Certifications Guide

A guide for data analysis using Python, covering essential libraries such as IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn, and offering practical guidance through Jupyter notebooks.

A guide to using Python for data analysis, covering topics such as data wrangling with pandas, numerical computing with NumPy, and data visualization with matplotlib.

The article explains how Big Data involves the management and processing of vast datasets, while Data Science focuses on analyzing these datasets to extract meaningful insights and inform decision-making.

This blog post provides a visual mind map outlining core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It serves as a beginner-friendly roadmap that connects key topics, methods, and tools like scikit-learn, TensorFlow, and PyTorch.

Learn practical machine learning with Python and scikit-learn, progressing from basics to advanced topics through modules tailored to your interests.

Learn how to use linear regression to analyze business data, interpret results, and make data-driven decisions, using Excel and real-world examples.

A video from Corey Schafer that provides an introduction to the fundamental data structures in the Pandas library

A video from UTM faculty teaching Python basics, including syntax, data types, control structures, and functions

A video from UTM faculty introducing variables and basic Python programming concepts.

A course that covers tabular data manipulation and analysis using Python's Pandas library, covering data ingestion, cleaning, aggregation, and integration with Scipy and Matplotlib.