Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Covers the fundamentals of data science, the scientific method evolution, the role of a data scientist, and the significance of data as the new oil.
Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
