This course on Data Science Principles provides an in-depth
exploration of the fundamentals of data science. It is designed to
give students a comprehensive overview of the techniques and
methods used to explore, analyze, and interpret data. In this course,
students will learn how to use the latest tools to identify trends,
patterns, and relationships from large sets of complex data. Through
online lectures, demonstrations, and hands-on activities, learners
will gain insight into the practice of data science.
We will explore the principles underlying modern data science, as
well as practical tools for working with datasets. You will learn how
to use popular software such as Python and R and gain hands-on
experience with real-world datasets.
You will also explore concepts such as variable analysis, feature
engineering, machine learning models, decision trees, clustering
algorithms, supervised/unsupervised learning techniques, natural
language processing (NLP), artificial neural networks (ANNs), deep
learning algorithms such as Convolutional Neural Networks
(CNNs), recurrent neural networks (RNNs) & Generative
Adversarial Networks (GANs).