Ronald Mak Department of Computer Science Department of Applied Data Science Spring Semester 2023 Office hours: MW 4:30 – 5:30 PM Clark Hall CL 325 Office location: Clark Hall CL 325 (mostly working from home) E-mail: ron.mak@sjsu.edu

DATA 200 Computational Programming for Analytics (section 21)

W 6:00 - 8:45 PM room BBC 021

• Syllabus
• Prerequisites
• Recommended Books
• Some Useful Websites

Assignments

#	Assigned	Due	Assignment
1	Feb 1	Feb 8	Initial program
2	Feb 8	Feb 15	If statements and loops Solutions: Assignment2-solutions.ipynb    GuessBot.py Fun graphs: BinarySearchGraphs.ipynb
3	Feb 15	Feb 22	Watering Plans Solution: WateringPlans.ipynb
4	Feb 22	Mar 1	List Practice Solution: Assignment4-solutions.ipynb
5	Mar 1	Mar 8	1. Dictionaries 2. Sets Solution: Assignment5.1-solutions.ipynb    Assignment5.2-solutions.ipynb
6	Mar 8	Mar 15	Matrix Multiplication with numpy Solution: Assignment6-solution.ipynb
7	Mar 15	Mar 22	SJSU Administrators and Professors
8	Mar 22	Apr 5	Simple Data Analysis

Lectures

Week	Date	Content
1	Jan 25	Waiver test
2	Feb 1	Slides: Python's role in data analytics; software installations; ways to run Python code; introduction to Python programming Notebooks: 02_02.ipynb   02_03.ipynb   02_04.ipynb   02_05.ipynb   02_06.ipynb   02_07.ipynb   02_08.ipynb   02_09.ipynb   AgeHistogram.ipynb Python programs: RollDieDynamic.py   FindMin.py
3	Feb 8	Slides: Python control statements: if, while, and for; measures of central tendency Notebooks: 03_05.ipynb   03_06.ipynb   03_07.ipynb   03_08.ipynb   03_09.ipynb   03_10.ipynb   03_11.ipynb   03_13.ipynb   03_14.ipynb   03_15.ipynb   03_16.ipynb   03_17.ipynb
4	Feb 15	Slides: Functions; program design and development; Assignment #3 04_02.ipynb   04_03.ipynb   04_04.ipynb   04_05.ipynb   04_07.ipynb   04_08.ipynb   04_09.ipynb   04_10.ipynb   04_11.ipynb   NumberTranslator.ipynb    NumberTranslator.py
5	Feb 22	Slides: More about functions; lists Notebooks: 04_12.ipynb   04_13.ipynb   04_14.ipynb   04_15.ipynb   05_02.ipynb   05_03.ipynb   05_04.ipynb   05_05.ipynb   05_06.ipynb   05_07.ipynb   05_08.ipynb   05_09.ipynb   05_10.ipynb   05_12.ipynb   05_15.ipynb   05_16.ipynb   Scope.ipynb Practice quiz: Quiz-0222-1401.ipynb    Quiz-0222-1401.solutions.ipynb
6	Mar 1	Slides: Dictionaries and sets 06.02.01.ipynb   06.02.02.ipynb   06.02.03.ipynb   06.02.04.ipynb   06.02.05.ipynb   06.02.07.ipynb   06.02.08.ipynb   06.02.09.ipynb   06.03.00.ipynb   06.03.01.ipynb   06.03.02.ipynb   06.03.03.ipynb   06.03.04.ipynb
7	Mar 8	Slides: numpy Notebooks: 07_02.ipynb   07_03.ipynb   07_04.ipynb   07_05.ipynb   07_06.ipynb   07_07.ipynb   07_08.ipynb   07_09.ipynb   07_10.ipynb   Scores-list.ipynb   Scores_array.ipynb   MatrixMultiplication.ipynb
8	Mar 15	Slides: A deeper look at strings; regular expressions Notebooks: 08_02.01.ipynb   08_02.02.ipynb   08_02.03.ipynb   08_03.ipynb   08_04.ipynb   08_05.ipynb   08_06.ipynb   08_07.ipynb   08_08.ipynb   08_09.ipynb   08_10.ipynb   08_11.ipynb   08_12.01.ipynb   08_12.02.ipynb   08_12.03.ipynb
9	Mar 22	Slides: File I/O; reading and writing CSV files; analysis of Titanic survival data Notebooks: 09_03.01.ipynb   09_03.02.ipynb   09_04.ipynb   09_05.ipynb   09_08.01.ipynb   09_08.02.ipynb   09_09.ipynb   09_10.ipynb   09_11.ipynb   09_12.01.ipynb   Analysis of Titanic survival data: TitanicSurvival.ipynb    TitanicERROR.ipynb Copy file program: copy_file.py CSV data files: UsedCars.csv   TitanicSurvival.csv JSON data file: accounts.json

Goals

This course emphasizes learning the Python programming language and its libraries well enough to develop data analytical applications. Class meetings will encourage high class participation. Mini-labs during each class meeting will give you opportunities to try what you just learned.

Course Learning Outcomes (CLO)

• CLO 1: Understand the role of Python and i ts libraries for data analytics.
• CLO 2: Program in Python proficiently for developing analytical applications.
• CLO 3: Apply appropriate data structures to formulate domain data.
• CLO 4: Apply algorithms to solve analytical problems.
• CLO 5: Choose correct Python libraries and call their functions appropriately.
• CLO 6: Design and develop user-friendly interactive GUI-based analytical applications.

Prerequisites

Classified standing or instructor consent.

Recommended Books

Python for Everyone, Edition 2.0 Cay Horstmann and Rance Necaise Wiley, 2019 ISBN 978-1-943153-67-1 An excellent book for beginning programmers. Dr. Horstmann was a professor in the Computer Science Department at SJSU.

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud Paul Deitel and Harvey Deitel Pearson Education, 2019 ISBN 978-1107186125 Another excellent book for beginning programmers, more focused on data science.

Python for Programmers with Introductory AI Case Studies Paul Deitel and Harvey Deitel Pearson Education, 2019 ISBN 978-0135224335 Essentially the same book as the one above but without the introductory material for beginning programmers. The first part of the class will be based on the first ten chapters of this book.

Hands-On Data Analysis with Pandas, second edition Stephanie Molin Packt Publishing, 2021 ISBN 978-1-80056-345-2 The last part of the class will be based on the first two sections of this book.

A Useful Website

• PyQt5 Tutorial

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