# data[].attributes.body
This module empowers professionals from non-STEM backgrounds to confidently engage with data science in their work. Designed specifically for those who are new to quantitative methods, the module takes an intuitive, accessible approach to the technical language commonly used for data science and analytics.

Rather than diving into creating complex mathematics, the module focuses on interpretation—teaching participants how to confidently read, run, and extract insights from existing dashboards and reports, written in R and Python.

# data[].attributes.body
Write some documentation for one of the software

# data[].attributes.body
Probability distributions - discrete

# data[].attributes.body
Probability distributions - continuous

# data[].attributes.body
Method of moments (integration)

# data[].attributes.body
Maximum likelihood Estimators (differentiation)

# data[].attributes.body
Discrete time markov chains (Matrix algebra)

# data[].attributes.body
RECAP

# data[].attributes.body
Feedback on progress/expectations for final task

# data[].attributes.body
Clinical trials

# data[].attributes.body
Flood risk

# data[].attributes.body
Student retention statistics

# data[].attributes.body
Hospital waiting rooms

# data[].attributes.body
Traffic!! duration, count of people in queues etc

# data[].attributes.body
Medical topic revisited

# data[].attributes.body
Open and navigate R

Create a vector

Discrete probabilities

Generate random variables

# data[].attributes.body
Open and navigate Jupyter Notebook

Load data

Creating a sample

Density plot

# data[].attributes.body
Use a function (mean/mode/variance)

Very basic loops

Use a RMD file

Run chucks

Debug - well identify bugs

# data[].attributes.body
Good practice - comments, names, layout

More creating and manipulating random variables.

Briefly touch on regression models

# data[].attributes.body
Manipulating matrices

convergence (practical implications)

More loops

resampling

# data[].attributes.body
More loops

Classification - logistic regression

Basic messy data - NA handling