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‎modules/index.qmd‎

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---
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format: html
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pagetitle: Home
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---
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# Welcome to Programming in Python for Data Science!
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This course is part of the [Key Capabilities for Data Science program](https://extendedlearning.ubc.ca/programs-credentials/key-capabilities-data-science-certificate) and will teach you how to conduct data analysis in Python.
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During the course, you will work with powerful Python packages made for data-science, including Pandas for processing tabular data, Altair for data visualization and NumPy for working with numerical data types.
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You will also learn about iteration, flow control, and the data types relevant to data exploration and analysis.
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You will leave this course capable of processing raw data into a format suitable for analysis, writing your own analysis functions, and deriving data-driven insights via the creation of interactive visualizations and summary tables.
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Course prerequisites: None

‎modules/module0/module0-01-programming_in_python_for_data_science.qmd‎

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---
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format:
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html:
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page-layout: full
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---
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# 1. Programming in Python for Data Science
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::: {.panel-tabset .nav-pills}
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## Video
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<iframe
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class="video"
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src="https://www.youtube.com/embed/grkcTZyb9Tk?start=0&end=219&rel=0"
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title="Module 0 Video - Programming in Python for Data Science"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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## Slides
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<iframe
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class="slide-deck"
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src="slides/module0_01.html"
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></iframe>
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:::

‎modules/module0/module0-02-are_you_ready.qmd‎

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---
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format: html
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---
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<script src='../../src/quiz.js'></script>
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# 1.1. Exercises
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## Are you ready?
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<div id='mcq1'></div>
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<script>
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generateQuiz(
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'mcq1',
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'Question 1',
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'Are you ready to start the course <i>Programming in Python for Data Science</i>?',
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{
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'Yes! Let’s get this party started!': 'We love the enthusiasm! Alright let’s move on to Module 1.',
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'I have a Python background but have not had very much wrangling experience.': 'We recommend taking this course so that you are familiar with <code>Pandas</code> and have the fundamentals master for our more advanced Data Science courses. Please continue to Module 1.',
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'I don’t think this is the right fit for me.': 'That’s ok. If you don’t think this is the right course for you, we hope you find what you are looking for. We still suggest taking a look at what we have to offer in Module 1 before formulating a set opinion.',
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},
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'Yes! Let’s get this party started!',
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);
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</script>

‎modules/module0/slides/module0_01.qmd‎

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---
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format: revealjs
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title: Welcome!
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title-slide-attributes:
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data-notes: |
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Welcome to Programming in Python for Data Science.
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---
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```{python}
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# | echo: false
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%run src/utils.py
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```
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## Course Learning Outcomes
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1. Define tidy data and explain why it is an optimal format for data analysis.
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1. Transform data into the tidy data format using pandas.
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1. Demonstrate fundamental programming concepts such as loops and conditionals.
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1. Understand the key data structures in Python.
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1. Read data into Python data from vanilla (e.g., .csv) and non-standard plain text files, as well as common spreadsheet file types (e.g., .xls).
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1. Construct simple plots using Altair
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1. Manipulate a single data table by:
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7.1 Filtering rows based on a criterion or combination of criteria.
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7.2 Selecting variables.
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7.3 Creating new variables and modifying pre-existing ones.
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7.4 Rearranging the observations or variables by sorting.
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1. Manage and manipulate data with dates and times, missing values and categorical variables as well as renaming dataframe columns.
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1. Produce human-readable code that incorporates best practices of programming and coding style.
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# Let's learn Programming in Python for Data Science
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:::{.notes}
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This course is designed to give you a solid foundation of coding in Python.
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No prior python knowledge is needed for this course.
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:::

‎modules/module1/module1-00-module_learning_outcomes.qmd‎

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---
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format:
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html:
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page-layout: full
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---
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# 0. Module Learning Outcomes
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::: {.panel-tabset .nav-pills}
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## Video
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<iframe
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class="video"
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src="https://www.youtube.com/embed/vW2ZXIfZioE?start=0&end=29&rel=0"
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title="Module 1 Video - Module Learning Outcomes"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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## Slides
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<iframe
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class="slide-deck"
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src="slides/module1_00.html"
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></iframe>
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:::

‎modules/module1/module1-01-introduction_to_dataframes.qmd‎

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---
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format:
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html:
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page-layout: full
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---
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# 1. Introduction to Dataframes
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::: {.panel-tabset .nav-pills}
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## Video
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<iframe
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class="video"
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src="https://www.youtube.com/embed/W88f5DAl9hk?start=0&end=114&rel=0"
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title="Module 1 Video - Introduction to Dataframes"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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## Slides
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<iframe
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class="slide-deck"
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src="slides/module1_01.html"
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></iframe>
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:::

‎modules/module1/module1-02-describing_a_dataframe.qmd‎

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---
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format: html
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---
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<script src='../../src/quiz.js'></script>
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# 1.1. Exercises
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## Describing a Dataframe
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<div id='mcq1'></div>
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<script>
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generateQuiz(
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'mcq1',
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'Question 1',
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'What are dataframes comparable to?',
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{
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'Text Documents': 'Is this an easy way to organize data?',
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'Excel Sheets': 'Good job!',
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'Picture Frames': 'Are we storing pictures in a dataframe?',
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},
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'Excel Sheets',
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);
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</script>
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<div id='mcq2'></div>
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<script>
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generateQuiz(
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'mcq2',
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'Question 2',
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'If a <code>csv</code> file was opened in a plain text editor, what character would be separating the values of each column?',
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{
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'<code>:</code>': 'The "C" in <code>csv</code> does not stand for colon.',
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'<code>;</code>': '<code>csv</code> is an acronym for _______ Separated Values format, so likely not "Semi-Colon".',
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'<code>,</code>': 'Great! You have been paying attention.',
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},
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'<code>,</code>',
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);
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</script>

‎modules/module1/module1-03-introduction_to_pandas.qmd‎

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---
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format:
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html:
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page-layout: full
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---
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# 2. Introduction to Pandas
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::: {.panel-tabset .nav-pills}
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## Video
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<iframe
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class="video"
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src="https://www.youtube.com/embed/W88f5DAl9hk?start=120&end=371&rel=0"
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title="Module 1 Video - Introduction to Pandas"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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## Slides
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<iframe
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class="slide-deck"
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src="slides/module1_03.html"
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></iframe>
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:::

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