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move more course-specific material out of the book
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‎doc/source/1_introduction.rst‎

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Introduction: abstraction in mathematics and programming
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========================================================
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.. details:: Video introduction.
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.. .. details:: Video introduction.
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.. vimeo:: 486106801
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.. .. vimeo:: 486106801
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.. only:: html
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.. .. only:: html
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Imperial students can also `watch this video on Panopto <https://imperial.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ee8cae7f-1b42-4db3-adc0-ac840144de53>`_
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.. Imperial students can also `watch this video on Panopto <https://imperial.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=ee8cae7f-1b42-4db3-adc0-ac840144de53>`_
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A core tool of mathematics is to define abstract objects and the
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operations which apply to them. This approach defines all the basic
@@ -63,145 +63,6 @@ abstraction in mathematics, abstraction in coding is a form of
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constructive laziness: it simultaneously allows the mathematician to
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achieve more and do less work.
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This is a second course in programming, building a previously
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acquired basic understanding of programming in Python. In covering
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more advanced programming, we will pay particular attention to objects
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and abstraction as they occur in Python. Furthermore, we will do so
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from a mathematician's perspective, understanding programming as a
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process of defining and manipulating mathematical objects, and
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scientifically testing and debugging the results.
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How to do this course
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---------------------
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Programming, like mathematics, is a practical, problem solving discipline. It's
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not possible to learn to program just by reading notes and watching lectures. To
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learn to program you need to put the content of the course into practice by
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writing code. The structure of this course is designed to help you to really
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understand new concepts in programming by putting them into practice. Each week,
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we run through the same cycle:
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1. Read the notes
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2. Watch the videos
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3. Do the quiz
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4. Write the code
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The notes
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.........
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All of the new material we cover in this course is presented in these notes.
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Each chapter contains the material for one week of term, so in week :math:`n`,
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you work through chapter :math:`n`. The notes always the starting point for your work.
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Each chapter will introduce new concepts in programming, often tied back to
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related mathematical concepts, and always illustrated by practical code
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examples. Python has excellent `official online documentation
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<https://docs.python.org/3/>`_, and we link to that throughout the text.
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External links show up in orange while :ref:`internal links to other parts of
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the notes <introduction>` are red. Sometimes we introduce counterexamples:
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illustrations of code errors or bad implementation ideas. These will be flagged
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with a big red cross:
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.. container:: badcode
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.. code-block:: python3
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print "Hello World"
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Conversely, if it's necessary in context to highlight which approach is the
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correct one, the code will come with a big green tick:
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.. container:: goodcode
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.. code-block:: python3
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print("Hello World")
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At the end of each chapter there is a glossary containing key new concepts
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introduced in that chapter. Always check the glossary and convince yourself that
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you understand all the terms introduced there, this is a good check on whether
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you have understood the chapter as a whole. However, there is more to learning
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new concepts than just the vocabulary, so don't be tempted to skip reading the
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chapter and just jump to the glossary!
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126-
The chapters broadly alternate between introducing new programming concepts,
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such as :ref:`objects <objects>`, :ref:`abstract data types
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<abstract_data_types>`, or :ref:`inheritance <inheritance>`, and
129-
covering various aspects of the craft of programming, such as :ref:`style
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<inheritance>` and :ref:`debugging <debugging>`. In this way the course combines specific programming knowledge
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with more general coding skills.
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The videos
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..........
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Throughout the notes are links to videos. These aren't typical lecture videos in
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that they're not primarily focussed on delivering the new ideas in the course.
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In particular, the videos don't set out to duplicate the delivery of the
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material in the notes. Instead, the videos focus on putting the concepts into
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practice, often by showing live coding sessions. Usually you'll want to watch
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the video for a given section *after* reading the corresponding notes.
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The quizzes
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...........
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Towards the end of each chapter is a link back to a quiz on Imperial's
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Blackboard system. The quizzes are designed to allow you to convince yourself
148-
that you've understood the material in the chapter. Sometimes they will simply
149-
be multiple choice questions testing your understanding of the material, but
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sometimes you will need to open up Python and try things out in order to work
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out the right answer. The quizzes do not contribute to your module grade, but how
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well you are doing on them is an indication of your progress on the module.
