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‎_quarto.yml

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style: 'docked'
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background: 'primary'
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contents:
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- section: "**M0. Welcome to Programming in Python for Data Science**"
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contents:
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- text: '0. Welcome!'
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href: modules/index.qmd
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- href: modules/module0/module0-01-programming_in_python_for_data_science.qmd
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- text: '   1.1. Prerequisite confirmation'
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href: modules/module0/module0-02-are_you_ready.qmd
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- section: "**M1. Python & Pandas - An Unexpected Friendship**"
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contents:
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- href: modules/module1/module1-00-module_learning_outcomes.qmd
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- href: modules/module1/module1-01-introduction_to_dataframes.qmd
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- text: '   1.1. Exercises'
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href: modules/module1/module1-02-describing_a_dataframe.qmd
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- href: modules/module1/module1-03-introduction_to_pandas.qmd
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- text: '   2.1. Exercises'
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href: modules/module1/module1-04-definitions.qmd
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- href: modules/module1/module1-07-slicing_with_pandas_using_.loc[].qmd
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- text: '   3.1. Exercises'
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href: modules/module1/module1-08-slicing_and_dicing_practice.qmd
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- href: modules/module1/module1-11-slicing_columns_using_.loc[].qmd
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- text: '   4.1. Exercises'
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href: modules/module1/module1-12-slicing_columns_only.qmd
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- href: modules/module1/module1-13-selecting_using_.loc[].qmd
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- text: '   5.1. Exercises'
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href: modules/module1/module1-14-rearraging_columns_and_rows.qmd
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- href: modules/module1/module1-16-obtaining_dataframe_values.qmd
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- text: '   6.1. Exercises'
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href: modules/module1/module1-17-practicing_selecting_values.qmd
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- href: modules/module1/module1-18-selecting_a_single_column.qmd
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- text: '   7.1. Exercises'
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href: modules/module1/module1-19-practicing_selecting.qmd
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- href: modules/module1/module1-20-slicing_and_selecting_using_df.iloc[].qmd
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- text: '   8.1. Exercises'
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href: modules/module1/module1-21-practicing_slicing_and_selecting_using_index_position.qmd
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- href: modules/module1/module1-24-sorting_dataframes.qmd
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- text: '   9.1. Exercises'
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href: modules/module1/module1-25-practice_sorting.qmd
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- href: modules/module1/module1-26-summary_statistics.qmd
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- text: '   10.1. Exercises'
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href: modules/module1/module1-27-fruit_bowl_statistics.qmd
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- href: modules/module1/module1-29-frequency_tables_and_writing_csvs.qmd
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- text: '   11.1. Exercises'
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href: modules/module1/module1-30-practicing_frequency_tables.qmd
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- href: modules/module1/module1-31-quick_viz_with_altair.qmd
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- text: '   12.1. Exercises'
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href: modules/module1/module1-32-practicing_bar_charts.qmd
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- href: modules/module1/module1-34-what_did_we_just_learn.qmd
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- section: "**Module 6: Functions Fundamentals and Best Practices**"
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contents:
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- href: modules/module6/module6-00-module_learning_outcomes.qmd

‎environment.yaml

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dependencies:
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- python>=3.11,<3.13
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- altair-all=5.5.*
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- pandas=1.5.*
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- pandas=2.2.*
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- scipy
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- matplotlib
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- jupyter

‎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!', 'I have a Python background but have not had very much wrangling experience.'],
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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=121&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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