update for 2021

ubco-cmps · Sep 8, 2021 · 99b8768 · 99b8768
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diff --git a/_toc.yml b/_toc.yml
@@ -1,70 +1,77 @@
 format: jb-book
-root: about/syllabus
+root: about/unsyllabus
 parts:
 - caption: About this course
   chapters:
-  - file: about/course_schedule
-  - file: about/reference
-    title: Reference Materials
-- caption: Getting started!
-  chapters:
-  - file: getting-started/todo_list
-    title: Things to do before start of course
+  - file: about/syllabus 
+  - file: about/syllabus_bits/course_schedule
+    title: Course Schedule and Learning Outcomes
+  - file: about/accommodations
+
+# - caption: Getting started!
+#   chapters:
+#   - file: getting-started/todo_list
+#     title: Things to do before start of course
+# - caption: Course Feedback
+#   chapters:
+#   - file: about/feedback
+#     title: Anonymous Feedback Form
+# - caption: Lectures
+#   chapters:
+#   - file: class/lecture1
+#     title: Lecture 1 - Introduction to Python
+#   - file: class/lecture2
+#     title: Lecture 2 - Functions in Python
+#   - file: class/lecture3
+#     title: Lecture 3 - Style and numpy
+#   - file: class/lecture4
+#     title: Lecture 4 - Classes and modules
+#   - file: class/lecture5
+#     title: Lecture 5 - Introduction to R and the Tidyverse
+#   - file: class/lecture6
+#     title: Lecture 6 - Functions and Testing in R
+#   - file: class/lecture7
+#     title: Lecture 7 - Functional-style programming in R
+#   - file: class/lecture8
+#     title: Lecture 8 - Tidy evaluation in R
+# - caption: Labs
+#   chapters:
+#   - file: labs/intro
+#     title: Start here!
+#   - file: labs/lab1
+#     title: Lab 1 - Python Functions, Lists, and Dictionaries
+#   - file: labs/lab2
+#     title: Lab 2 - Python 2
+#   - file: labs/lab3
+#     title: Lab 3 - Intro to R
+# - caption: Practice Problems
+#   chapters:
+#   - file: practice/intro
+#     sections:
+#     - file: practice/01_strings_exercise
+#       title: 1. Strings
+#     - file: practice/02_numbers_exercise
+#       title: 2. Numbers
+#     - file: practice/03_conditionals_exercise
+#       title: 3. Conditionals
+#     - file: practice/04_lists_exercise
+#       title: 4. Lists
+#     - file: practice/05_dictionaries_exercise
+#       title: 5. Dictionaries
+#     - file: practice/06_for_loops_exercise
+#       title: 6. Loops
+#     - file: practice/07_functions_exercise
+#       title: 7. Functions
+#     - file: practice/10_file_io_exercise
+#       title: 8. File input/output
+#     - file: practice/11_classes_exercise
+#       title: 9. Classes
+#     - file: practice/12_exceptions_exercise
+#       title: 10. Exceptions
+#     - file: practice/13_debugging_exercise
+#       title: 11. Debugging
+
 - caption: Course Feedback
   chapters:
   - file: about/feedback
-    title: Anonymous Feedback Form
-- caption: Lectures
-  chapters:
-  - file: class/lecture1
-    title: Lecture 1 - Introduction to Python
-  - file: class/lecture2
-    title: Lecture 2 - Functions in Python
-  - file: class/lecture3
-    title: Lecture 3 - Style and numpy
-  - file: class/lecture4
-    title: Lecture 4 - Classes and modules
-  - file: class/lecture5
-    title: Lecture 5 - Introduction to R and the Tidyverse
-  - file: class/lecture6
-    title: Lecture 6 - Functions and Testing in R
-  - file: class/lecture7
-    title: Lecture 7 - Functional-style programming in R
-  - file: class/lecture8
-    title: Lecture 8 - Tidy evaluation in R
-- caption: Labs
-  chapters:
-  - file: labs/intro
-    title: Start here!
-  - file: labs/lab1
-    title: Lab 1 - Python Functions, Lists, and Dictionaries
-  - file: labs/lab2
-    title: Lab 2 - Python 2
-  - file: labs/lab3
-    title: Lab 3 - Intro to R
-- caption: Practice Problems
-  chapters:
-  - file: practice/intro
-    sections:
-    - file: practice/01_strings_exercise
-      title: 1. Strings
-    - file: practice/02_numbers_exercise
-      title: 2. Numbers
-    - file: practice/03_conditionals_exercise
-      title: 3. Conditionals
-    - file: practice/04_lists_exercise
-      title: 4. Lists
-    - file: practice/05_dictionaries_exercise
-      title: 5. Dictionaries
-    - file: practice/06_for_loops_exercise
-      title: 6. Loops
-    - file: practice/07_functions_exercise
-      title: 7. Functions
-    - file: practice/10_file_io_exercise
-      title: 8. File input/output
-    - file: practice/11_classes_exercise
-      title: 9. Classes
-    - file: practice/12_exceptions_exercise
-      title: 10. Exceptions
-    - file: practice/13_debugging_exercise
-      title: 11. Debugging
+    title: Anonymous Feedback Form
diff --git a/about/syllabus.md b/about/syllabus.md
diff --git a/about/syllabus_bits/calendar_entry.md b/about/syllabus_bits/calendar_entry.md
@@ -0,0 +1,6 @@
+**{{ COURSE_CODE }} ({{ CREDITS }}) {{ TITLE }}**
+
+The [UBCO calendar description](http://www.calendar.ubc.ca/okanagan/courses.cfm?code=DATA) of this course is: 
+
+> Introduction to software and tools for Data Science. Setup process. Credit will be granted for only one of DATA 301 or DATA 530.
+> Restricted to students in the MDS program.
diff --git a/about/course_schedule.md → about/syllabus_bits/course_schedule.md b/about/course_schedule.md → about/syllabus_bits/course_schedule.md
@@ -2,44 +2,21 @@
 Course Schedule
 =======================
 
