It Might Look Normal but This Distribution Will Ruin Your Stats
Explore a deceptive non-normal distribution that mimics normality, its prevalence in nature, and its impact on hypothesis testing. Learn about robust statistics and their applications in data analysis.
Three Musketeers - Sherlock Holmes, Mathematics and Python
Explore how mathematics and Python can be used to solve crime mysteries, inspired by Sherlock Holmes. Learn to apply statistical concepts and Python libraries to tackle forensic puzzles and hypothetical cases.
Non Sequitur - An Exploration of Python's Random Module
Explore Python's random module, covering pseudorandom number generation, Mersenne Twister algorithm, and various probability distributions. Gain insights into randomness applications and practical usage in programming.
Statistics 101 for System Administrators
Python for sysadmins: Analyze system data using statistics, scipy, and matplotlib. Learn to parse logs, calculate distributions, find correlations, and create informative plots to quickly identify performance issues.
Probabilistic Programming in Python
Learn probabilistic programming in Python using PyMC3 for flexible statistical modeling, automated parameter estimation, and uncertainty quantification in data science and scientific research.
Extending Scikit-Learn with Your Own Regressor
Learn to extend Scikit-Learn by implementing a robust Theil-Sen estimator, exploring its advantages over ordinary least squares and understanding Scikit-Learn's design and contribution process.
Machine Learning Under Test
Explore testing strategies for machine learning algorithms, focusing on code quality, domain-specific challenges, and practical implementation techniques for more robust and reliable ML models.
Unveiling the Universe with Python
Explore how Python and Monte Carlo simulations unveil the mysteries of the Universe, from analyzing cosmic microwave background data to revealing the surprising dominance of dark matter and energy.
Planning for the Worst
Explore strategies for handling production issues, from common errors to obscure failures. Learn practical tips, best practices, and code solutions to enhance system reliability and resilience.
Neat Analytics with Pandas Indexes
Explore advanced Pandas indexing techniques for efficient data analysis, covering index types, time-series resampling, and multi-indexing to enhance your productivity in Python analytics.
An Introduction to PyTorch & Autograd
Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.
Introduction to Nonparametric Bayesian Models
Explore flexible machine learning with Nonparametric Bayesian Models. Learn about Dirichlet Process, compare parametric vs nonparametric approaches, and discover relevant Python libraries for advanced data analysis.
Lies, Damned Lies, and Statistics
Explore common statistical fallacies and learn to recognize misleading data interpretations. Gain insights on correlation, bias, and significance to draw accurate conclusions from statistics.
Machine Learning on Non-Curated Data
Techniques for handling missing values and non-normalized categories in machine learning, focusing on practical solutions using scikit-learn and DirtyCat for efficient data analysis without extensive cleaning.
Bioinformatics Pipeline for Revealing Tumour Heterogeneity
Explore a Python-based bioinformatics pipeline for analyzing single-cell DNA sequencing data to reveal tumor heterogeneity, aiding in personalized cancer treatment decisions.
A Gentle Introduction to Causal Inference
Explore causal inference techniques to uncover relationships in observational data, avoid misinterpretation pitfalls, and extract deeper insights beyond simple correlation calculations.
Sliding into Causal Inference, with Python
Explore causal inference tools in Python to simulate parallel realities and answer 'what if' questions. Learn about randomized experiments, differences-in-differences, propensity scores, and synthetic controls.
Descubriendo el corazón de la optimización - Estadísticas más que un concepto
Explora el papel crucial de las estadísticas en la optimización de bases de datos, revelando técnicas avanzadas para mejorar el rendimiento y la eficiencia de los sistemas SQL Server.
Understanding and Visualizing Data Using R in SQL Server
Explore data analysis using R with SQL Server. Learn to connect, validate, and visualize data through statistical methods and reporting tools. Includes sentiment analysis demo of Twitter PASS MVP Summit.
Data Analytics Explained for Business Leaders
Demystifying data analytics for business leaders, bridging the gap between technical and strategic understanding to drive innovation, productivity, and informed decision-making.
Predictive Analytics for Practitioners
Practical guide to predictive analytics covering data preparation, sampling, algorithm strengths/weaknesses, and model accuracy assessment. Ideal for practitioners seeking to enhance their skills in this field.