Course evaluation: SNHU MAT-243 Applied Statistics for STEM


In this post, I will evaluate SNHU’s MAT-243 to highlight its topics and my likes and dislikes about the course so that you can start the term with the right expectations.

MAT-243 learning resources

All learning resources for MAT-243 take place in ZyBooks and include statistical topics and a basic introduction to some statistical methods in Python.

Zybooks covers:

  1. Data Visualization
  2. Descriptive Statistics
  3. Probability and Counting
  4. Probability Distributions
  5. Inferential Statistics
  6. Linear Regression
  7. Multiple Regression
  8. One-way ANOVA and Chi-Square Tests

Although the topics in ZyBooks span 8-sections and the course spans 8-weeks, the topics are not evenly divided throughout the course.

What is covered in MAT-243

MAT-243 begins with a brief introduction to graphs, their usage, and how to generate them using Python in an interactive web application, Jypter Notebooks.

The weekly discussions and projects require Python for loading and manipulating large datasets to answer the assignment prompts, which is why ZyBooks briefly covers Python statistical methods.

MAT-243 is not oriented around programming, and I never had to code anything besides being guided to change some function parameters. However, the weekly quiz consisting of only 5-questions will test your understanding of statistical operations in Python.

The first week covers data visualizations, descriptive statistics, and probability and counting. The topics for the first week are easy to grasp but require a good amount of time to complete on ZyBooks.

Module Two is when I began learning about different data distributions, including standard distribution, a student’s t-distribution, and an f-distribution. I used my understanding of data distributions in later modules to perform hypotheses testing, compare variables using statistical methods and graphically, and create simple and multiple regression models to predict a response variable using one or more predictor variables.

Mat-243 topic: Simple linear regression: Predicting the total number of wins using average points scored

MAT-243 does not require memorizing and solving math formulas on paper. Instead, all the computations for each assignment are done through Python.

MAT-243 weekly assignments 1 of 2

MAT-243 weekly assignments 2 of 2

What I liked about MAT-243

My biggest appreciation for MAT-243 is its ability to stay focused on statistics without cluttering the content with math or programming.

I liked how the learning material in ZyBooks was organized, and I did not have a hard time understanding the explanations and exercises, even though this was my first statistics course.

I enjoyed learning about regression models and thought the idea of statistically predicting a value and testing its accuracy was interesting.

I also appreciated the soft introduction to graphical representations and various statistical Python libraries.

My overall experience in MAT-243 was great, and the instructor was also a decent person and didn’t try to complicate learning.

Statistics, in general, left a good impression on me, and I believe it will serve me well as a computer science major, especially in data science, if I ever decide to go that route.

What I disliked about MAT-243

The weekly workload was not equally balanced throughout the weeks, and the weeks with a project due had more homework assignments.

Weeks three, five, and seven were the heaviest weeks consisting of a good amount of ZyBooks study material, a discussion post interpreting statistical concepts using Jypter Notebooks, a quiz, and a project.

The remainder of the weeks were somewhat light, and some topics required further studying outside of ZyBooks to fully grasp.

Final thoughts

I was not expecting to complete MAT-243 with a nearly perfect grade, but the extra research and the time I took to understand the content paid off.

MAT-243 final grade