Foundations of Data Science

25 parts

Datalabcc: Foundations of Data Science. Data science relies on several important aspects of mathematics. In this course, you'll learn what forms of mathematics are most useful for data science, and see some worked examples of how math can solve important data science problems.

Mathematics for data science practitioners

Introduction to the Mathematics chapter of Datalabcc's "Foundations in Data Science" series. Speaker: Barton Poulson.

Elementary Algebra

Primer on elementary algebra. Speaker: Barton Poulson.

Linear Algebra

Primer on linear algebra. Speaker: Barton Poulson.

Systems of linear equations

Primer on systems of linear equations. Speaker: Barton Poulson.

Calculus

Primer on calculus. Speaker: Barton Poulson.

Calculus and optimization

How calculus relates to optimization. Speaker: Barton Poulson.

Mathematics: Big O

Big O notation. Speaker: Barton Poulson.

Probability

Basics of probability. Speaker: Barton Poulson.

Mathematics: Bayes' theorem

The probability of a hypothesis, given data. Speaker: Barton Poulson.

Mathematics: Next steps

Why math is useful in data science. Speaker: Barton Poulson.

Statistics (intro)

Why statistics are useful for data science. Speaker: Barton Poulson.

Statistics: Exploration overview

Statistics is exploring data. Speaker: Barton Poulson.

Statistics: Exploratory graphics

Graphical data exploration. Speaker: Barton Poulson.

Statistics: Exploratory statistics

Numerical data exploration. Speaker: Barton Poulson.

Statistics: Descriptive statistics

Simple description of statistical data. Speaker: Barton Poulson.

Statistics: Inferential statistics

Inferring results from incomplete data. Speaker: Barton Poulson.

Statistics: Hypothesis testing

Basics of hypothesis testing. Speaker: Barton Poulson.

Statistics: Estimation

Finding parameter values, confidence intervals. Speaker: Barton Poulson.

Statistics: Estimators

Methods for estimating parameters. Speaker: Barton Poulson.

Statistics: Measures of fit

Measuring the correspondece between data and model. Speaker: Barton Poulson.

Statistics: Feature selection

How to choose useful variables. Speaker: Barton Poulson.

Statistics: Problems in modelling

Common problems in statistical modelling. Speaker: Barton Poulson.

Statistics: Model validation

Does your model fit reality? Speaker: Barton Poulson.

Statistics: Do it yourself!

You don't have to be a wizard to do statistics! Speaker: Barton Poulson.

Statistics: Next steps

Overview of possible follow up courses and subjects from the same publisher. Speaker: Barton Poulson.