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Introduction.html
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<section>
<h1 class="title">Introduction to Data Science using Python</h1>
<h2 class="author">Abhijit Dasgupta, PhD</h2>
<h3 class="date">May 12-14, 2020</h3>
</section>
<section><section id="abhijit-dasgupta" class="title-slide slide level1"><h1>Abhijit Dasgupta</h1></section><section id="background" class="slide level2">
<h2>Background</h2>
<ul>
<li>Ph.D. in biostatistics (University of Washington)</li>
<li>Post-doctoral training at National Cancer Institute</li>
</ul>
</section><section id="experience" class="slide level2">
<h2>Experience</h2>
<ul>
<li>Biostatistician and data scientist</li>
<li>Associated with NIH for over 15 years</li>
<li>Over 15 years experience in Python, 20 years in R</li>
<li>Teaches data science, statistics, programming and data visualization
<ul>
<li>NIH</li>
<li>Georgetown</li>
<li>Other government agencies, corporations</li>
</ul></li>
</ul>
</section></section>
<section><section id="online-workshops-a-brave-new-world" class="title-slide slide level1"><h1>Online workshops<br/>A brave new world</h1></section><section id="structure" class="slide level2">
<h2>Structure</h2>
<p>This workshop will be using hybrid methods</p>
</section><section id="face-to-face" class="slide level2">
<h2>Face-to-face</h2>
<p>We will have face-to-face contact for about 50% of the time, through Zoom</p>
<p>This will include lectures, exercies, Q & A</p>
</section><section id="online-material" class="slide level2">
<h2>Online material</h2>
<ol type="a">
<li>screencasts</li>
<li>slides</li>
<li>videos</li>
<li>progress checks</li>
</ol>
</section><section id="supplemental-material" class="slide level2">
<h2>Supplemental material</h2>
<p>There are PDF versions of Jupyter notebooks in Canvas, with links to actual notebooks</p>
</section></section>
<section><section id="schedule" class="title-slide slide level1"><h1>Schedule</h1></section><section id="day-one" class="slide level2">
<h2>Day One</h2>
<table>
<colgroup>
<col style="width: 5%" />
<col style="width: 13%" />
<col style="width: 21%" />
<col style="width: 60%" />
</colgroup>
<thead>
<tr class="header">
<th>Day</th>
<th>Time</th>
<th>Format</th>
<th>Topics</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Day 1</td>
<td>9am - noon</td>
<td>In-person on Zoom</td>
<td>Why data science in Python?<br />A Python primer for Data Science</td>
</tr>
<tr class="even">
<td></td>
<td>1pm - 2pm</td>
<td>In-person on Zoom</td>
<td>Python tools for data science<br />Data wrangling, cleaning, summarizing and munging</td>
</tr>
<tr class="odd">
<td></td>
<td>2pm-4pm</td>
<td>Asynchronous material</td>
<td>Data munging</td>
</tr>
<tr class="even">
<td></td>
<td>4pm-4:30pm</td>
<td>In-person on Zoom</td>
<td>Q & A</td>
</tr>
<tr class="odd">
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
</section><section id="day-two" class="slide level2">
<h2>Day Two</h2>
<table>
<thead>
<tr class="header">
<th>Day</th>
<th>Time</th>
<th>Format</th>
<th>Topics</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Day 2</td>
<td>9am-10 am</td>
<td>In person using Zoom</td>
<td>Data visualization</td>
</tr>
<tr class="even">
<td></td>
<td>10am-noon</td>
<td>Asynchronous material</td>
<td>Data visualization</td>
</tr>
<tr class="odd">
<td></td>
<td>1pm-2pm</td>
<td>In person using Zoom</td>
<td>Statistical Analysis using Python</td>
</tr>
<tr class="even">
<td></td>
<td>2pm-4pm</td>
<td>Asynchronous material</td>
<td>Statstistical Analysis using Python</td>
</tr>
<tr class="odd">
<td></td>
<td>4pm-4:30pm</td>
<td>In-person using Zoom</td>
<td>Q & A</td>
</tr>
<tr class="even">
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
</tbody>
</table>
</section><section id="day-three" class="slide level2">
<h2>Day Three</h2>
<table>
<colgroup>
<col style="width: 5%" />
<col style="width: 13%" />
<col style="width: 21%" />
