CSC 314: Introduction to Cloud Based Data Science and Machine Learning : 3 hours

Description

A study of the resources available to do data science and machine learning in the cloud. Includes an overview of the different aspects of data science and machine learning. Pre-requisite: CSC 221 and CSC 210. CSC 312 is recommended

Goals for CSC 314 are:

• Enable students to create systems that use cloud based resources to do data science and machine learning
• Equip students to understand the ethics associated with data science and machine learning and to make quality decisions about them.

Course outcomes for CSC 314 are:

• Upon completion of CSC 314, students will:
  • have created two projects in the cloud of their choosing: one with data science and one with machine learning
  • understand the fundamentals of data science and machine learning and the ethical issues associated with each.

Program outcomes for CSC 314 are:

1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. (Computing student learning outcome 1)
2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program\x92s discipline. (Computing student learning outcome 2)
3. Communicate effectively in a variety of professional contexts. (Computing student learning outcome 3)
4. Recognize professional responsibilities and make informed judgments in computing practice based on legal, ethical, and moral principles. (Computing student learning outcome 4)
5. Function effectively as a member or leader of a team engaged in activities appropriate to the program\x92s discipline. (Computing student learning outcome 5)
6. Learn new areas of technology. (Computing student learning outcome 6)
7. Use technology to help bring Christ to the world and apply Christian principles to their work. (Computing student learning outcome 7)
8. Understand and use appropriate cloud technologies in specified areas (Computing student learning outcome 8)
9. Apply computer science theory and software development fundamentals to produce computing-based solutions. (Computer Science student learning outcome 1)
10. Support the delivery, use, and management of information systems within an information systems environment. (Computing and Information Systems student learning outcome 1)
11. Apply security principles and practices to maintain operations in the presence of risks and threats. (Cyber Security student learning outcome 1)

Integration

1. Self learning by using existing online lessons to learn the details about a specific AI or data science tool found in the cloud and using it to develop a proof of concept program.
2. Note taking by using notes taken from online lessons to produce reports while disconnected from the lessons
3. Technical writing and presentations by creating reports and presenting work to the class
4. Professional by doing assigned learning tasks in a timely manner
5. Ethics by writing a report about the ethical components of associated with one or more aspects of AI and data science in cloud computing

Details

• Professor: Arisoa Randrianasolo
• Office: Online via Zoom.
• Office hours MWF:1-4pm
• Class Time: Online
• Class Location: Online
• Textbook: No text required.

Course Content

The course will consist of lectures (online), labs and projects.

Policies

• Integrity
• LatePolicy

Assignments

• Weekly Labs contribute to 70% of your final grade
• Midterm project 15% of your final grade
• Final project 15% of your final grade

Grades

Your grades are made up of:

• Grade scale
  • 93% <= average <= 100% → A
  • 90% <= average < 93% → A-
  • 87% <= average < 90% → B+
  • 83% <= average < 87% → B
  • 80% <= average < 83% → B-
  • 77% <= average < 80% → C+
  • 73% <= average < 77% → C
  • 70% <= average < 73% → C-
  • 67% <= average < 70% → D+
  • 63% <= average < 67% → D
  • 60% <= average < 63% → D-
  • 0% <= average < 60% → F

Tentative Schedule

Week	Topics and Activities
1	Jupiter lab and python essential (identation,loop,selection, list)
2	python reading from file
3	Intro to supervised learnig: Linear regression
4	K-nearest neighbor
5	Naïve Bayes
6	Support Vector Machine
7	Support Vector Machine with Kernels
8	Midterm Project
9	Neural Network (identity)
10	Neural Network (other activation functions)
11	Convolutional Neural Network
12	Intro to unsupervised and Principal Component Analysis
13	Kmeans clustering
14	Density Based clustering
15	Agglomerative clustering
16	Final Project

Campus Integrity Policy

The student handbook (p. 156) states: “Any act of deceit, falsehood or stealing by unethically copying or using someone else’s work in an academic situation is strictly prohibited.

1. A student found guilty of plagiarism or cheating will receive an “F”(zero) for that particular paper, assignment or exam. Should this occur, the professor will have an interview with the student and will submit a written report of the incident to the academic dean.
2. If a second offense should occur, the student will be asked to appear before the professor, the academic dean and the vice president for student development.
   
   

The student should realize that at this point continuation in a course and even his/her academic career may be in jeopardy. In the event of a recommendation for dismissal, the matter shall be referred to the Student Development Committee.”

AI Use Policy

It is expected that any coursework (including, but not limited to, essays, papers, exams, projects, and lab reports) submitted by a student will be a product of their own creation, demonstrating their achievement of the learning outcomes related to the assigned task. With this in mind, note that submitting work that includes unauthorized or undocumented use of Artificial Intelligence (AI) may be considered as cheating or plagiarism. If you are unsure about appropriate use of AI on a given assignment, talk with your professor.

Services

The Americans with Disabilities Act (ADA) is a law which provides civil rights protection for people with disabilities. Bethel University, in compliance with equal access laws, requests that students with disabilities seeking to acquire accommodations make an appointment with the Center for Academic Success—Disability Services. It is located in the Miller-Moore Academic Center, 033. You may also phone 574-807-7460 or email rachel.kennedy@betheluniversity.com.edu for an appointment.

Education Majors:

Please use the link below to review all appropriate standards. Standards