Wed.Jul 16, 2025

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10 Python Math & Statistical Analysis One-Liners

KDnuggets

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter 10 Python Math & Statistical Analysis One-Liners Python makes common math and stats tasks super simple.

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Conversations with Trailblazing Women: Madhura Raut, Lead Data Scientist

Dataconomy

The latest guest on our series is Madhura Raut, Lead Data Scientist and the seed engineer for global leader tech platform for human capital management. As an internationally recognized expert in artificial intelligence and machine learning, Madhura has made extraordinary contributions to the field through her pioneering work in labor demand forecasting systems and her role in advancing the state-of-the-art in time-series prediction methodologies.

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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

Flipboard

Blog Top Posts About Topics AI Career Advice Computer Vision Data Engineering Data Science Language Models Machine Learning MLOps NLP Programming Python SQL Datasets Events Resources Cheat Sheets Recommendations Tech Briefs Advertise Join Newsletter The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs This article explains how to turn messy raw data into useful features that help machine learning models make smarter and more accurate predictions.

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AI learns language like a kid learns to read

Dataconomy

Researchers at Harvard University, Freya Behrens, Florent Krzakala, and Lenka Zdeborová, including first author Hugo Cui, have conducted a study analyzing the internal processes of artificial intelligence systems, specifically focusing on self-attention layers in language models. This research, detailed in “ A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention ,” published in the Journal of Statistical Mechanics: Theory and Experime

AI 140
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Precision in Motion: Why Process Optimization Is the Future of Manufacturing

Speaker: Jason Chester, Director, Product Management

In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.

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ML Project – Credit Card Fraud Detection using Random Forest

Data Flair

Program 1 Credit Card Fraud Dataset import pandas as pd import numpy as np from tkinter import * from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt import seaborn as sns... The post ML Project – Credit Card Fraud Detection using Random Forest appeared first on DataFlair.

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Hexadecimal numbering

Dataconomy

Hexadecimal numbering, or base-16, offers a fascinating way to represent numeric values using a compact and efficient system. This numbering scheme plays a vital role in various fields, particularly in computing and programming, where clarity and precision are paramount. Understanding hexadecimal can provide insights into both practical applications and complex mathematical concepts.

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Chain of thought monitorability: A new and fragile opportunity for AI safety

Hacker News

AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known AI oversight methods, CoT monitoring is imperfect and allows some misbehavior to go unnoticed. Nevertheless, it shows promise and we recommend further research into CoT monitorability and investment in CoT monitoring alongside existing safety methods.

AI 139
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AWS launches Kiro a new AI-powered coding tool

Dataconomy

Amazon Web Services (AWS) has introduced Kiro, a new integrated development environment (IDE) that utilizes artificial intelligence agents to bring more structure and reliability to the software development process. The tool, now available in a preview version, is designed to address the challenges associated with “ vibe coding ,” a practice where developers use AI with minimal guidance, which can lead to inconsistencies.

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I'm switching to Python and actually liking it

Hacker News

I’ve started writing more Python code lately (because of… AI, you know). In this post, I share the tools, libraries, configs, and other integrations I use for building production-grade Python applications following a frontend-backend architecture.

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ML Project – Tourist Destination Recommender System using Random Forest

Data Flair

Program 1 Tourist Recommendation Dataset import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, classification_report import matplotlib.pyplot as plt import seaborn as sns import matplotlib.pyplot as plt... The post ML Project – Tourist Destination Recommender System using Random Forest appeared first on DataFlair.

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Airflow Best Practices for ETL/ELT Pipelines

Speaker: Kenten Danas, Senior Manager, Developer Relations

ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!

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Meta paid a $10,000 bounty for a major AI privacy flaw

Dataconomy

Meta addressed a security flaw within its Meta AI chatbot, which permitted users to view the private prompts and AI-generated responses of other individuals. Sandeep Hodkasia, founder of AppSecure, disclosed this vulnerability to TechCrunch , confirming Meta paid him a $10,000 bug bounty reward for his private disclosure filed on December 26, 2024. Hodkasia stated Meta deployed a fix on January 24, 2025, adding that no evidence of malicious exploitation of the bug was found.

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ML Project – Salary Prediction Based-on Skills and Experience using Gradient Boosting

Data Flair

Program 1 Salary Prediction Dataset # Salary Prediction Based on Skills and Experience using Gradient Boosting import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.pipeline... The post ML Project – Salary Prediction Based-on Skills and Experience using Gradient Boosting appeared first on DataFlair.

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Revisiting k-Means: 3 Approaches to Make It Work Better

Flipboard

The k-means algorithm is a cornerstone of unsupervised machine learning, known for its simplicity and trusted for its efficiency in partitioning data into a predetermined number of clusters.

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ML Project – Stock Price Prediction using Gradient Boosting

Data Flair

Program 1 Stock Market Dataset # Import libraries import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import GradientBoostingRegressor from sklearn.metrics import mean_squared_error, r2_score # Load dataset df = pd.read_csv("D://scikit_data/stock/stock_market_dataset.csv") df["Date"] = pd.to_datetime(df["Date"])... The post ML Project – Stock Price Prediction using Gradient Boosting appeared first on DataFlair.

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Whats New in Apache Airflow 3.0 –– And How Will It Reshape Your Data Workflows?

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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Opportunities for Causal Machine Learning in Precision Oncology

Flipboard

Published In Copyright © 2025 Massachusetts Medical Society. For personal use only.