<col style="width: 60%" />
</colgroup>
<thead>
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<th>Day</th>
<th>Time</th>
<th>Format</th>
<th>Topics</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>Day 3</td>
<td>9am-10am</td>
<td>In person using Zoom</td>
<td>Data analytics; Machine Learning</td>
</tr>
<tr class="even">
<td></td>
<td>10am-noon</td>
<td>Asynchronous material</td>
<td>Data analytics; Machine Learning</td>
</tr>
<tr class="odd">
<td></td>
<td>1pm-2pm</td>
<td>In person using Zoom</td>
<td>String manipulation; Introduction to bioinformatics</td>
</tr>
<tr class="even">
<td></td>
<td>2pm-3:30pm</td>
<td>Asynchronous material</td>
<td>Introduction to bioinformatics; working with other languages; <br />additional resources</td>
</tr>
<tr class="odd">
<td></td>
<td>3:30pm-4:30pm</td>
<td>In-person using Zoom</td>
<td>Q & A, Miscellaneous topics</td>
</tr>
</tbody>
</table>
</section><section id="adaptability" class="slide level2">
<h2>Adaptability</h2>
<p>There will be some flexibility in materials depending on progress, interests, etc.</p>
<p>Not much though, since we don’t have much time</p>
</section></section>
<section><section id="communications" class="title-slide slide level1"><h1>Communications</h1></section><section id="slack" class="slide level2">
<h2>Slack</h2>
<ul>
<li>I will be available on Slack throughout the 3 days and beyond. Please use for questions, comments, private messages and the like.</li>
<li>I will also send polls for feedback</li>
</ul>
</section><section id="e-mail" class="slide level2">
<h2>E-mail</h2>
<p>Email is less preferred, but is okay for longer form messages or more detailed questions</p>
<p><a href="mailto:[email protected]" class="email">[email protected]</a></p>
</section></section>
<section><section id="introductions" class="title-slide slide level1"><h1>Introductions</h1></section><section id="section" class="slide level2">
<h2></h2>
<ul>
<li>Who are you?</li>
<li>What is your background?</li>
<li>Why are you here?</li>
</ul>
</section></section>
<section><section id="who-is-a-data-scientist" class="title-slide slide level1"><h1>Who is a data scientist?</h1></section><section id="one-definition" class="slide level2">
<h2>One definition</h2>
<p><img data-src="graphs/Data_Science_VD.png" /></p>
<aside class="notes">
<ol type="1">
<li>It’s the confluence of statistics, computer science, domain knowledge</li>
<li>You need all three to make this work</li>
<li>We’ll be talking more about stat/CS than domain expertise here</li>
</ol>
</aside>
</section><section id="unclear-definition" class="slide level2">
<h2>Unclear definition</h2>
<ul>
<li>Statistician</li>
<li>Computer scientist</li>
<li>Database engineer</li>
<li>Software engineer</li>
<li>Data engineer</li>
<li>Mathematician</li>
</ul>
<p>Some of the best ones I know are <br/> neurobiologists and physicists</p>
</section><section id="a-broad-umbrella" class="slide level2">
<h2>A broad umbrella</h2>
<p>Anyone who wants to work with data to solve problems within particular domains</p>
</section></section>
<section><section id="data-science" class="title-slide slide level1"><h1>Data Science</h1></section><section id="what-it-involves" class="slide level2">
<h2>What it involves</h2>
<p><img data-src="graphs/DSPipeline.png" alt="DSPipeline" /></p>
</section><section id="what-it-involves-1" class="slide level2">
<h2>What it involves</h2>
<p><img data-src="graphs/data-science-explore.png" alt="data-science-explore" /></p>
</section><section id="what-it-involves-2" class="slide level2">
<h2>What it involves</h2>
<ol type="1">
<li>Managing and cleaning data</li>
<li><p>Interest in exploring relationships between things, informed by domain knowledge</p></li>
<li>Statistical know-how</li>
<li><p>Computational skills</p></li>
<li><p>Tools</p></li>
</ol>
</section><section id="were-here-for-the-tools" class="slide level2">
<h2>We’re here for the tools</h2>
<p>The main two tools are</p>
