# Overview

In this post, we’ll explore Gradient Descent from the ground up starting conceptually, then using code to build up our intuition brick by brick.

# Overview

This is a quick walk through of using the `sunburstR` package to create sunburst plots in R. The original document is written in `RMarkdown`, which is an interactive version of markdown.

The two main libraries are `tidyverse` (mostly `dplyr` so you can just load that if you want) and `sunburstR`. There are other packages for sunburst plots including: plotly and ggsunburst (of ggplot), but we'll explore sunburstR in this post.

`library(tidyverse)library(sunburstR)`

The data is from week 50 of TidyTuesday, exploring the BBC’s top 100 influential women of 2020. …

# Overview

This is a continuation of my progress through Data Science from Scratch by Joel Grus. We’ll use a classic coin-flipping example in this post because it is simple to illustrate with both concept and code. The goal of this post is to connect the dots between several concepts including the Central Limit Theorem, hypothesis testing, p-Values and confidence intervals, using python to build our intuition.

# Central Limit Theorem

Terms like “null” and “alternative” hypothesis are used quite frequently, so let’s set some context. The “null” is the default position. The “alternative”, alt for short, is something we’re comparing to the default (null).

# Permutation in Python

Using statistics to help users find your product

# Overview

`Itertools` are a core set of fast, memory efficient tools for creating iterators for efficient looping (read the documentation here).

# Itertools Permutations

One (of many) uses for `itertools` is to create a `permutations()` function that will return all possible combinations of items in a list.

# Context

There are several posts that could serve as context (as needed) for the concepts discuss in this post including these posts on:

# Distributions

In this post, we’ll cover probability distributions. This is a broad topic so we’ll sample a few concepts to get a feel for it. Borrowing from the previous post, we’ll chart our medical diagnostic outcomes.

# Overview

This post is a in continuation of my coverage of Data Science from Scratch by Joel Grus.

# Bayes Theorem

Previously, we established an understanding of conditional probability, but building up with marginal and joint probabilities. We explored the conditional probabilities of two outcomes:

## Outcome 1: What is the probability of the event “both children are girls” (B) conditional on the event “the older child is a girl” (G)?

The probability for outcome one is roughly 50% or (1/2).

## Outcome 2: What is the probability of the event “both children are girls” (B) conditional on the event “at least one of the children is a girl” (L)?

The probability for outcome two is roughly 33% or (1/3). …

# Overview

This post is chapter 6 in continuation of my coverage of Data Science from Scratch by Joel Grus. We will work our way towards understanding conditional probability by understanding preceding concepts like marginal and joint probabilities.

# Challenge

The first challenge in this section is distinguishing between two conditional probability statements.

# Overview

This post is chapter 5 in continuation of my coverage of Data Science from Scratch by Joel Grus.

Specifically, we’ll examine how specific features of the Python language as well as functions we built in a previous post on Vectors (see also Matrices) can be used to build tools used to describe data and relationships within data (aka statistics). … 