Statistics – Fundamentals for Data Analysts

Statistics is a foundational part of working with different types of data and distributions. Becoming familiar with statistics prepares you on how to derive meaning from raw data. This introductory statistics course helps you learn how statistics and probabilities are utilized in data analytics and data science, and focuses on statistical methods that anybody can perform with no prior statistics background.

The first section covers statistical concepts that can be used to describe your data, while the second section features concepts that can be used to gain insights from your data. Exercises and real-world examples in this interactive course provide practice in using statistics to describe and draw inferences from data. All methods will be shown using Python for easy reproducibility, allowing you to be able to interpret and communicate your data more effectively.

This course can be applied to the Data Analysis – Advanced Certificate.


Corey  Fritsch, PhD

Corey Fritsch, PhD

Corey Fritsch is an experienced data analyst with a passion for continued learning and research. Fritsch earned his Doctor of Philosophy in educational statistics and measurement at UW-Milwaukee, as well as graduate certificates in applied data analysis and business analytics. ... read more

Who Should Attend

Data analysts seeking to develop skills in the use of statistics. Prior completion of the Data Analysis Certificate is not required. Participants may choose to take this course on its own or take all courses listed to earn the Data Analysis – Advanced Certificate.

Benefits and Learning Outcomes

  • Summarize descriptive and inferential statistics definitions and concepts.
  • Calculate descriptive and inferential statistics on a data set.
  • Tell a story about a data set by using statistical methods.

Course Outline/Topics

  • Descriptive statistics
    • Samples and populations
    • Simple statistics (mean, median, mode, standard deviation, variance)
    • Types of statistics for each type of data (e.g., Likert scales)
    • Correlation vs causation
    • Probability theory
    • Probability distributions (normal, binomial, skewness, Central Limit Theorem)
    • Visualization and storytelling with descriptive statistics
  • Inferential statistics
    • Bootstrap and random sampling of distributions
    • Sampling for hypothesis testing and confidence intervals
    • T-Tests for showing differences in distributions
    • Decision analysis
    • Bayesian inference
    • Univariate regression
    • Multivariate regression
    • ANOVA
    • Principal Component Analysis (PCA)
    • Non-parametric tests (Friedman, Rank testing)
    • Visualization and storytelling with inferential statistics


Participants should have experience with basic data handling. Prior experience with Python is helpful, but not required.

Date: 2 Mon, May 12-19, 2025

Delivery Method: In-person

Time: 8:30am-4:30pm CT

Location: UWM School of Continuing Education

Instructor: Corey Fritsch PhD


$845 by Apr 28, 2025
$895 after Apr 28, 2025

CEUs: 1.4

Enrollment Limit: 20

Program Number: 5020-16194

Registration Deadline: May 11, 2025

Register Now


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