May 1, 2024
Updated May 10, 2025
19 minute read
Data, in its most fundamental sense, is a collection of facts, figures, observations, or descriptions. It can be numbers on a spreadsheet, text in a document, images, videos, or even the clicks you make while browsing the internet. Over time, the way we perceive and utilize data has evolved dramatically. Initially, data was primarily a record-keeping tool. Today, it is the lifeblood of modern society, powering industries, shaping economies, and influencing our daily decisions. Understanding data is no longer a niche skill but an increasingly vital component of literacy in the 21st century.
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Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data.
Comprehensive guide to deep learning, and covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It also provides hands-on experience with popular deep learning frameworks such as TensorFlow and PyTorch.
Practical guide to machine learning, and covers topics such as data preprocessing, model selection, and evaluation. It also provides hands-on experience with popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow.
Introduces the concepts and tools of data science from a business perspective, and covers topics such as data mining, machine learning, visualization, and communication.
Classic introduction to reinforcement learning, and covers topics such as Markov decision processes, value functions, and policy iteration. It also provides hands-on experience with popular reinforcement learning algorithms such as Q-learning and SARSA.
Comprehensive guide to data mining, and covers topics such as data preprocessing, clustering, classification, and association rule mining. It also provides hands-on experience with popular data mining tools such as WEKA and RapidMiner.
Classic introduction to statistical learning, and covers topics such as linear regression, logistic regression, and tree-based methods. It also provides hands-on experience with popular statistical learning software such as R and Python.
Collection of essays on machine learning, and covers topics such as the history of machine learning, the challenges of machine learning, and the future of machine learning. It also provides insights from one of the world's leading experts in machine learning.
Classic introduction to computer networking, and covers topics such as the basics of computer networks, the design of computer networks, and the performance of computer networks. It also provides hands-on experience with popular computer networking software such as Wireshark and tcpdump.
Provides a hands-on introduction to data science, and teaches the reader how to collect, clean, analyze, and visualize data. It also covers some machine learning techniques.
Is an introduction to operating systems, and covers topics such as processes, threads, memory management, and file systems. It also provides hands-on experience with popular operating systems such as Linux and FreeBSD.
Classic introduction to computer architecture, and covers topics such as the basics of computer hardware, the design of computer systems, and the performance of computer systems. It also provides hands-on experience with popular computer architecture simulators such as SimpleScalar and gem5.
Is an introduction to probability theory, and covers topics such as random variables, probability distributions, and Bayesian inference. It also provides hands-on experience with popular probability software such as R and Python.
Classic introduction to linear algebra, and covers topics such as matrices, vectors, and eigenvalues. It also provides hands-on experience with popular linear algebra software such as MATLAB and Python.
Classic introduction to calculus, and covers topics such as limits, derivatives, and integrals. It also provides hands-on experience with popular calculus software such as MATLAB and Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/9x4tts/dat
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