Introduction To Data Science

Explore what data science is, how data drives business value, and the roles and distinctions among data scientists, analysts, and BI professionals.

Guiding you through a data science mindset, the lecture maps daily routines to three data types—speech, text, and images/videos—and explains their three-category data classification.

Explore three data types—speech, text/numeric data, and images—and how AI, natural language processing, and computer vision analyze them for sentiment, translation, health, and autonomous driving.

Data science is an interdisciplinary field that uses methods and algorithms to extract knowledge from data, whether structured or unstructured, with 80 percent unstructured data.

Clarify the data science lifecycle from raw data to information, knowledge, and wisdom, contrast data mining with data science, and summarize machine learning and its supervised, unsupervised, and reinforcement categories.

Explore supervised learning, including regression and classification, and how models learn from data to predict house prices or classify cancerous tissue and images.

Explore unsupervised learning, where no target variable exists. Understand clustering for recommendations and market segmentation, dimensionality reduction, and reinforcement learning with rewards and penalties.

Explore market basket analysis and item associations that drive cross-sell ideas, such as diapers and beer, and Netflix recommendations, framed by deep learning and rising computing power.

Compare deep learning with machine learning through neural networks, features, and backpropagation, then see how convolutional neural networks and chest x-ray data enable covid detection and predictions.