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Big Data Analytics

Master digital data for meaningful insights.

Big Data. Bigger Career.

DAT 203 - Predictive Modelling and Data Mining C01 - Winter 2021

Academic Credit Value:
3 units
Course Delivery Mode:
Virtual Classroom
Hours of Study:
36 hours
Course Prerequisite(s):
Introductory statistics course, or DAT 101 Statistics for Data Analysis.
Course Anti-requisite(s):
No Anti-requisite(s)
Instructor Name:
CANCELLED
Course Dates:
01/11/2021 - 04/05/2021



Required Course Materials:
Required readings and materials will be posted on the Avenue to Learn course site.
Optional Course Materials:
Supplemental readings and materials will be posted on the Avenue to Learn course site.
Course Description:

Students are advised to retain course outlines for future use in support of applications for employment or transfer of credits.

Refer to the Policy & Procedure section for further course and McMaster Continuing Education information. 

The course will introduce predictive modeling techniques as well as related statistical and visualization tools for data mining. The course will cover common machine learning techniques that are focused on predictive outcomes. Students will learn how to evaluate the performance of the prediction models and how to improve them through time.
Learning Outcomes:

Upon completion of this course, students will:

1. Explain and demonstrate key machine learning (ML) terminology, ML applications and distinguish from more basic analytics and big data techniques
2. Demonstrate Python/R proficiency and ability to work with data
3. Implement machine learning functions
4. Examine different methods and their applicability towards various problems
5. Formulate and communicate (orally, written) advanced analytics concepts
6. Demonstrate ethical and professional standards related to the field of data analytics
7. Participate in large and small group discussions related to course work and projects
Course Evaluation

The final grade is calculated based on the following components:

Lab Assignments:  65% (10 labs @6.5% each)
Final Project: 35% (3 components: Project proposal: 5 marks; Presentation: 10 marks;
Project report: 20 marks)​

Course Format:
This course is designed to present the fundamental concepts and theories in data management and data analytics to promote the application to the workplace and professional practice. Course activities will include instructor presentations, required readings, and experiential learning activities (ie. case studies, group discussions, projects, etc.)
Assignment Submission:
In-class and/or A2L assignment folder(s). Consult with Instructor.
Late Coursework:
Late assignments will be subject to a 2% per day late penalty (includes weekends and holidays) for up to seven (7) days. After this date, no assignments will be accepted and a grade of zero (0) will be applied.  Extensions for course work must be approved by the instructor before the due date (see Academic Regulations below), and will be granted for illness or emergencies only. Students may be asked to submit supporting documentation for an extension request. NOTE:  This policy applies to assignments and other hand in type coursework only.  This policy does not apply to discussion board topics/postings which do not allow for late postings/contributions.

Policy & Procedures:

Academic Regulations (Attendance, Coursework, Tests/Exams):
In accordance to McMaster University’s General Academic Regulations, “it is imperative that students make every effort to meet the originally scheduled course requirements and it is a student’s responsibility to write examinations as scheduled.” Therefore, all students are expected to attend and complete the specific course requirements (i.e. attendance, assignments, and tests/exams) listed in the course outline on or by the date specified. Students who need to arrange for coursework accommodation, as a result of medical, personal or family reasons, must contact the course instructor within 48 hours of the originally scheduled due date. It is the student’s responsibility to contact the Program Manager to discuss accommodations and procedures related to deferred tests and/or examinations within 48 hours of the originally scheduled test/exam, as per policy. Failure to contact the course instructor, in the case of missed coursework, or the Program Manager, in the case of a missed test/examination, within the specified 48-hour window will result in a grade of zero (0) on the coursework/exam and no further consideration will be granted.

*Note: Supporting documentation will be required but will not ensure approval of accommodation(s).
Academic Integrity
You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university.

It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at http://www.mcmaster.ca/academicintegrity/

The following illustrates only three forms of academic dishonesty:
  1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
  2. Improper collaboration in-group work.
  3. Copying or using unauthorized aids in tests and examinations.
Academic Accommodations:

ACADEMIC ACCOMMODATION OF STUDENTS WITH DISABILITIES

Students with disabilities who require academic accommodation must contact Student Accessibility Services(SAS) at 905-525-9140 ext. 28652 or sas@mcmaster.ca  to make arrangements with a Program Coordinator. For further information, consult McMaster University’s Academic Accommodation of Students with Disabilities policy.