-| Class # | Date                   | Course Topics                                                | Readings  |
-|---------|------------------------|--------------------------------------------------------------|-----------|
-| 1       | Wednesday September 9  | Python Introduction and Data Types                           | Lecture 1 |
-| 2       | Monday September 14    | Python Conditions and Loops                                  | Lecture 2 |
-| 3       | Wednesday September 16 | Python Lists, Tuples, Dictionaries, and Functions            | Lecture 3 |
-| 4       | Monday September 21    | Python File I/O and Exceptions, Modules and Objects          | Lecture 4 |
-| 5       | Wednesday September 23 | Introduction to R and the tidyverse                          | Lecture 5 |
-| 6       | Monday September 28    | Quiz 1								                          | Lecture 6 |
-| 7       | Wednesday September 30 | R Data Structures: Vectors, Lists, Matrices, and Data Frames | Lecture 7 |
-| 8       | Monday October 5       | R Hypothesis Testing and Linear Regression                   | Lecture 8 |
-| 9       | Wednesday October 7    | Quiz 2                                                       | Lecture 9 |
+| Class # | Date                   | Course Topics                                                |
+|---------|------------------------|--------------------------------------------------------------|
+| 1       | Wednesday September 8  | Python Introduction and Data Types                           |
+| 2       | Monday September 13    | Python Conditions and Loops                                  |
+| 3       | Wednesday September 15 | Python Lists, Tuples, Dictionaries, and Functions            |
+| 4       | Monday September 20    | Python File I/O and Exceptions, Modules and Objects          |
+| 5       | Wednesday September 22 | Introduction to R and the tidyverse                          |
+| 6       | Monday September 27    | Quiz 1								                          |
+| 7       | Wednesday September 29 | R Data Structures: Vectors, Lists, Matrices, and Data Frames |
+| 8       | Monday October 4       | R Hypothesis Testing and Linear Regression                   |
+| 9       | Wednesday October 6    | Quiz 2                                                       |
 
