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.

Instructor

Corey  Fritsch, MS

Corey Fritsch, MS

Corey Fritsch is an experienced data analyst with a passion for continued learning and research. Fritsch is currently pursuing his Doctor of Philosophy in Educational Statistics and Measurement at UW-Milwaukee, as well as graduate certificates in Applied Data Analysis and ... 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

Prerequisites

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

Date: Mar 13-Apr 9

Delivery Method: Online

Platform: Canvas

Instructor: Corey Fritsch MS

Fee:

$845 by Feb 27
$895 after Feb 27

CEUs: 1.4

Enrollment Limit: 12

Program Number: 5020-14939

Note: This is an asynchronous online class with weekly recorded lectures, assignments and instructor feedback using online tools.  Work any time of day as your schedule permits and submit assignments according to due dates. Weekly participation is required. An optional video call may be scheduled at a time mutually agreed upon by students and the instructor.

Registration Deadline: Mar 13

Register Now

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