Lifelong Learning for a Brighter World

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Foundations of Analytics

Business Intelligence, Data Analysis and Data Science

Your Introduction to the World of Analytics

BDA 201 - Statistics for Data Analytics C01

Academic Credit Value:
3 units
Course Delivery Mode:
Online
Hours of Study:
36 hours
Course Prerequisite(s):
Grade 11/12 Mathematics (College/University prep)
Course Anti-requisite(s):
Not Applicable
Instructor Name:
Owais Hashmi
Course Dates:
01/27/2020 - 03/23/2020



Required Course Materials:
Required readings and materials will be posted on the Avenue to Learn course site. This course will be delivered online. Live lessons to be delivered on the scheduled date and time via WebEx. Required equipment is a computer, microphone/headset to participate in live lessons.
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. 

This course introduces descriptive statistics, basic inferential statistics, linear regression, and probability concepts and calculations. Practical application activities in the course focus on how statistical methods are used in the analysis of data. Common statistical tools will be introduced and employed in order to demonstrate how significant and insightful information is collected, used and applied to problem-solving processes. This course is designed for individuals with no, or limited, study in Statistics.

Learning Outcomes:

1.     Interpret and produce various graphical displays of data and information and learn how to choose the most appropriate technique in a variety of situations.

2.     Understand that all data has variability: separate that variability into information (knowledge) and error (unknown structure, noise, randomness)

3.     Interpret and compute confidence intervals and data statistics (mean, median, histograms and significant differences).

4.     Understand and perform basic probability calculations

  5.     Solve problems with statistical variables that have a binomial, Poisson, normal or other probability distributions

6.     Fit and interpret linear correlations and regression and determine correlation coefficients between different data variables.

7.     Use multiple regression to predict a response variable and determine the most significant      predictor variables.

8.     Design an experimental program and then interpret experimental data.

9.     Use R and Python to process and analyze data.

Course Evaluation

The final grade is calculated based on the following components: 

·       Weekly Tutorial: 22.5% (9@2.5% each)

·       Weekly Assignments: 45% (9@5% each)

·       Course Project: 32.5% (Part 1: 15%; Part 2: 17.5%)

Course Format:
This course is designed to present the fundamental concepts and theories in statistics and data analysis and promotes the application to the workplace and professional practice. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, lab activities, group discussions, projects, etc.).
Assignment Submission:
Course assignments are submitted to the appropriate A2L Assignment folder by the specified due date
Late Coursework:
Late submissions will be penalized 2% per day (including weekends and holidays) up to seven (7) days past the due date. After seven days, the Assignment folder will close, no further assignments will be accepted, and a grade of zero(0) will be assigned for the course work item unless otherwise specified by the Instructor.  All coursework must be submitted by the last official date of the course. Requests for extensions must be submitted to the Instructor before the assignment due date (see Coursework Policies).  Extensions are permitted for exceptional circumstances only; supporting documentation may be requested.   

Policy & Procedures:

Academic Regulations (Attendance, Coursework, Tests/Exams):

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:

Students with disabilities who require academic accommodations must contact the Student Accessibility Centre (SAS) to meet with an appropriate Disability Services Coordinator. To contact SAS, phone 905-525-9140 ext. 28652, or email sas@mcmaster.ca. For further information, consult McMaster University’s Policy for Academic Accommodation for Students with Disabilities.

On-line Elements:
In this course, we will be using online elements, which may include email, Avenue to Learn, and external websites.  Students should be aware that, when they access the electronic components of this course, private information such as first and last names, usernames for the McMaster e-mail accounts, and program affiliation may become apparent to all other students in the same course. The available information is dependent on the technology used. Continuation in this course will be deemed consent to this disclosure. If you have any questions or concerns about such disclosure please discuss this with the course instructor.
Turnitin.com:

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. 

 

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

Schedule

Module

Activity

Week 1

 

Visualizing data

· In class tutorial

· Assignment – Due Week 2

Week 2

 

Statistics basic concepts

· In class tutorial

· Assignment - Due Week 3

Week 3

 

Basic probability

· In class tutorial

· Assignment - Due Week 4

Week 4

 

Probability Distributions

· In class tutorial

· Assignment - Due Week 5

· Project - Part 1

Week 5

 

Correlations and regression 1

· In class tutorial

· Assignment - Due Week 6

Week 6

 

Correlations and regression 2

· In class tutorial

· Assignment - Due Week 7

Week 7

 

Estimate with confidence intervals

· In class tutorial

· Assignment - Due Week 8

· Project - Part 2

Week 8

 

Design and analysis of experiments 1

· In class tutorial

· Assignment - Due Week 9

Week 9

Design and analysis of experiments 2

· In class tutorial

· Assignment - Due Week 9