-## Lecture Learning Outcomes
-
-1. Introduction to R and Review of Basic Statistics
-
-	- understand purpose and usefulness of R and difference with Python
-	- define different types of data: qualitative, quantitative
-	- describe data use numerical summaries (measure of centre/spread)
-	- define and calculate: mean, median, variance, standard deviation, range
-	- define: quantile, quartile, interquartile range, five number summary	
-	- install and use RStudio
-	- set and get the working directory
-	- list the different types of data structures in the R language
-	- write small programs/commands in R that may use variables, conditions, loops, and functions
-	- use R to determine the type and structure of an object
-
-2. R Data Structures: Vectors, Lists, Matrices, and Data Frames
-
-	- create, index, and subset vectors, lists, and matrices
-	- generate vectors of random data
-	- read in data sets from files
-	- use head and tail to explore a data set
-	- use data frames/factors for data analysis
-	- explain what factors are and why they are useful
-	- create graphs/visualizations: frequency table, bar chart, histogram, boxplot using base R and ggplot2
+## Learning Outcomes
 
-3. Python Introduction and Data Types
+1. Python Introduction and Data Types
 
 	- understand Python 2 and Python 3 have some syntax differences
 	- follow Python syntax rules including indentation, variable naming, and comments
@@ -53,14 +30,14 @@ Course Schedule
 	- use date and time functions
 	- proficient in reading input from console and output results to console
 
-4. Python Conditions and Loops
+2. Python Conditions and Loops
 
 	- create comparisons and use them for decisions with if
 	- combine conditions with and, or, not
 	- make decisions using if/elif/else syntax
 	- perform repetition using loop constructs for and while
 		
-5. Python Lists, Tuples, Dictionaries, and Functions
+3. Python Lists, Tuples, Dictionaries, and Functions
 
 	- create and use lists and list functions
 	- understand advance syntax for list comprehensions, list slicing
@@ -72,7 +49,7 @@ Course Schedule
 	- use built-in functions and functions in the math library including generating random numbers
 	- exposure to passing functions and lambda functions
 
-6. Python File I/O and Exceptions
+4. Python File I/O and Exceptions
 
 	- open, read, write, and close text files
 	- process CSV files including using the csv module
@@ -81,7 +58,7 @@ Course Schedule
 	- explain the purpose of exceptions and exception handling
 	- use try-except statement to handle exceptions and understand how each of try, except, else, finally blocks are used
 
-7. Python Modules and Objects
+5. Python Modules and Objects
 
 	- use object-oriented terminology: class, object, method, parameter, instance variable, inheritance, superclass, subclass
 	- create classes with methods
@@ -95,7 +72,30 @@ Course Schedule
 	- write simple Map-Reduce programs
 	- apply object methods using the dot syntax
 
-8. R Hypothesis Testing and Linear Regression
+7. Introduction to R and Review of Basic Statistics
+
+	- understand purpose and usefulness of R and difference with Python
+	- define different types of data: qualitative, quantitative
+	- describe data use numerical summaries (measure of centre/spread)
+	- define and calculate: mean, median, variance, standard deviation, range
+	- define: quantile, quartile, interquartile range, five number summary	
+	- install and use RStudio
+	- set and get the working directory
+	- list the different types of data structures in the R language
+	- write small programs/commands in R that may use variables, conditions, loops, and functions
+	- use R to determine the type and structure of an object
+
+8. R Data Structures: Vectors, Lists, Matrices, and Data Frames
+
+	- create, index, and subset vectors, lists, and matrices
+	- generate vectors of random data
+	- read in data sets from files
+	- use head and tail to explore a data set
+	- use data frames/factors for data analysis
+	- explain what factors are and why they are useful
+	- create graphs/visualizations: frequency table, bar chart, histogram, boxplot using base R and ggplot2
+
+9. R Hypothesis Testing and Linear Regression
 
