996 skills found · Page 5 of 34
honghanhh / Coursera Practical Data Science SpecializationSolutions on Practical Data Science Specialization on Coursera (offered by deeplearning.ai)
caisonglu / CachemasterCachemaster is similar to VMTOUCH, but with more functions. Such as kick page cache, warmup/readahead data, lock data in mem, stat page cache, stat page cache in realtime mode, by file or directory~ Personally, I think the most usefule function is real-time-statistic of page cache. You can see the page cache thrashing when kernel do page reclaiming.
Okes2024 / Data Driven Assessment Of Soil Heavy Metal Contamination Rivers State Nigeria Using Multivariate This study leverages data science methodologies to quantitatively assess soil contamination in Joinkrama, Ahoada West LGA, Rivers State, Nigeria. Employing a structured geospatial and statistical pipeline, soil samples were collected across stratified
elliottmorris / R For Political DataA repo for analysis of political data in the R statistical programming language.
LuisM78 / Occupancy Detection DataThis is a repository for data for the publication: Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Luis M. Candanedo, Véronique Feldheim. Energy and Buildings. Volume 112, 15 January 2016, Pages 28-39.
dr-mushtaq / Machine LearningA complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
Okes2024 / Analyzing Behavioral Patterns In ADHD PatientsThis project generates synthetic ADHD and control patient data, applying machine learning and statistical analysis to uncover behavioral patterns, visualize differences, and evaluate predictive models for cognitive and activity-related traits.
exploripy / ExploripyPre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.
nicholasdille / PowerShell StatisticsStatistical analysis of data on the command line
praveen6235 / Statistical Analysis And Visualization Of Adidas US Retail DataThe project enforces data cleaning and visualization techniques, using python libraries such as Pandas, Matplotlib, Seaborn and numpy, to provide meaningful insight into an employee attraction in a visually attractive format
Okes2024 / VR Data Science For Exposure Therapy Analysisproject leverages synthetic VR session data to analyze patient responses in exposure therapy. Using statistical and visualization techniques, it identifies trends in anxiety reduction, session engagement, and heart rate stabilization to support effective mental health treatment strategies.
great-northern-diver / LoonA Toolkit for Interactive Statistical Data Visualization
worldbank / SPIRepository containing raw data, code, and final output for the Statistical Performance Indicators project of the World bank
cutterkom / DestatiscleanrImports and cleans data from official German statistical offices to jump-start the data analysis
pmartR / PmartRThe pmartR R package provides functionality for quality control, normalization, exploratory data analysis, and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data.
xxxiaol / QRDataAre LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data
TermuxHackz / SteghideSteghide is a steganography program that is able to hide data in various kinds of image- and audio-files. The color- respectivly sample-frequencies are not changed thus making the embedding resistant against first-order statistical tests.
ay-lab / FitHiChIPStatistically Significant loops from HiChIP data
yitaohu88 / Empirical Method In FinanceWinter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
suneelpatel / Statistics For Data Science Using PythonUsing Python, learn statistical and probabilistic approaches to understand and gain insights from data. Learn statistical concepts that are very important to Data science domain and its application using Python. Learn about Numpy, Pandas Data Frame.
