Study Guide

Objective

You learn fundamental concepts of probability and statistics, which are crucial in the field of machine learning and data engineering.

You are familiar with combinatorics and basic theorems and rules of probability and you can apply them. You can handle random variables, and use discrete and continuous variables and distributions to present data and solve problems related to your professional field.

With descriptive statistics, you can characterize data in the form of diagrams and calculate statistical parameters and quantiles. With inferential statistics you come to conclusions and can make predictions based on your data.

You become familiar with applications used to handle large amounts of data.

Content

Combinatorics
Theorems and rules of probability
Discrete and continuous variables and distributions
Descriptive statistics, common diagrams and statistical parameters, quantiles
Inferential statistics and common statistical significance tests such as t-test, ANOVA and chi-square
Correlation and linear regression
Use of applications, such as Excel and Python

Assessment criteria, satisfactory (1)

You can compute the number of permutations and variations, solve easy probability tasks, calculate simple statistical parameters and present simple diagrams and find linear regression.

Assessment criteria, good (3)

You can solve a wide range of problems related to applications of probability and statistics. You can utilize many theorems and rules of probability. You can compute combinations and various descriptive parameters as well as quantiles. You can create and utilize binomial and continuous distributions. You are able to interpret correlation and perform linear regression and statistical significance tests.

Assessment criteria, excellent (5)

You can apply the theorems and rules of combinatorics and probability to new problems. You are able to apply different discrete and continuous parameters and distributions and create tests and predictions on your data.