用Python於數據科學和機器學習

This course covers both theoretical and practical aspects of applied data science, analytics, and visualization in Python. We will start from general python programming basics, data structures, and algorithm design with a heavy emphasis on applying data analysis and visualization techniques to solve real-world problems in different domains. Topics include data representation, manipulation and clearing, visualization, regression, convolutional and recurrent neural networks, reinforcement learning, model development and evaluation with most up-to-date Python modules and popular toolkits.

Python is an easy to use, open-source and versatile programming language that is especially popular among those new to programming. The use of Python has increased by a factor of 10 since 2005 and is projected to be more popular than the industry leading JAVA language in just a few years. It is already the number one software package for those teaching introduction to computer science courses. It is concise, easy to read, and can be used for a variety of industry needs including: web development, data analytics, core software development, and a wide range of scientific and mathematical applications. Python also has numerous libraries for data manipulation and analysis as well as a very active development community that continually updates and creates new packages. It has been adopted by a wide variety of industries and applications including data science, machine learning, data analytics, predictive analytics, business intelligence, and web analytics. In this specialized study program students will learn Python programming concepts including programming styles, idioms, libraries, data structures, data retrieval, processing, visualization, networked application program interfaces and databases.

Homework will be assigned on a weekly basis. Weekly quizzes: even when is a written quiz, odd weeks is a Python quiz. Midterm and final project will be a video describing a project

PRIMARY REFERENCE • Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud by Paul J. Deitel , and Harvey Deitel OTHERS: • Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming by Eric Matthes ISBN-10: 1593279280 ISBN-13: 978-1593279288 ISBN-13: 978-0135404676 ISBN-10: 0135404673 • Practice of Computing Using Python, The, Student Value Edition,3rd Edition, by William F. Punch, and Richard Enbody ISBN-13: 978-0134380315 ISBN-10: 0134380312 • Python for Everyone, 2nd Edition by Cay S. Horstmann, Rance D. Necaise ISBN-13: 978-1119056553 ISBN-10: 1119056551 • Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Editionby Wes McKinney ISBN-13: 978-1491957660 ISBN-10: 1491957662 • Python for Software Design: How to Think Like a Computer Scientist 1st Edition by Allen B. Downey (Author). Available at http://www.greenteapress.com/thinkpython/thinkpython.html ISBN-13: 978-0521725965 ISBN-10: 0521725968 • Automate the Boring Stuff with Python: Practical programming for total beginners by Al Sweigart. Available at https://automatetheboringstuff.com/ ISBN-10: 1593275994 ISBN-13: 978-1593275990

地點

ED 716a

時間

Mon. 9am-11am

聯絡方式

0905672515