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The exercises
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.............
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As we've already noted, really learning to program better is only achieved by
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writing code. The core of each week's activities is therefore to put the new
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concepts and programming structures you've learned into practice. The
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programming exercises are given at the end of each chapter, just before the
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glossary. Each time there will be a skeleton code available from
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:ref:`GitHub Classroom <fons:github_classroom_exercise>` which provides the starting
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point. Sometimes you might be asked to complete a piece of code while on other
164-
occasions you'll need to write a whole Python module from scratch. Each set of
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exercises will come with a matching set of tests. These are small programs which
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check whether your code produces the correct responses to a range of inputs.
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Tests like this provide immediate feedback and enable you to know how you are
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doing without having to wait for code to be submitted and marked.
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.. note::
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Solutions to exercises will not be issued. The notes and accompanying
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example code contain examples of the same programming constructs that the
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exercises require you to implement, and the tests provide a mechanism to
175-
know when you have a correct answer. There are also help mechanisms via the
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course forum and the lab sessions. Issuing solutions to problems would
177-
simply encourage students to study the solutions rather than write code, and
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the only way to learn to program is to write code.
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Assessment
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..........
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The weekly quiz and exercises are not assessable: they are formative activities
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designed to help you learn the module. Instead, the module will be assessed by
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two controlled programming assessments, effectively programming exams. The first
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programming assessment will be held in week 7 and will be worth 20% of the
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marks for the course. This provides an opportunity to receive feedback in the
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middle of the term, and is a practice for the main exam. The :ref:`course contents for
189-
week 7 <midterm>` will comprise practice programming exercises for this midterm test.
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The second programming assessment will be held in the main May exam period and
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will count for the other 80% of the course.
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The instructions and skeleton code for the programming exercises will be
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released at a fixed time on GitHub Classroom, and the code which you have
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committed to GitHub by the end of the allotted time will be marked.
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The exam questions will be similar to the weekly exercises in the course,
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and may include new programmes to write from specification, modifications or
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extensions to be made to code which is provided, and debugging exercises in
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which defective code is provided which you need to correct. Marks will be
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allocated both for the functional correctness of the code written, and for
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good style and following Python coding conventions.
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Obtaining the right software tools
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----------------------------------
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‎doc/source/tests_and_exams.rst‎

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How to do this course
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---------------------
3+
4+
Programming, like mathematics, is a practical, problem solving discipline. It's
5+
not possible to learn to program just by reading notes and watching lectures. To
6+
learn to program you need to put the content of the course into practice by
7+
writing code. The structure of this course is designed to help you to really
8+
understand new concepts in programming by putting them into practice. Each week,
9+
we run through the same cycle:
10+
11+
1. Read the notes
12+
2. Watch the videos
13+
3. Do the quiz
14+
4. Write the code
15+
16+
The notes
17+
.........
18+
19+
All of the new material we cover in this course is presented in these notes.
20+
Each chapter contains the material for one week of term, so in week :math:`n`,
21+
you work through chapter :math:`n`. The notes always the starting point for your work.
22+
Each chapter will introduce new concepts in programming, often tied back to
23+
related mathematical concepts, and always illustrated by practical code
24+
examples. Python has excellent `official online documentation
25+
<https://docs.python.org/3/>`_, and we link to that throughout the text.
26+
External links show up in orange while :ref:`internal links to other parts of
27+
the notes <introduction>` are red. Sometimes we introduce counterexamples:
28+
illustrations of code errors or bad implementation ideas. These will be flagged
29+
with a big red cross:
30+
31+
.. container:: badcode
32+
33+
.. code-block:: python3
34+
35+
print "Hello World"
36+
37+
Conversely, if it's necessary in context to highlight which approach is the
38+
correct one, the code will come with a big green tick:
39+
40+
.. container:: goodcode
41+
42+
.. code-block:: python3
43+
44+
print("Hello World")
45+
46+
At the end of each chapter there is a glossary containing key new concepts
47+
introduced in that chapter. Always check the glossary and convince yourself that
48+
you understand all the terms introduced there, this is a good check on whether
49+
you have understood the chapter as a whole. However, there is more to learning
50+
new concepts than just the vocabulary, so don't be tempted to skip reading the
51+
chapter and just jump to the glossary!
