Linear Algebra with Python: Essential Math for Data Science
Take control of your data by honing your fundamental math skills
Linear algebra is a field of mathematics dealing with vector spaces and linear functions. The understanding of linear algebra is crucial for data analysis techniques and machine learning. Even stateoftheart deep learning algorithms rely on the concepts of linear algebra. While the field of linear algebra is extensive, it is important to focus on the areas that are directly applicable for data science.
This is the first course in a fourpart series focused on essential math topics. These courses are grouped in pairs with this natural progression:
and
What you'll learnand how you can apply it
By the end of this live, handson, online course, you’ll understand:
 Matrices and vectors and how to perform mathematical operations using matrices and vectors
 How linear equations are constructed
And you’ll be able to:
 Represent data as a matrix or vector
 Construct a system of linear equations
 Using Python’s NumPy package to perform linear algebra operations
This training course is for you because...
 You are someone in a technical role but are looking for foundational knowledge to transition into a data scientist position
 You work with data and want to start building predictive models
 You want to become a data analyst or data scientist
Prerequisites
 Basic math: addition, subtraction, multiplication and division
 Algebra
 Basic Python: variable creation, conditional statements, functions, loops
Recommended preparation:
 None
Recommended followup:
 Take Linear Regression with Python (live online training course)
 Take Probability with Python (live online training course)
 Take Statistics and Hypothesis Testing with Python (live online training course)
About your instructor

Michael studied mathematics and music as an undergraduate at the University of Arizona before obtaining a master’s degree studying computational statistics at Arizona State University. He has developed usable software tools alongside cutting edge statistical theory, putting new ideas in the hands of researchers and practitioners. This research experience, along with his time spent in academic and professional teaching positions, sparked a love for sharing knowledge and helping others grow.
As an undergraduate research assistant in an artificial intelligence and music lab, Michael wrote Python code to model musical data for a jazzimprovisation robot. Later, he developed an R package and a novel procedure to allow biologists to automatically compare and select among many statistical models with confidence. Michael has also taught prospective graduate students to take the GRE for Kaplan Test Prep, and served as a TA for upper level mathematics courses.
In his free time, Michael turns this passion for math and science toward art, creating codebased visual art and organizing events for digital art and music.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Getting Started (5 minutes)
 Presentation: Introduction to Jupyter Notebook environment
Introduction to Linear Algebra (5 minutes)
 Presentation: What is linear algebra? What aspects of it are covered in this class?
 Poll: Which of the following equations is a linear equation?
Vectors and Matrices (10 minutes)
 Presentation: What are vectors and matrices?
 Exercise: Fillintheblank — Matrix dimensions
Operations with Vectors and Matrices (10 minutes)
 Presentation: How do we work with matrices (addition and multiplication)?
 Poll: Which of the matrices from the last exercise can be multiplied?
 Exercise: Add two matrices
Scalar Product and Orthogonality (10 minutes)
 Presentation: How do you multiply two vectors?
 Exercise: Find the dot product of two matrices
Linear Independence and Transformation (10 minutes)
 Presentation: What is independence?
 Poll: Are these vectors dependent or independent?
 Presentation: Linear transformation property
Eigenvectors and Eigenvalues (5 minutes)  Presentation: What makes Eigenvectors different from other vectors?
Q&A and Discussion (10 minutes)
 Break (5 minutes)
Introduction to NumPy (10 minutes)
 Presentation: What is a NumPy array (i.e. the ndarray class)?
 Exercise: Create an array on Jupyter Hub
Operation with NumPy Arrays (15 minutes)
 Presentation: How do you use Universal functions (e.g. addition) in NumPy?
 Exercise: Add two arrays
 Presentation: How do you do matrix multiplication?
 Exercise: Multiply two arrays
 Presentation: How do you transpose a matrix?
 Exercise: Transpose an array
Performance Improvements When Using NumPy Arrays (5 minutes)
 Presentation: Why is NumPy faster
Array Indexing (10 minutes)
 Presentation: How can I get a subset of an array?
 Exercise: Slicing an array
Q&A and Discussion (10 minutes)