1. Set a timer for one hour to read the Syllabus (including links), Schedule (see link below), and Blackboard (Bb) content.

2. In Bb, select the Assignments tab on the left. Complete the Orientation Exam. 

3. Begin work on the first assignment.

4. Maintain a daily commitment to studying and coding.

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:

COSC1336 or department chair approval.

Learning Objectives:

To obtain introductory-to-intermediate knowledge and practical skills applying the following concepts and technologies.

• Descriptive Statistics
• Inferential Statistics
• Python for Data Science
• Jupyter Notebook
• SciPy
• NumPy
• Matplotlib
• Pandas
• Seaborn

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.

*** Schedule ***

Course Requirements and Grading Rubric:

Tutoring Services (generally intended for entry-level subject material)

General Course Policies

Welcome Letter

Three Major Units

The course is structured into three major units:

• Introduction to Descriptive Statistics
• Whirlwind tour of Python
• Data Science Handbook

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.