Syllabus Sections
- GETTING STARTED
- COURSE DESCRIPTION/RATIONALE
- STUDENT LEARNING OUTCOMES/LEARNING OBJECTIVES
- READINGS
- COURSE REQUIREMENTS
- GENERAL COURSE POLICIES & WELCOME LETTER
- COURSE SUBJECTS
- BLACKBOARD ACCESS
Publish Date
01/11/2021 14:07:14
Computer Programming: Scientific Python
ITSE-1302
Credit Spring 2021
01/19/2021 - 05/16/2021
Course Information
Section 001
Distance Learning
MW 09:00 - 10:00
DLS DIL
Rudy Martinez
Section 001
Laboratory
MW 10:00 - 10:45
DLS DIL
Rudy Martinez
Section 002
Distance Learning
ONL DIL
Rudy Martinez
Office Hours
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--- - ---
Email Professor to Confirm Appointment. -
M T W Th F
7:30am - 8:30am
Use Google Meet audio.
getting Started
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Set a timer for one hour to read the Syllabus (including links), Schedule (see link below), and Blackboard (Bb) content.
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In Bb, select the Resources tab on the left. Watch the Orientation Video.
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In Bb, select the Assignments tab on the left. Complete the Orientation Exam.
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Begin work on the first assignment.
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Maintain a daily commitment to studying and coding.
Course Description/Rationale
Official description:
This course is an introduction to scientific computer programming including design, development, testing, implementation, and documentation. It may include: Python Fundamentals, functions, data structures, classes, objects, statistical programming, data visualization with MatPlotLib and Programming with the NumPy library.
Prerequisites:
COSC 1336 or department chair approval.
Student Learning Outcomes/Learning Objectives
Learning Objectives:
To obtain introductory-to-intermediate knowledge and practical skills applying the following concepts and technologies.
- Descriptive Statistics
- Python for Data Science
- Jupyter Notebook
- SciPy
- NumPy
- Matplotlib
- Pandas
- Seaborn
Readings
Course Content:
OER (Open Educational Resources) are used in this course and are listed in the Blackboard classroom.
Purchase of a textbook is not required.
Course Requirements
*** Schedule ***
Course Requirements and Grading Rubric:
Tutoring Services (generally intended for entry-level subject material)
Course Subjects
Three Major Units
The course is structured into three major units:
- Introduction to Descriptive Statistics
- Whirlwind tour of Python
- Data Science Handbook
Blackboard Access
Blackboard (Bb) course access 1st week of class:
Students must access the course in Bb during the 1st week of class to be counted as "Attending". If a student does not access the course in Bb during the 1st week of class s/he will be classified as "Never Attended" and will be ineligible for financial aid and automatically dropped from the course.