Academic Accommodation for Religious, Indigenous or Spiritual Observances (RISO)
Students requiring academic accommodation based on religious, indigenous or spiritual observances should follow the procedures set out in the RISO policy. Students will need to contact their instructors as soon as possible to make alternative arrangements for classes, assignments, and other coursework. It is thestudent’s responsibility to contact McMaster Continuing Education to discuss accommodations related toexaminations.
On-line Elements:

Conduct Expectations:

As a McMaster student, you have the right to experience, and the responsibility to demonstrate, respectful and dignified interactions within all of our living, learning and working communities. These expectations are described in the Code of Student Rights & Responsibilities (the “Code”). All students share the responsibility of maintaining a positive environment for the academic and personal growth of all McMaster community members, whether in-person or online.

It is essential that students be mindful of their interactions online, as the Code remains in effect in virtual learning environments. The Code applies to any interactions that adversely affect, disrupt, or interfere with reasonable participation in University activities. Student disruptions or behaviours that interfere with university functions on online platforms (e.g. use of Avenue 2 Learn, WebEx or Zoom for delivery), will be taken very seriously and will be investigated. Outcomes may include restriction or removal of the involved students’ access to these platforms.

 Copyright and Recording:

Students are advised that lectures, demonstrations, performances, and any other course material provided by an instructor include copyright protected works. The Copyright Act and copyright law protect every original literary, dramatic, musical and artistic work, including lectures by University instructors.

The recording of lectures, tutorials, or other methods of instruction may occur during a course. Recording may be done by either the instructor for the purpose of authorized distribution, or by a student for the purpose of personal study. Students who wish to record sessions need to acquire permission from the instructor. Students should be aware that their voice and/or image may be recorded by others during the class. Please speak with the instructor if this is a concern for you.

Turnitin.com:
In this course, we may be using a web-based service (Turnitin.com) to reveal plagiarism. Students will be expected to submit their work electronically to Turnitin.com and in hard copy so that it can be checked for academic dishonesty. Students who do not wish to submit their work to Turnitin.com must still submit a copy to the instructor. No penalty will be assigned to a student who does not submit work to Turnitin.com. All submitted work is subject to normal verification that standards of academic integrity have been upheld (e.g., on-line search, etc.). To see the Turnitin.com Policy, please go to McMaster Academic Integrity Policy.
Course Changes:

The instructor reserves the right to modify elements of the course and will notify students accordingly.

Extreme Circumstances:

The University reserves the right to change the dates and deadlines for any or all courses in extreme circumstances (e.g., severe weather, labour disruptions, etc.). Changes will be communicated through regular McMaster communication channels, such as McMaster Daily News, A2L and/or McMaster email.

Course Withdrawal Policy:
Policies related to dropping a course and course withdrawals are posted to the Centre for Continuing Education’s program webpage, FAQs & Policies (https://www.mcmastercce.ca/cce-policies#Dropping).
Storm Closure Policy:

In the event of inclement weather, the Centre for Continuing Education will abide by the University’s Storm Closure Policy: https://www.mcmaster.ca/policy/Employee/storm_emergency_policy.pdf, and will only close if the University is closed. All in-class courses, exams and room bookings by internal and external clients will be cancelled if the Centre for Continuing Education is closed. On-line courses will take place as scheduled.

Grading Scale:
 Grade Equivalent
Grade Point
Equivalent Percentages
A+ 12 90-100
A 11 85-89
A- 10 80-84
B+ 9 77-79
B 8 73-76
B- 7 70-72
C+ 6 67-69
C 5 63-66
C- 4 60-62
D+ 3 57-59
D 2 53-56
D- 1 50-52
F 0 0-49
Course Schedule:

Week

Topic

Activity

1

Course Introduction

Introduction to Predictive Modelling

Lab #1: Lab Activity: Data Science Tools

2

Preparing Data and Feature Engineering

Lab #2: Data Cleaning

3

Linear Regression

Linear Regression with Variable Selection

Lab #3: Linear Regression wirh Variable Selection

4

Classification: Logistic Regression

Lab #4: Logistic Regression

5

Hyper-parameters, Regularization and Cross-Validation

Lab #5: Improving model performance with regularization

6

Classification : Decision Trees

Lab #6: Decision trees

7

Simulation Modeling

Lab #7: Marketing Simulation

8

Dimensionality Reduction

Lab #8 Priniciple Component Analysis

9

Clustering: K-mean

Lab #9: K-means

10

Clustering: Shift mean

Lab #10: Shift mean

11

Spatial-Temporal Analytics

Lab Activity; Examples of Spatial-Temporal Analytics

12

Project Presentations

Class Wrap up