996 skills found · Page 6 of 34
statnet / StatnetSoftware Tools for the Statistical Analysis of Network Data (meta-package)
modelop / AugustusAugustus is an open source system for building and scoring statistical models designed to work with data sets that are too large to fit into memory
UW-GAC / GENESISGENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness
Vlad1808 / Anomaly Detection Bootstrapping“Anomaly Detection with Bootstrapping” is a software system that applies a bootstrapped statistical workflow to pinpoint and quantify data abnormalities—such as measurement errors, rounding artifacts, mis-recorded values, mislabeled records, and out-of-distribution samples—within large-scale datasets.
kknet / AlgorithmicTradingSelect a supervised algorithm that can predict stock prices of historical data based on the predictors (statistical indicators). Accordingly formulate a trading strategy based on predicted values to generate orders on same historical training set to backtest how much portfolio would have increased. Select the combination of Machine learning algorithm and Trading strategy to maximize gain for future orders placed automatically via the program.
jgscott / ECO395MECO 395M: Data Mining and Statistical Learning
VIDA-NYU / Data PolygamyData Polygamy is a topology-based framework that allows users to query for statistically significant relationships between spatio-temporal data sets.
guindilla / Coursera Statistics 002Data Analysis and Statistical Inference, Duke University
sinarueeger / Statistical Genetics ResourcesResources (software, data, research article) in the field of statistical genetics
jojo142 / QuantPortfolioMy quant portfolio leverages quantitative finance and data-driven insights to optimize investment strategies. Using advanced models, statistical analysis, and machine learning, I develop systematic trading strategies to capitalize on market inefficiencies and generate alpha.
sccn / SIFTSIFT is an EEGLAB-compatible toolbox for analysis and visualization of multivariate causality and information flow between sources of electrophysiological (EEG/ECoG/MEG) activity. It consists of a suite of command-line functions with an integrated Graphical User Interface for easy access to multiple features. There are currently six modules: data preprocessing, model fitting and connectivity estimation, statistical analysis, visualization, group analysis, and neuronal data simulation.
vrdmr / CS273a Introduction To Machine LearningIntroduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
ghiffaryr / Grplotlazy statistical data visualization
ihejunqiu / PerformanceAnalyzerUnder the iOS platform, the analyzer is a tool which statistics CPU, FPS, Memory, Loading-Time and provides the output of statistical data. And contain SQL execution time monitor base on FMDatabase and UI refresh in main thread monitor
niranjangs4 / WebScrappingWeb Scraping using Python Data mining , Data Analyzing & Data Visualization of the collected Data, The python script is written to fetch all the individual categories the website , The code is written for fetching the data from the first page and it iterates to each and every pages of website ( activities, categories, count of bought), and I used statistical techniques for mathematically analysis and presenting the data into visualization
imker25 / Samba ExporterA Prometheus exporter for statistic data of the samba file server.
echen / Gap StatisticAn implementation of the gap statistic algorithm to compute the number of clusters in a set of numerical data.
tannerjt / ClassybrewClassybrew is a utility for generating statistical class breaks in your data and applying colorbrewer theory to you color palette.
gbarrile / PopEcoModelingOnline course on statistical modeling in population ecology. Includes instructional videos with associated code and data.
LeafYeeXYZ / PsychPenAI加持的在线统计分析和数据可视化工具 / AI-powered web-based statistic and data visualization tool.
