Metis is one of the approved prerequisite providers. Candidates may access the Metis course through their website at https://www.thisismetis.com Candidates are responsible for the cost of course offered at Metis.
1 course (36 hours) with 12 required sessions
New course starts each month featuring dedicated expert instructors.
Sessions are recorded for review or if you miss a session.
Base cost (see Metis website for pricing)
Metis offers a live online course with dedicated expert instructors who are ready to answer your questions. The approved prerequisite course offered by Metis lasts 6 weeks.
The single course offered by Metis is titled Beginner Python and Math for Data Science, and it consists of the following 6 topics covered during live sessions. Check the Metis website for upcoming classes.
Candidates are introduced to programming in Python. Candidates will learn about Jupyter Notebooks – a popular platform for running Python programs. The course will cover the basics of programming including data structures, data operations, if else statements, for and while loops, and logical operations.
This segment of the course covers advanced functionality in Python, including functions, debugging, error handling, string manipulations, and writing efficient code.
Python Mathematical Libraries
Candidates will learn about using libraries that are useful for data manipulation and visualization. Candidates will learn to use NumPy, Pandas, and Matplotlib. These libraries will allow candidates to load and save data, manipulate data such as aggregating, filtering, detecting outliers, and visualizing.
This segment of the course is a refresher in linear algebra. It will cover the fundamentals of linear algebra, including vectors, and vector manipulations, matrices and matrix manipulations, linear equations and solutions, eigenvalues and eigenvectors.
Calculus and Probability
This module is a refresher in the fundamentals of calculus. It reintroduces students to such central concepts of calculus such as derivatives, integrals, determining local maximum and minimum, and limits. In addition, the module provides a refresher on central concepts of probability such as random variables, mean, variance, probability mass and density functions, and cumulative distribution functions.
This final refresher module covers a few important statistical concepts such as ANOVA, hypothesis testing and p-value, and confidence intervals.