**Daily update | 19 December, 2021**

# Medium

#### A better way to browse the web for data practitioners

**Author:** Laetitia Carle**Claps:** 156**Source:** artificial-intelligence**Description:** What if you didn’t have to open 30 tabs every time you were trying to find content online?

#### How to Speed Up XGBoost Model Training

**Author:** Michael Galarnyk**Claps:** 107**Source:** machine-learning**Description:** Gradient boosting algorithms are widely used in supervised learning. While they are powerful, they can take a long time to train. Extreme…

#### Why You Should Start Using Pathlib As An Alternative To the OS Module

**Author:** Ahmed Besbes**Claps:** 78**Source:** data-science**Description:** First reason: object-oriented programming

#### Become a Data Scientist in 2022: A Practical 52-Week Course

**Author:** Frank Andrade**Claps:** 66**Source:** data-science**Description:** A complete data science course that covers statistics, SQL, Python, math, and more

#### How Data Scientists Deal with the Unknown

**Author:** TDS Editors**Claps:** 61**Source:** machine-learning**Description:** Our weekly selection of must-read Editors’ Picks and original features

# StackExchange

#### Why are Ratios "Dangerous" in Statistical Modelling?

**Source:** stats**Views:** 147**Score:** 9**Tags:** regression probability distributions normal-distribution model

#### Why Are Empirical Bayes Methods Not Considered "Controversial"?

**Source:** stats**Views:** 45**Score:** 4**Tags:** bayesian prior empirical-bayes

#### Performance Metrics for Classification Models with an Ordinal Response Variable

**Source:** stats**Views:** 18**Score:** 2**Tags:** classification categorical-data predictive-models ordinal-data accuracy

#### How are copulas used in the real world?

**Source:** stats**Views:** 32**Score:** 2**Tags:** probability distributions conditional-probability markov-chain-montecarlo copula

#### In Bayesian models, can you use Uniform(-inf, inf) as a prior?

**Source:** stats**Views:** 23**Score:** 2**Tags:** bayesian prior uniform-distribution metropolis-hastings