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Weave (YC W25) is hiring an AI engineer

Hacker News

About What Happens at YC? Apply YC Interview Guide FAQ People YC Blog Companies Startup Directory Founder Directory Launch YC Startup Jobs All Jobs ◦ Engineering ◦ Operations ◦ Marketing ◦ Sales Startup Job Guide YC Startup Jobs Blog Find a Co-Founder Library SAFE Resources Startup School Newsletter Requests for Startups For Investors Hacker News Bookface Open main menu Apply for F2025 batch.

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AI course creators: The secret to creating interactive courses

Dataconomy

As technology continues to evolve the education landscape, artificial intelligence is one of the most effective allies for course creators. It provides innovative learning solutions that promote more accessibility to education and better learning experiences. This article examines how AI could change course creation and some advantages you can gain from it.

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Why drones and AI can’t quickly find missing flood victims, yet

Flipboard

For search and rescue, AI is not more accurate than humans, but it is far faster.

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What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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Mira Murati’s AI startup lands massive $2B seed

Dataconomy

Thinking Machines Lab, an AI startup founded by Mira Murati, OpenAI’s former chief technology officer, officially finalized a $2 billion seed funding round on Monday, as confirmed by a company spokesperson to TechCrunch. This funding round was led by Andreessen Horowitz. The transaction establishes the startup’s valuation at $12 billion.

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Turning Data Into Decisions: How Analytics Improves Transportation Strategy

Smart Data Collective

Cookies help us display personalized product recommendations and ensure you have great shopping experience. Accept X By using this site, you agree to the Privacy Policy and Terms of Use. Accept Analytics Analytics Show More Turning Data Into Decisions: How Analytics Improves Transportation Strategy 3 Min Read How Data Analytics Improves Lead Management and Sales Results 9 Min Read How Data Analytics Reduces Truck Accidents and Speeds Up Claims 7 Min Read Interior Designers Boost Profits with Pre

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Stephen Wolfram : Mind vs Machine How AI is Redefining Human Thought

Flipboard

What if the very nature of human thought—our creativity, intuition, and ability to connect ideas—was both our greatest strength and our most glaring …

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A Leadership Blueprint for Driving Trusted, AI-Ready Data Ecosystems

The Data Administration Newsletter

As AI adoption accelerates across industries, the competitive edge no longer lies in building better models; it lies in governing data more effectively. Enterprises are realizing that the success of their AI and analytics ambitions hinges not on tools or algorithms, but on the quality, trustworthiness, and accountability of the data that fuels them.

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A Guide to Debugging Apache Airflow® DAGs

In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

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Where the Jobs Are Going: 20 High-Growth Jobs Reshaping the Workforce

Flipboard

Each of these occupations is set to grow by at least 20% in the coming decade. We walk you through what each field entails and the interesting details worth knowing.

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AI and Data Engineering: The Importance of Validation in Complex Data Systems

The Data Administration Newsletter

In today’s digital systems, vast volumes of information are produced daily. However, simply collecting large amounts of data is not enough. That data must be reliable and well-structured to serve any real purpose.

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Reimagining Work with AI-Native Thinking

Flipboard

At AIM’s flagship event, MachineCon GCC Summit 2025, Pavitar Singh, CEO and co-founder of UnifyApps, and Ramaswamy PV, EVP and global CIO of Virtusa, …

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Meet the Fellow: Elena Sirotkina

NYU Center for Data Science

This entry is part of our Meet the Fellow blog series, which introduces and highlights faculty who have recently joined CDS. Meet incoming CDS Faculty Fellow Elena Sirotkina , who is joining CDS this fall. Sirotkina holds a PhD in Political Science from the University of North Carolina at Chapel Hill. Sirotkina’s research focuses on computational political behavior, where she develops computational approaches and methods for political science by leveraging computer vision tools and behavioral la

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Agent Tooling: Connecting AI to Your Tools, Systems & Data

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

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A former engineer reveals what it’s really like inside OpenAI

Dataconomy

Calvin French-Owen, an engineer previously engaged with a new OpenAI product, resigned three weeks ago, subsequently detailing his year-long tenure in a blog post that offers insight into the company’s operational culture and the development of its Codex coding agent. French-Owen clarified his departure was not due to internal conflict but rather a desire to return to startup founding, building on his experience as a co-founder of Segment, a customer data company acquired by Twilio in 2020

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All in the Data: Where Good Data Comes From

The Data Administration Newsletter

Let’s start with a truth that too many people still overlook — not all data is good data. Just because something is sitting in a database or spreadsheet doesn’t mean it’s accurate, trustworthy, or useful.

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How Anthropic Just Validated The End Of Wall Street’s $500,000 Quant Jobs

Flipboard

Just weeks after reporting on how AI startups are democratizing quantitative analysis across financial markets, Anthropic has launched Claude for Financial Services, a comprehensive platform that transforms how finance professionals analyze markets and make investment decisions.

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Understanding Data Pipelines: Why They Matter, and How to Build Them

The Data Administration Newsletter

Building effective data pipelines is critical for organizations seeking to transform raw research data into actionable insights. Businesses rely on seamless, efficient, scalable pipelines for proper data collection, processing, and analysis. Without a well-designed data pipeline, there’s no assurance that the accuracy and timeliness of data will be available to empower decision-making.

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How to Modernize Manufacturing Without Losing Control

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri