JetBrains·2yData Exploration With pandas
Learn how to explore and understand data using pandas in PyCharm by leveraging summary statistics and graphical plots. Discover how to distinguish between continuous and categorical variables, generate summary statistics, and visualize data using histograms, box plots, bar charts, and scatter plots. Utilize JetBrains AI Assistant to generate relevant code snippets and enhance your data analysis workflow.
Towards Data Science·2yAdvanced Techniques in Lying Using Data Visualizations
Readers learn how data visualizations can be manipulated to support any narrative by omitting data points, exploiting pattern psychology, selectively categorizing data, and adjusting readability. The content encourages critical evaluation of presented data and emphasizes the importance of ethical practices in data presentation.
Hacker News·2yanthropic-quickstarts/financial-data-analyst at main · anthropics/anthropic-quickstarts
Develop a Next.js application combining Claude's AI capabilities with interactive data visualization for financial data analysis. Features include intelligent data analysis using Claude, multi-format file upload support, and various chart types. Requires Node.js and an Anthropic API key. The project utilizes React, TailwindCSS, and Recharts for the frontend, and Next.js API Routes and Anthropic SDK for the backend. Adaptable for diverse applications beyond financial analysis.
The New Stack·2yVisualizing Data in Python With Matplotlib
Learn to effectively communicate complex datasets using Matplotlib, a versatile Python library for data visualization. This guide covers the basics of creating line plots, bar charts, scatter plots, and subplots, as well as how to customize these visualizations to enhance data comprehension and support data-driven decisions.
Fermyon·2yMeasuring Crowd Engagement with an MQTT-based IoT App
Deploy an MQTT-based IoT app using SpinKube to measure booth engagement at conferences. This setup uses sound sensors to detect foot traffic, removing human error from engagement measures and informing staffing decisions. Components include an MQTT message persister in Rust, a Typescript-based backend API, and a frontend for data visualization. The guide covers deployment on a Kubernetes cluster using the MQTT trigger, ensuring a complete and real-time analysis of booth traffic.
YouTube·2yHow I would learn Data Analysis (If i could start over) | Data Analyst Roadmap 2024
Data analysts play a crucial role in helping organizations make data-driven decisions. Key steps include data collection, data cleaning and pre-processing, exploratory data analysis, and data visualization. Essential skills for the role include knowledge of statistics, Excel, SQL, and Python, along with strong communication skills. To stand out as a fresher, focus on unique projects, leverage data sets of personal interest, and publish dashboards online.
Salesforce Engineering·2yEngineering 360 Dashboard: Transforming Complex Data into Powerful Engineering Insights
The Engineering 360 Dashboard at Salesforce provides actionable insights and data-driven decisions for engineering operations, focusing on developer productivity, agile practices, and high product standards. It integrates with tools like Data Cloud, Tableau, and MuleSoft to eliminate data silos and ensure consistent metrics. Key challenges addressed include data unification, scalability, and maintaining data trust and security. Ongoing improvements include incorporating AI and anomaly detection to enhance the platform’s capabilities.
Metabase·2yHow to build better line and bar charts
Effective data visualization requires thoughtful consideration. Bar and line charts, which are common in analytics, should be used based on the data's nature. Line charts show trends over time, while bar charts emphasize totals. Best practices include sorting data, simplifying visuals, and using meaningful colors. Stacked charts, which add complexity, should be used judiciously to compare accumulative metrics. By organizing data naturally and reducing visual clutter, charts can be made clearer and more insightful.
Game Developers·2yD3 / ThreeJS Network Topology Simulation
Recently tasked with creating a 3D Knowledge Graph for endpoint forensics, the author achieved impressive results but faced challenges in organizing endpoints clearly in 3D, often leading to user confusion. They are seeking insights on better presentation methods, particularly from techniques used in RTS games. Suggestions for isometric gridding and other methods are welcomed.
InfoWorld·2y5 ways data scientists can prepare now for genAI transformation
Generative AI (genAI) is revolutionizing data science by enhancing tools, processes, and deliverables. Data scientists should expand their skills to include unstructured data analysis, integrate AI-generated dashboards, and support citizen data scientists. Key focuses include ethical AI use, leveraging industry-specific AI models, and using graph databases for advanced analytics. GenAI also lowers technical barriers, empowering businesses to leverage data-driven decision-making more effectively.
Medium·2yGraph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads
September brought a wave of exciting topics in ML and AI, showcasing a diversity of tutorials and guides. Highlights include guides on implementing Graph RAG, mastering key Python functions for data scientists, automated prompt engineering, and building AI agents using Python. Additionally, articles covered SQL essentials for data engineers, insights on choosing LLM agent frameworks, and innovative data visualization techniques.