<ol type="1">
<li>Python (<a href="https://www.python.org" class="uri">https://www.python.org</a>)</li>
<li>R (<a href="https://www.r-project.org" class="uri">https://www.r-project.org</a>)</li>
</ol>
<p>There is a perpetual flame war between the two camps</p>
<p>That is not important</p>
</section></section>
<section><section id="why-python" class="title-slide slide level1"><h1>Why Python?</h1></section><section id="pros" class="slide level2">
<h2>Pros</h2>
<ol type="1">
<li>Very popular general purpose programming language</li>
<li>Strong ecosystem through packages (over 230K projects)</li>
<li>Succint syntax</li>
<li><p>Reasonably fast while also relatively easy to program</p>
<ul>
<li>Computational time vs Developer time</li>
</ul></li>
<li>Self-documenting</li>
<li>Easier to integrate into production pipelines that already use Python
<ul>
<li>Web frameworks (Django, Flask, …)</li>
<li>Workflow managers (Luigi, …)</li>
</ul></li>
<li><p>Increasingly strong Data Science Stack</p></li>
</ol>
</section><section id="cons" class="slide level2">
<h2>Cons</h2>
<ol type="1">
<li>Not a rich-enough ecosystem for some purposes</li>
<li>More computer science-y, less statistical</li>
<li>Poorer frameworks for display and dissemination of information</li>
</ol>
<p>These are areas where R tends to shine.</p>
</section><section id="python-data-science-stack" class="slide level2">
<h2>Python Data Science stack</h2>
<p>Contributed packages over past 30 years</p>
<ul>
<li>To emulate Matlab
<ul>
<li>Numpy</li>
<li>Scipy</li>
<li>Matplotlib</li>
</ul></li>
<li>To emulate Maple
<ul>
<li>Sympy</li>
</ul></li>
<li>To add statistics/data science
<ul>
<li>Pandas</li>
<li>Various data visualization packages
<ul>
<li>seaborn</li>
<li>plotly</li>
</ul></li>
</ul></li>
</ul>
</section><section id="python-data-science-stack-1" class="slide level2">
<h2>Python Data Science stack</h2>
<ul>
<li>Many more user-contributed packages</li>
<li>The basic philosophy has been to concentrate on a few monolithic comprehensive packages
<ul>
<li>statsmodels (Statistics)</li>
<li>scikit-learn (Machine Learning)</li>
<li>pillow (Image analysis)</li>
<li>nltk (Natural Language Processing)</li>
<li>tensorflow & PyTorch (Deep learning)</li>
<li>PyMC3 (Bayesian learning)</li>
</ul></li>
</ul>
</section><section id="python-as-glue" class="slide level2">
<h2>Python as glue</h2>
<p><img data-src="graphs/r_py_glue.png" /></p>
<ul>
<li>The <code>rpy2</code> Python package is not developed on Windows</li>
<li>The <code>reticulate</code> R package actually works quite well</li>
</ul>
</section><section id="python-as-glue-1" class="slide level2">
<h2>Python as glue</h2>
<ol type="1">
<li>Data I/O
<ul>
<li>We can read data from a variety of formats into Python
<ul>
<li>Some proprietary</li>
<li>R, SAS, Stata, SQL, Parquet, JSON</li>
</ul></li>
</ul></li>
<li>There are ways of running R, SAS, others from within Python</li>
<li>The Jupyter sub-ecosystem allows the same interface for <a href="https://github.com/jupyter/jupyter/wiki/Jupyter-kernels">many languages</a>
<ul>
<li>R, SAS, Julia, Haskell, Javascript</li>
</ul></li>
</ol>
</section></section>
<section><section id="our-path-through-this-workshop" class="title-slide slide level1"><h1>Our path through this workshop</h1></section><section id="outline" class="slide level2">
<h2>Outline</h2>
<ol type="1">
<li>A Python primer to get the basics of the language</li>
<li>Using pandas for data I/O, manipulation, cleaning and munging</li>
<li>Using matplotlib and seaborn for data visualization</li>
<li>Using pandas, scipy and statsmodels for statistics</li>
<li>Using scikit-learn for basic machine learning</li>
<li>Applications
<ul>
<li>General examples</li>
<li>High-level bioinformatics</li>
<li>High-level string manipulation</li>
</ul></li>
<li>Introducing resources for further study</li>
</ol>
</section></section>
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