$118.49
Author: Deitel Paul
Brand: Pearson
Edition: 1
Number Of Pages: 880
Details: Review
“Strikes a good balance between teaching computer science fundamentals and putting data science techniques into practice. Designed to help students not only learn programming fundamentals but also leverage the large number of existing libraries to start accomplishing tasks with minimal code. Concepts are accompanied by rich Python examples that students can adapt to implement their own solutions to data science problems. I like that cloud services are used.” —David Koop, Assistant Professor, U-Mass Dartmouth
“Fun, engaging real-world examples and exercises will encourage students to conduct meaningful data analyses. This book provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what “the best” means. Great overview of all the big data technologies with relevant examples.” —Jamie Whitacre, Data Science Consultant
“Great introduction to Python! This book has my strongest recommendation both as an introduction to Python as well as Data Science. A great introduction to IBM Watson and the services it provides!” —Shyamal Mitra, Senior Lecturer, University of Texas
“The best designed Intro to Data Science/Python book I have seen.” —Roland DePratti, Central Connecticut State University
“You’ll develop applications using industry standard libraries and cloud computing services.” —Daniel Chen, Data Scientist, Lander Analytics
“The book’s applied approach should engage students. The examples involving the top-down, stepwise refinement of programs illustrate how programs are really developed. A fantastic job providing background on various machine learning concepts without burdening the users with too many mathematical details.” —Garrett Dancik, Associate Professor of Computer Science/Bioinformatics, Eastern Connecticut State University
“Wonderful for first-time Python learners from all educational backgrounds and majors. My business analytics students had little to no coding experience when they began the course. In addition to loving the material, it was easy for them to follow along with the example exercises and by the end of the course were able to mine and analyze Twitter data using techniques learned from the book. The chapters are clearly written with detailed explanations of the example code, which makes it easy for students without a computer science background to understand. The modular structure, wide range of contemporary data science topics, and companion Jupyter notebooks make this a fantastic resource for instructors and students of a variety of Data Science, Business Analytics, and Computer Science courses. The “Self Checks” are great for students. Fabulous Big Data chapter–it covers all of the relevant programs and platforms. Great Watson chapter! This is the type of material that I look for as someone who teaches Business Analytics. The chapter provided a great overview of the Watson applications. Also, your translation examples are great for students because they provide an “instant reward”–it’s very satisfying for students to implement a task and receive results so quickly. Machine Learning is a huge topic and this chapter serves as a great introduction. I loved the housing data example–very relevant for business analytics students. The chapter was visually stunning.” –Alison Sanchez, Assistant Professor in Economics, University of San Diego
“I like the new combination of topics from computer science, data science, and stats. A compelling feature is the integration of content that is typically considered in separate courses. This is important for building data science programs that are more than just cobbling together math and computer science courses. A book like this may help facilitate expanding our offerings and using Python as a bridge for computer and data science topics. For a data science program that focuses on a single language (mostly), I think Py
Release Date: 25-04-2019
Package Dimensions: 52x233x1280
