CS 110 syllabus COMPUTER SCIENCE I (Genomics) CS 110G (3 credits) Genomics/big data emphasis -- Python 3 SPRING 2018 SYLLABUS MOODLE LINK Last updated 04-May-2018 Objectives Grading Course policies Python Library Reading/Homework Instructor Loren Rhodes E-mail: rhodes@juniata.edu Office: C208 Brumbaugh Academic Center Office phone and voice mail: 814-641-3620 Cell phone: 814-644-3309 Office Hours are kept current on my home page. See the home page or my office door for recent changes; others office hours may be arranged by appointment. Class meeting times: M W F 8:00-8:55 C102 BAC Required texts: Jessen Havill, Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming , CRC Press, 2017, ISBN 978-1-4822-5414-3 An understanding of genomics and/or biology IS NOT a pre-requisite! Many of the algorithms are based on biological processess but having a background in them are not required. Successful students must have access to a text for reference and reading. Do not expect to just look up Python on the internet as there are abstract computer science concepts not so clearly explained. Not having a book is an unaccepatable excuse for non-preparation. We will focus primarily on the first 8 chapters of this 13 chapter book. Most of the material in the latter 5 chapters are Computer Science II topics. Software Implementation Environment: We will use the environment as suggested by the text: Python 3.4 or 3.6 and IDLE. An integrated distribution can be found at https://store.continuum.io/cshop/anaconda/ Grading: Be aware these sample exams prior to 2016 are for the non-genomics, Java-based CS 110 course. Use them for topic preparation and exam style as the course is substantially different from those earlier years. 15% Exam 1, Monday, February 12, 2018 (Sp18 exam1 and exam1key) (Sp16 exam1 and exam1key) (Sp15 exam1 and exam1key ) (Sp12 exam1 and exam1key ) (Sp11 exam1 and exam1key ) (Sp09 exam1 and exam1key) 15% Exam 2, Friday, March 9 Study guide (Sp18 exam2 and exam2key) (Sp16 exam2 and exam2key ) (Sp15 exam2 and exam2key ) (Sp12 exam2 and exam2key) (Sp11 exam2 and exam2key ) (Sp09 exam2 and exam2key) 15% Exam 3, Friday, April 13 Study guide (Sp18 exam3 and exam3key ) (Sp16 exam3 and exam3key ) (Sp15 exam3 and exam3key ) (Sp12 exam3 and exam3key) (Sp 11 exam3 and exam3key) (Sp09 exam3 and exam3key) 20% Exam 4, Friday, May 11, 1:00 p.m. (change from official time) during finals period (study guide) 35% In-class exercises, homework assignments and projects Objectives: The student is expected to develop an understanding of some of the fundamentals concepts of computer science including algorithm development, abstraction, limits of computing, program reliability, program correctness, security and ethical issues; to develop or strengthen problem solving skills; to develop or strengthen software development skills using object-oriented programming (design, development, implementation, testing and presentation of software) with the Python language. to develop more focused skills in the processing of large genomics data sets to develop or strengthen understanding of the Unix operating system environment for development and big data manipulation Projects will have an emphasis on the manipulation of genomics data sets. Assignments and programs: Assignments should be prepared for the following class period. Some may be collected for grading, others will just be gone over in class. Programming projects, which are based on those in the text, will have their due dates announced in class. There are resources from the textbook web site, such as code samples from the book. Project references will typically link to the correct file sources and listing within the file. These listings are copyrighted and only legally used by students in the course. Course policies These standard course policies are described on this web page. Please read them carefully, especially on academic integrity. Daily Course Format: Classroom activities will include