According to the 2024 Stack Overflow Developer Survey, online resources such as videos, blogs, and forums were the top choice for developers across all age groups worldwide to learn code, with younger developers were more likely to use online sources. The second most popular learning resource for most of the groups were online courses or certifications, which were most popular among those aged 25 to 34 years and 35 to 44 years, with around 54 percent and 52 percent of respondents, respectively. Books and physical media were more popular among developers aged 25 and older compared to younger developers.

Data of Experiment 3

This dataset contains data from California resident tax returns filed with California adjusted gross income and self-assessed tax listed by zip code. This dataset contains data for taxable years 1992 to the most recent tax year available.

Australian Standard Geographic Classification (ASGC) coding indexes from 1981-2011 in numerous formats.

This archive contains the experimental data associated with the paper "Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes" by Riccardo Caramellino, Eugenio Piasini, Andrea Buccellato, Anna Carboncino, Vijay Balasubramanian, and Davide Zoccolan.

Comprehensive YouTube channel statistics for Learn Code With Durgesh, featuring 346,000 subscribers and 67,055,116 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Technology category and is based in IN. Track 1,553 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil - [https://doi.org/10.1002/ppj2.20036]

The ZIP file contains:

• MCT1_Rcode.R - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.
• MCT1... .csv - 3 CSV data files required by the R script.
• MCT2_Rcode.R - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.
• MCT2... .csv - 3 CSV data files required by the R script.
• R_RooThProcessing.R - R code for aggregating root traits from RooTh software.
• Modelling folder - OpenSimRoot with model parameters and root data used in manuscript.

Financial overview and grant giving statistics of Code for Progress

The Woodland Carbon Code is a voluntary standard, initiated in July 2011, for woodland creation projects that make claims about the carbon they sequester (take out of the atmosphere).

Woodland Carbon Code statistics are used to monitor the uptake of this new voluntary standard, and are published quarterly since January 2013.

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Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

Zip Code; Median household income; Unemployed (ages GE 16); Families below 185% FPL; Children (ages 0-17) below 185% FPL; Children (ages 3-4) enrolled in preschool or nursery school; Less than high school; High school graduate; Some college or associates degree; College graduate or higher; High school graduate or less. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

ObjectiveTo evaluate the National Electronic Injury Surveillance System’s (NEISS) comparability with a data source that uses ICD-9-CM coding. MethodsA sample of NEISS cases from a children’s hospital in 2008 was selected, and cases were linked with their original medical record. Medical records were reviewed and an ICD-9-CM code was assigned to each case. Cases in the NEISS sample that were non-injuries by ICD-9-CM standards were identified. A bridging matrix between the NEISS and ICD-9-CM injury coding systems, by type of injury classification, was proposed and evaluated. ResultsOf the 2,890 cases reviewed, 13.32% (n = 385) were non-injuries according to the ICD-9-CM diagnosis. Using the proposed matrix, the comparability of the NEISS with ICD-9-CM coding was favorable among injury cases (κ = 0.87, 95% CI: 0.85–0.88). The distribution of injury types among the entire sample was similar for the two systems, with percentage differences ≥1% for only open wounds or amputation, poisoning, and other or unspecified injury types. ConclusionsThere is potential for conducting comparable injury research using NEISS and ICD-9-CM data. Due to the inclusion of some non-injuries in the NEISS and some differences in type of injury definitions between NEISS and ICD-9-CM coding, best practice for studies using NEISS data obtained from the CPSC should include manual review of case narratives. Use of the standardized injury and injury type definitions presented in this study will facilitate more accurate comparisons in injury research.

Codes for "Dynamic Oligopoly and Price Stickiness" in Mathematica and MATLAB.Abstract: How does market concentration affect the potency of monetary policy? To tackle this question we build a model with oligopolistic sectors. We provide a formula for the response of aggregate output to monetary shocks in terms of sufficient statistics: demand elas- ticities, concentration, and markups. We calibrate our model to the evidence on pass-through, and find that higher concentration significantly amplifies non-neutrality. To isolate the strategic effects of oligopoly, we compare our model to one with monopolistic competition recalibrated to ensure firms face comparable demand functions. Finally, we compute an exact Phillips curve for our model. Qualitatively, our Phillips curve incorporates extra terms relative to the standard New Keynesian one. However, quantitatively, we show that a standard Phillips curve, appropriately recalibrated, provides an excellent approximation.

