured data.
- The main features of the LLM Scraper library, such as code-generation and data extraction, work and are useful.
- The article also provided information about the author's background and experience in software development.
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2 Likes
Medium
1w
188
Image Credit: Medium
Data Exploration with Agentic AI: Exploring the Titanic Dataset using SmolAgents
- With multi-agent frameworks powered by AI models, detailed EDA can be performed by asking questions.
- SmolAgents is a versatile library from Hugging Face that simplifies complex workflows.
- A custom tool, get_titanic_data, loads the Titanic dataset for exploration.
- Questions were posed to the agent to understand the dataset's structure and handle missing values and outliers.
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11 Likes
Medium
1w
141
Programming Methodologies
- Modular Programming is focused on writing code as functions with minimal interaction between them.
- Bottom-up algorithm design starts with existing primitives and gradually constructs more complex features.
- Structured programming uses code structures to write programs in a procedural decomposition style.
- Algorithm analysis involves checking correctness, simplicity, space complexity, and time complexity.
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8 Likes
Medium
1w
34
Image Credit: Medium
10 Do’s and 5 Don’ts for Creating a Dashboard in Tableau
- Define who will use the dashboard. Executives prefer high-level KPIs, while analysts may need detailed breakdowns.
- Decide what question your dashboard should answer, such as monitoring sales trends, customer behavior, or operational efficiency.
- Connect Tableau to clean, reliable, and accurate data sources, such as SQL databases, Excel files, or cloud platforms.
- Provide filters (e.g., date, region, category) to let users customize their views without overwhelming them.
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Medium
1w
115
Image Credit: Medium
Big Data Meets Private Equity: How AI Is Unlocking Hidden Investment Opportunities
- Data analytics is crucial for Private Equity (PE) firms to navigate a diverse client portfolio and complex data.
- Challenges such as data overflow and outdated strategies can be addressed through a data-driven approach.
- Data analytics strategy is the need of the hour for PE firms to remain competitive in a dynamic market.
- PE firms can benefit from transparency, optimized investment opportunities, and revenue growth through data analytics.
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Pymnts
2w
189
Image Credit: Pymnts
Advise Raises $1.6 Million for Consumer Goods SaaS Platform
- Advise, an Irish software-as-a-service (SaaS) platform for consumer goods makers, raised 1.55 million euros (about $1.6 million).
- The funding will help the company develop its artificial intelligence-powered SaaS platform, designed to promote better pricing strategies for fast-moving consumer goods (FMCG) firms.
- Advise plans to expand its workforce and extend its market reach after a year of significant revenue growth.
- The platform aims to democratize and simplify the data analytics process, allowing manufacturers to have more influence and competitive edge in retail.
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11 Likes
Medium
2w
356
Image Credit: Medium
The Future of Data Analysis: How AI is Revolutionizing the Role of Data Analysts.
- AI is revolutionizing the role of data analysts by automating repetitive tasks.
- AI empowers data analysts to focus on higher-value tasks and strategic insight.
- Data analysts need to adapt and embrace AI to future-proof their careers.
- AI transforms data analysts into key players in shaping the future of organizations.
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Siliconangle
2w
972
Image Credit: Siliconangle
AWS enriches partner network with new AI services
- Amazon Web Services (AWS) is enhancing its partner network with new artificial intelligence (AI) services.
- AWS is focused on developing faster AI technologies for the benefit of its partners.
- The company announced new partner services, including the Business Outcomes Xcelerator Program and the Managed Services Program.
- AWS aims to foster collaboration with partners to provide AI solutions that meet customer needs.
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9 Likes
Medium
2w
90
Dynamic Product Ecosystem Approach
- The Dynamic Product Ecosystem is designed to foster collaboration and deliver results in product development.
- It emphasizes the interconnectedness of engineering, design, data science, and business insights.
- The approach focuses on continuous improvement, customer involvement, and data-driven decision-making.
- It promotes the use of appropriate tools and fosters a culture of collaboration and ownership.
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