 	- explain the purpose of confidence intervals
 	- perform hypothesis testing using R

diff --git a/about/syllabus_bits/course_teaser.md b/about/syllabus_bits/course_teaser.md
@@ -0,0 +1,13 @@
+You should take Physics 111 if you want to have some fun while also learning cool ways how our universe works.
+For those of you, physics is new and alien - I welcome you to this course!
+I hope to make this course as inclusive and accessible as I possibly can, and as long as you have some reasonable skills with algebra, you should be able to pick up most, if not all of the things you need to succeed in this course.
+For some of you, the topics you learn may be quite familiar to you already, it will still be valuable for you to take this course and crystallize in your minds some key concepts that you thought you knew.
+I believe that one of the best ways to ensure learning happens, is repeated exposure to the same content from different lenses.
+So I hope that exposure to content you may already be familiar with, through a new lens will help you appreciate the content more.
+
+We will also be watching lots of cool-physics related videos so there's also that!
+
+```{tip}
+To get the most out of this course, I suggest you come in with an open mind, and participate fully and willingly in the activities I have planned for you.
+It may not be something you've done before, but the techniques I'll be using in my course are research-supported, and battle-tested!
+```
diff --git a/about/syllabus_bits/grading_practices_detailed.md b/about/syllabus_bits/grading_practices_detailed.md
@@ -0,0 +1,18 @@
+The grading scheme for this course is:
+
+| Item          | Weight | Frequency           |
+|---------------|--------|---------------------|
+| Learning Logs | 10%    | Weekly on Fridays   |
+| Labs          | 50%    | Weekly on Tuesdays  |
+| Quiz 1        | 20%    | Once on October 4th |
+| Quiz 2        | 20%    | Once on October 6th |
+
+
+```{attention} 
+All (non-quiz) deadlines in this course have an automatic 48 hour grace period after the due dates listed above.
+Any submissions submitted past the grace period will not be graded.
+```
+
+```{note}
+Important: The maximum mark you can get on each item is 100%. Any available bonus marks are not transferrable to other assignments.
+```
diff --git a/about/syllabus_bits/grading_practices_simple.md b/about/syllabus_bits/grading_practices_simple.md
@@ -0,0 +1,20 @@
+| Item          | Weight | Frequency |
+|---------------|--------|-----------|
+| Learning Logs | 10%    | Weekly    |
+| Labs          | 50%    | Weekly    |
+| Quiz 1        | 20%    | Once      |
+| Quiz 2        | 20%    | Once      |
+
+Final grades will be based on the evaluations listed above and the final grade will be assigned according to the standardized grading system outlined in the [UBC Okanagan Calendar](http://okanagan.students.ubc.ca/calendar/).
+
+```{note}
+Note: All course activities and assessments, including the Final Exam, will be conducted Online.
+```
+
+```{caution} 
+Please note that the "Labs" in this course are run almost completely separately from the Lectures/Tutorials/Tests/Exams. Any grading policies instituted in the lecture portion of the course are independent of the lab policies. The Labs will also be conducted online.
+```
+
+```{note}
+Note: Any requests for changes to final exams must be sent to the office of the Associate Dean of Students (fos.students.ubco@ubc.ca).
+```
diff --git a/about/syllabus_bits/passing_requirement.md b/about/syllabus_bits/passing_requirement.md
@@ -0,0 +1,12 @@
+Yes. To pass this course, you must:
+
+- Achieve an average of 60% in the quizzes.
+- Achieve an average of at least 60% across all labs 
+
+If students do not satisfy the appropriate requirements, the student will be assigned the **lower** of their earned course grade or, a maximum overall grade of 45% in the course.
+
+Please refer to the [UBCO Calendar](http://www.calendar.ubc.ca/okanagan/index.cfm?tree=18,285,984,1168) for additional details for program-wide passing requirements. 
+
+In particular:
+
+> 60% is the minimum passing grade for master’s students; however, only 6 course credits with grades from 60-67% may be counted toward a master's program. For all other courses, students must obtain a minimum of 68%.