52+
53+
The chapters broadly alternate between introducing new programming concepts,
54+
such as :ref:`objects <objects>`, :ref:`abstract data types
55+
<abstract_data_types>`, or :ref:`inheritance <inheritance>`, and
56+
covering various aspects of the craft of programming, such as :ref:`style
57+
<inheritance>` and :ref:`debugging <debugging>`. In this way the course combines specific programming knowledge
58+
with more general coding skills.
59+
60+
The videos
61+
..........
62+
63+
Throughout the notes are links to videos. These aren't typical lecture videos in
64+
that they're not primarily focussed on delivering the new ideas in the course.
65+
In particular, the videos don't set out to duplicate the delivery of the
66+
material in the notes. Instead, the videos focus on putting the concepts into
67+
practice, often by showing live coding sessions. Usually you'll want to watch
68+
the video for a given section *after* reading the corresponding notes.
69+
70+
The quizzes
71+
...........
72+
73+
Towards the end of each chapter is a link back to a quiz on Imperial's
74+
Blackboard system. The quizzes are designed to allow you to convince yourself
75+
that you've understood the material in the chapter. Sometimes they will simply
76+
be multiple choice questions testing your understanding of the material, but
77+
sometimes you will need to open up Python and try things out in order to work
78+
out the right answer. The quizzes do not contribute to your module grade, but how
79+
well you are doing on them is an indication of your progress on the module.
80+
81+
The exercises
82+
.............
83+
84+
As we've already noted, really learning to program better is only achieved by
85+
writing code. The core of each week's activities is therefore to put the new
86+
concepts and programming structures you've learned into practice. The
87+
programming exercises are given at the end of each chapter, just before the
88+
glossary. Each time there will be a skeleton code available from
89+
:ref:`GitHub Classroom <fons:github_classroom_exercise>` which provides the starting
90+
point. Sometimes you might be asked to complete a piece of code while on other
91+
occasions you'll need to write a whole Python module from scratch. Each set of
92+
exercises will come with a matching set of tests. These are small programs which
93+
check whether your code produces the correct responses to a range of inputs.
94+
Tests like this provide immediate feedback and enable you to know how you are
95+
doing without having to wait for code to be submitted and marked.
96+
97+
.. note::
98+
99+
Solutions to exercises will not be issued. The notes and accompanying
100+
example code contain examples of the same programming constructs that the
101+
exercises require you to implement, and the tests provide a mechanism to
102+
know when you have a correct answer. There are also help mechanisms via the
103+
course forum and the lab sessions. Issuing solutions to problems would
104+
simply encourage students to study the solutions rather than write code, and
105+
the only way to learn to program is to write code.
106+
107+
Assessment
108+
..........
109+
110+
The weekly quiz and exercises are not assessable: they are formative activities
111+
designed to help you learn the module. Instead, the module will be assessed by
112+
two controlled programming assessments, effectively programming exams. The first
113+
programming assessment will be held in week 7 and will be worth 20% of the
114+
marks for the course. This provides an opportunity to receive feedback in the
115+
middle of the term, and is a practice for the main exam. The :ref:`course contents for
116+
week 7 <midterm>` will comprise practice programming exercises for this midterm test.
117+
The second programming assessment will be held in the main May exam period and
118+
will count for the other 80% of the course.
119+
120+
The instructions and skeleton code for the programming exercises will be
121+
released at a fixed time on GitHub Classroom, and the code which you have
122+
committed to GitHub by the end of the allotted time will be marked.
123+
124+
The exam questions will be similar to the weekly exercises in the course,
125+
and may include new programmes to write from specification, modifications or
126+
extensions to be made to code which is provided, and debugging exercises in
127+
which defective code is provided which you need to correct. Marks will be
128+
allocated both for the functional correctness of the code written, and for
129+
good style and following Python coding conventions.
130+
131+
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.. _midterm:
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Midterm test preparation

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