lecture, lecture with questions directed towards named students, group exercises, demonstrations and laboratory exercises. Many of the classes will be partial lab experiences. Students will typically work on projects in pairs. All activities require the student coming to class prepared for satisfactory performance. A tentative schedule of class topics, class activities and assignments is available. COMPUTER SCIENCE I (Genomics emphasis) SPRING 2018 TENTATIVE LECTURE/LAB OUTLINE This portion of the syllabus will be modified and updated regularly. Many of the notes refer to an earlier Java version of the course. Links to the lecture notes, program assignments and other lab/class materials will be added or revised as they are developed prior to the lecture. Program links may also be referring to projects from the previous semester and may not be updated until closer the time. The student is expected to read the materials prior to the lecture. Class activities listed for a day are when they are assigned. The instructor will clarify the due dates of projects and homeworks of those to be turned in and will be set in Moodle. Most exercises are given for your understanding and test preparation and will not be turned for grading. Some exercises used the following site as a tool to generate data sets and project ideas: http://www.bioinformatics.org/sms2 The Sequence Manipulation Suite. Lecture Topics Reading (prior to class meeting) Class Activities, Homework and Programs (assigned) 1-1/22 Introduction, Algorithms and Software Sections 1.1-1.2 Installing Python environment. Ex 2-1/24 More on algorithms Programming Basics Sections 1.2-1.3 Ex 1.2.2, 1.2.3, 1.2.6, 1.3.1, 1.3.2 Program #1 3-1/26 Hardware Data encoding Sections 1.4-1.5 Ex 1.4.1-11 4-1/29 Arithmetic, Variables and Expressions Section 2.1-2.2 Ex 2.2.2-8 Program #2 5-1/31 Variables Section 2.3 Ex 2.3.1-8 6-2/2 Functions, input/output Strings Section 2.4 Ex 2.4.1-6 Program #3 7-2/5 Binary arithmetic Section 2.5-2.6 Ex 2.5.1-5, .7, .14-15 8-2/7 Data abstraction, Turtle drawing, Section 3.1-3.2 Ex 3.2.1-4 Program #4 9-2/9 Functional Abstraction Section 3.3 Ex 3.3.1-7 10-2/12 Exam 1 (chapters 1,2,3.1-3.4) 11-2/14 Programming Style Documentation and Style Section 3.4 Ex 3.4.1-3 12-2/16 Functions II Section 3.5 Ex 3.5.1-4. Program #5 13-2/19 Scope and Namespaces Section 3.6-3.7 Ex 3.6.1-3 14 -2/21 Discrete Models (iteration) Section 4.1 Ex 4.1.1-38 15 -2/23 More loop programming Section 4.1 Program #6 16-2/26 Lists and Plotting Section 4.2 Ex 4.2.1-5 17-2/28 population changes Conditional iteration: the while loop Section 4.3 While loop lab 18-3/2 if-[then]-else control (selection) Section 5.1 19-3/5 Program #7 20-3/7 review and catch up if lab 21-3/9 Exam 2 (chapters 3-4, 5.1) Spring break 22-3/12 Random Walks Random Number Generators Sections 5.1-5.2 Ex. 5.1.1-4,7-13 Program #8 23-3/14 Boolean expressions Section 5.4 Ex. 5.4.1-21 24-3/16 A guessing game Sections 5.5-5.6 25-3/26 String operations review Streams and file I/O Text-file I/O on documents Section 6.1 Section 6.2 Program #9 Ex. 6.1.1-11, 6.2.1,3,6 26-3/28 Encoding strings Section 6.3 Ex 6.3.1-20 27-3/30 Intro to Object Oriented Programming Section 13.1 Ex 13.1-8, .18 28-4/2 Defining a class, its constructors, instance variables and methods Program #10 29-4/4 More on class design 30-4/6 31-4/9 Analyzing text, genomics Section 6.5-6.7 32-4/11 Summarizing data and lists Section 8.1 Ex 8.1.1-18 33-4/13 Exam 3 (Chapter 5-6.3, 13.1) 34-4/16 Creating and modifying lists Section 8.2 35-4/18 Sorting and searching Section 8.2, 11.2-11.3 36-4/20 37-4/23 Frequencies, Dictionaries Section 8.3 Program #11 38-4/25 Big data considerations Efficient algorithms Section 8.5 39-4/27 Two dimensional data Section 9.1-9.2 40-4/30 Program #12 41-5/2 Digital images Section 9.3 42-5/4 43-5/7 Regular expressions Regular Expression summary Regular Expressions Tutorial/ Regular expression lab Review and catch up Python versus Java 5/11/18 1-4 pm Final
