Fundamentals of Machine Learning in Data Science

Faculty
Commerce & Business Administration
Department
Computing Studies & Information Systems
Course Code
CSIS 3290
Credits
3.00
Semester Length
15 Weeks
Max Class Size
35
Method Of Instruction
Lecture
Lab
Typically Offered
To be determined
Campus
Online

Overview

Course Description
In this course, students will learn to apply machine learning concepts to analyze data and make predictions. Students will learn how to collect and wrangle data, to explore data using statistics and visualizations, to transform data for further modeling, to model data using machine learning algorithms to predict data patterns, and to evaluate these model-based predictions. Students will be expected to have prior experience with fundamentals of programming.
Course Content
  1. Programming language review for data analytics
    • Basic syntax, variables, control flow, loops, install and import libraries for data processing such as SciPy, NumPy, Pandas, Sci-Kit Learn, TensorFlow or other similar libraries and packages.
  2. Data And Features: using libraries such as NumPy and Pandas                   
    • Represent data using lists, arrays for structured data
    • Work with data frames using packages such as Pandas to represent diverse data
    • Use Control Flow for filtering data and performing filtered computations
    • Manipulate Data using functions and packages to process the data and perform computations
    • Understand, determine and represent Features
    • Perform Data Wrangling
  3. Exploring Data: - using libraries such as Matplotlib                                     
    • Visualize Data by creating plots using tools such as Matplotlib
    • Perform high dimensionality visualizations
  4. Transforming Data: using libraries such as Sci-Kit learn                              
    • Create Data Transformers and apply dimensionality reducing techniques as PCA
  5. Data Modeling: using libraries such as Keras, TensorFlow and scikit-learn
    • Use machine learning techniques such as clustering, supervised learning, K-nearest neighbours, Regression to model the data
  6. Evaluating Data: Evaluate modeled data using evaluation techniques         
    • Create and apply confusion matrices
    • Perform cross-validation using scoring metrics
    • Implement and apply power tuning and pipelining to evaluate the data
Methods Of Instruction

Lecture, seminars, demonstrations, and hands-on exercises/projects in the lab

Means of Assessment

Labs

0-5%

Project(s)

15-25%

Midterm Examination

30-40%

Final Examination*

30-40%

Total

100%

Some of these assessments may involve group work.

* Practical hands-on computer exam

In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components (including quizzes, tests, exams).

Learning Outcomes

At the end of this course, the successful student will be able to:

  1. Install and use appropriate tools and libraries needed for Data Science
  2. Understand and process data and features
  3. Collect and Wrangle Data for further processing
  4. Explore Data using statistics and visualizations
  5. Transform Data to a structure suitable for data modeling
  6. Model Data using machine learning algorithms
  7. Evaluate model-based predictions
Textbook Materials

Custom courseware, class notes provided by the instructor, and online resources or other textbooks as approved by the department

Requisites

Prerequisites

min grade C in CSIS 1190 OR currently active in the

PDD Data Analytics

PBD Computer and Information Systems

(Note: CSIS 1175 recommended)

Corequisites

No corequisite courses.

Equivalencies

 

 

Requisite for

This course is not required for any other course.

Course Guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester / year of the course, consider the previous version as the applicable version.

Course Transfers

Institution Transfer Details Effective Dates
Athabasca University (AU) AU COMP 3XX (3) 2019/05/01 to -
College of New Caledonia (CNC) CNC CSC 2XX (3) 2019/05/01 to -
Simon Fraser University (SFU) No credit 2019/05/01 to -
Thompson Rivers University (TRU) TRU COMP 3XXX (3) 2019/05/01 to -
University Canada West (UCW) UCW CMPT 3XX (3) 2019/05/01 to -
University of Northern BC (UNBC) UNBC CPSC 3XX (3) 2019/05/01 to -
University of the Fraser Valley (UFV) UFV COMP 381 (3) 2019/05/01 to -

Course Offerings

Fall 2020

CRN
Days
Dates
Start Date
End Date
Instructor
Status
Location
36507
Mon
08-Sep-2020
- 07-Dec-2020
08-Sep-2020
07-Dec-2020
Sarif
Bambang
Waitlist
Online
CSIS 3290 001 - This section is restricted to students in PDD Data & Analytics, and PBD Computer & Information Systems programs. Students will NOT receive credit for both CSIS 3190 and CSIS 3290.


This course will include synchronous on-line activities. Students should plan to be available on-line at scheduled course times.
Max
Enrolled
Remaining
Waitlist
35
23
12
2
Days
Building
Room
Time
Mon
14:30 - 17:20