Best of Statistics — July 2024

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   Article

   freeCodeCamp·2y

   What Are Monte Carlo Methods? How to Predict the Future with Python Simulations

   Monte Carlo methods utilize randomness to solve complex problems in fields such as physics, finance, and engineering by approximating solutions through repeated random sampling. These methods include various types, such as Classical, Bayesian, and Markov Chain Monte Carlo (MCMC). The post offers an explanation of these methods and their applications, along with a practical Python implementation of the Hamiltonian Monte Carlo variant using TensorFlow Probability for modeling a 2D Gaussian distribution.

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2. 2
   Article

   Hacker News·2y

   The Math of Card Shuffling

   A deck of cards needs to be riffle shuffled seven times to achieve a sufficiently random order. This is derived from the mathematical concept of permutations, considering a standard deck of 52 cards. Additionally, shuffling one card at a time would require an average of 236 single card riffles to completely randomize the deck. The post references a Numberphile video discussing these and other card shuffling facts.

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3. 3
   Article

   freeCodeCamp·2y

   What are Markov Chains? Explained With Python Code Examples

   Markov chains are mathematical models used to predict future events based on current states, with applications in various fields such as finance, genetics, and robotics. This guide explains the key types of Markov chains, including Discrete-Time, Continuous-Time, and Hidden Markov Models, along with a Python code example demonstrating how to implement a Gaussian Hidden Markov Model. Markov chains are valued for their 'memoryless' property and their ability to model complex systems efficiently.

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   Article

   Machine Learning News·2y

   6 Statistical Methods for A/B Testing in Data Science and Data Analysis

   A/B testing is crucial in data science for informed business decisions and optimizing revenue. The post outlines six key statistical methods: Z-Test for large samples with known variance, T-Test for small samples with unknown variance, Welch’s T-Test for unequal variances and sample sizes, Mann-Whitney U Test for non-normally distributed data, Fisher’s Exact Test for small sample sizes, and Pearson’s Chi-Squared Test for categorical data. Each method has specific applications and purposes, aiding in accurate data-driven insights.

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   Article

   KDnuggets·2y

   Generating Random Data with NumPy

   NumPy is a powerful Python package for mathematical and statistical computations, including generating random data. It provides tools to create random data from various distributions such as uniform, normal, Poisson, binomial, and exponential distributions. The package also allows setting seeds for reproducibility and combining different distributions to create custom data sets. This versatility makes NumPy essential for tasks like data simulation, synthetic data generation for machine learning, and statistical sampling.

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   Article

   KDnuggets·2y

   Bayesian Thinking in Modern Data Science

   Bayesian thinking transforms decision-making by updating initial beliefs with new evidence, enhancing predictions and decision-making in data science. It involves key concepts such as prior probability, likelihood, posterior probability, and evidence. Applications include Bayesian inference, predictive modeling, and Bayesian neural networks, which manage uncertainty and provide probabilistic forecasts. Tools like PyMC4, Stan, and TensorFlow Probability support Bayesian analysis for various tasks.

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   Article

   KDnuggets·2y

   Introduction to Statistics: A Statology Primer

   A collection of tutorials from Statology, this primer covers essential introductory statistics concepts. It explores the importance of statistics, the difference between descriptive and inferential statistics, the distinction between population and sample, the terms statistic vs. parameter, and the types of qualitative and quantitative variables. It also describes the four levels of measurement scales: nominal, ordinal, interval, and ratio.

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   Article

   KDnuggets·2y

   10 Data Analyst Interview Questions to Land a Job in 2024

   Entry-level data analyst candidates can expect a variety of interview questions focusing on technical expertise, business problem-solving, and soft skills. The technical round includes questions on hypothesis testing, handling outliers, and SQL. The business problem-solving round involves case studies to assess analytical abilities, while the soft skills round evaluates cultural fit and communication skills. Preparing with real-world projects, building a portfolio, and improving technical skills can significantly enhance job prospects.

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   Article

   Daily Dose of Data Science | Avi Chawla | Substack·2y

   3 Types of Missing Values

   Understanding why data is missing is critical before performing imputation. Missing data can be categorized into three types: Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR). Each type requires different imputation techniques. MCAR is the least common and assumes no pattern in missing data, MAR can be explained by other observed features, and MNAR involves missing data with a pattern, usually related to unobserved features.

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