This dataset includes soil wet aggregate stability measurements from the Upper Mississippi River Basin LTAR site in Ames, Iowa. Samples were collected in 2021 from this long-term tillage and cover crop trial in a corn-based agroecosystem. We measured wet aggregate stability using digital photography to quantify disintegration (slaking) of submerged aggregates over time, similar to the technique described by Fajardo et al. (2016) and Rieke et al. (2021). However, we adapted the technique to larger sample numbers by using a multi-well tray to submerge 20-36 aggregates simultaneously. We used this approach to measure slaking index of 160 soil samples (2120 aggregates). This dataset includes slaking index calculated for each aggregates, and also summarized by samples. There were usually 10-12 aggregates measured per sample. We focused primarily on methodological issues, assessing the statistical power of slaking index, needed replication, sensitivity to cultural practices, and sensitivity to sample collection date. We found that small numbers of highly unstable aggregates lead to skewed distributions for slaking index. We concluded at least 20 aggregates per sample were preferred to provide confidence in measurement precision. However, the experiment had high statistical power with only 10-12 replicates per sample. Slaking index was not sensitive to the initial size of dry aggregates (3 to 10 mm diameter); therefore, pre-sieving soils was not necessary. The field trial showed greater aggregate stability under no-till than chisel plow practice, and changing stability over a growing season. These results will be useful to researchers and agricultural practitioners who want a simple, fast, low-cost method for measuring wet aggregate stability on many samples.

Project Summary:The Interstate War Initiation and Termination (I-WIT) data set was created to enable study of macro-historical change in war initiation and termination. I-WIT is based on the Correlates of War (COW) version 4 list of interstate wars, and contains most of the interstate wars in the COW list; those excluded were wars the researchers believe do not meet the COW criteria for interstate wars. For each war, research assistants (RAs) coded a host of variables relating to war initiation and termination, including whether each side issued a declaration of war, the political and military outcomes of the war (which are coded separately), and the nature of any agreement that concluded the war. One argument made in several publications based on these data (also part of a larger book project) is that the proliferation of codified international humanitarian law has created disincentives for states to admit that they are in a state of war. Declaring war or concluding a peace treaty would constitute an admission of being in a state of war. As international humanitarian law has proliferated and changed in character over the past 100 years or so, it has set the costs of compliance – and also the costs of finding a state to be out of compliance – very high. Thus, states avoid declaring war and concluding peace treaties to try to perpetrate a type of legal fiction – that they are not at war – to limit their liability for any violations of the laws of war. Data Abstract: The data cover the period from 1816 to 2007 and span the entire world. Dozens of graduate and undergraduate RAs working between 2004 and 2010 compiled existing data from secondary sources and, when available online, primary sources to code variables listed and described in the coding instrument. RAs were given a coding instrument with a description and rules for coding each variable. Typically, they consulted both secondary and primary sources, although off-site archival sources were not consulted. They filled in a spreadsheet for each war with variable values, and produced a narrative report (henceforward, “narrative”) of 5-10 pages that gave background information on the war and also justified their coding. Each war was assigned to at least two RAs to check for inter-coder reliability. If there was disagreement between the first two RAs, a third RA was brought in to code discrepant variables for that war. Where possible, a 2/3 rule was followed in resolving discrepancies. Remaining discrepancies are addressed in the “discrepancy narrative,” which lists the discrepancies and documents final coding decisions. Files Description: Some sources were scanned (e.g., declarations of war or peace treaties) but for the most part, RAs took notes on their assigned cases and produced their coding and narratives based on these notes. The coding instrument and the discrepancy narrative are included in the data documentation files, and all data files produced – including original codings that were discrepant with later codings – are included in the interest of allowing other researchers to make their own judgments as to the final coding decisions. A companion data set – C-WIT (Civil War Initiation and Termination) – is still under construction and thus not shared at this time.

This dataset contains the ICD-10 code lists used to test the sensitivity and specificity of the Clinical Practice Research Datalink (CPRD) medical code lists for dementia subtypes. The provided code lists are used to define dementia subtypes in linked data from the Hospital Episode Statistic (HES) inpatient dataset and the Office of National Statistics (ONS) death registry, which are then used as the 'gold standard' for comparison against dementia subtypes defined using the CPRD medical code lists. The CPRD medical code lists used in this comparison are available here: Venexia Walker, Neil Davies, Patrick Kehoe, Richard Martin (2017): CPRD codes: neurodegenerative diseases and commonly prescribed drugs. https://doi.org/10.5523/bris.1plm8il42rmlo2a2fqwslwckm2 Complete download (zip, 3.9 KiB)

Notes:

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This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

Subscribers can access export and import data for 80 countries using HS codes or product names-ideal for informed market analysis.

South Korea Imports: Volume: Philippines: PrepFeather&Down&ArtMadeofFeather data was reported at 0.000 Ton th in Mar 2025. This stayed constant from the previous number of 0.000 Ton th for Feb 2025. South Korea Imports: Volume: Philippines: PrepFeather&Down&ArtMadeofFeather data is updated monthly, averaging 0.000 Ton th from Jan 2000 (Median) to Mar 2025, with 297 observations. The data reached an all-time high of 13.700 Ton th in Dec 2005 and a record low of 0.000 Ton th in Mar 2025. South Korea Imports: Volume: Philippines: PrepFeather&Down&ArtMadeofFeather data remains active status in CEIC and is reported by Korea Customs Service. The data is categorized under Global Database’s South Korea – Table KR.JA016: Trade Statistics: Import: Volume: HS Code: 2 Digits: Top 20 Countries.