This hands-on course empowers learners to apply, analyze, and evaluate unsupervised learning techniques—specifically clustering—using Microsoft Excel. Designed for learners with basic Excel knowledge, the course walks through the entire data clustering pipeline: from preparing and structuring datasets to building and refining logic-based cluster assignments. Learners begin by … [Read more...] about Excel: Apply & Evaluate Unsupervised Clustering
Data Analysis
Seaborn Python: Visualize & Analyze Data Distributions
This intermediate-level course is designed to help learners analyze, visualize, and interpret data distributions using the powerful Seaborn library in Python. Building upon foundational knowledge of data visualization, the course takes a hands-on approach to explore univariate and bivariate distributions, apply linear and polynomial regression models, and demonstrate advanced … [Read more...] about Seaborn Python: Visualize & Analyze Data Distributions
Data Science Fundamentals Part 2: Unit 2
Thsi course explores foundational and advanced techniques for making reliable inferences from data, starting with a the history and evolution of statistical analysis. Through hands-on lessons, you’ll learn how to leverage computational and sampling-based methods to draw meaningful conclusions, and gain practical experience with hypothesis testing—a cornerstone skill for … [Read more...] about Data Science Fundamentals Part 2: Unit 2
Data Science Fundamentals Part 1: Unit 3
This course explores the fundamentals of relational databases and how to seamlessly map Python data structures to robust database tables using object-relational mappers (ORMs). You'll gain practical experience in building efficient ETL (Extract, Transform, Load) pipelines, ensuring your data is not only accessible but also reliable and persistent. You'll learn about data … [Read more...] about Data Science Fundamentals Part 1: Unit 3
SPSS: Apply & Interpret Logistic Regression Models
This course provides a practical and applied introduction to logistic regression and supervised learning using IBM SPSS Statistics. Designed for learners seeking to build analytical skills in predictive modeling, the course emphasizes both conceptual understanding and tool-based execution. Through step-by-step instruction, learners will identify key components of logistic … [Read more...] about SPSS: Apply & Interpret Logistic Regression Models
