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<h1 class="title toc-ignore">Introduction to Data Science</h1>
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<p>Welcome to Introduction to Data Science page. This lecture will be given by <a href="https://blog.metu.edu.tr/ttemizel/">Assoc.Prof.Dr.Tugba Taskaya Temizel</a>. The assistant who will be doing the applied portion of the course is <a href="https://mehmetaliakyol.com">Mehmet Ali Akyol</a>.</p>
<p>This page will contain the lab codes for the class as RMarkdown documents. You can go to the <a href="https://github.com/makyol/datascience">Github repository</a> for the full Rmarkdown files to use in your local environment.The entirety of materials will be available through ODTU-Class. This blog only aims to provide a collection of rendered RMarkdown files.</p>
<p>The syllabus for this year’s lecture is as follows:</p>
<ul>
<li>Week 1: Introduction to Data Science: Basic Concepts</li>
<li>Week 2: Platforms & Workbenches</li>
<li>Week 3: Probability & Statistics Review</li>
<li>Week 4: Understanding Data: Exploratory Data Analysis</li>
<li>Week 5: Understanding Data: Descriptive Analytics & Visualization</li>
<li>Week 6: Data Acquisition and Preprocessing: Part I</li>
<li>Week 7: Data Acquisition and Preprocessing: Part II</li>
<li>Week 8: Practical Machine Learning: Part I</li>
<li>Week 9: Practical Machine Learning (ML): Part II</li>
<li>Week 10: Model Evaluation and Performance Metrics</li>
<li>Week 11: Data Storytelling</li>
<li>Week 12: Current Topics in Data Science: Part I – Big Data, ML Advance Topics & Data Mining</li>
<li>Week 13: Current Topics in Data Science: Part II – Prescriptive Analytics & Decision Support Systems</li>
<li>Week 14: Data Driven Organizations, Ethics and Legal Issues in Data Science</li>
</ul>
<p>The contents for the applied portion are as follows:</p>
<ul>
<li><a href="https://metudatascience.github.io/datascience/installation.html">R/RStudio Installation</a></li>
<li><a href="https://metudatascience.github.io/datascience/azure_notebooks.html">Azure Notebooks</a></li>
<li><a href="https://metudatascience.github.io/datascience/intro_to_r.html">Introduction to R</a></li>
<li><a href="https://metudatascience.github.io/datascience/amelia.html">Missing data handling</a></li>
<li><a href="https://metudatascience.github.io/datascience/discretization.html">Dimensionality reduction and discretization</a></li>
<li><a href="https://metudatascience.github.io/datascience/classification.html">Classification</a></li>
<li><a href="https://metudatascience.github.io/datascience/clustering.html">Clustering</a> <!-- * Descriptive statistics and visualization
* Association mining
* Regression and time series
* Classification and model evaluation--></li>
</ul>
<div id="external-resources" class="section level2">
<h2>External Resources</h2>
<ul>
<li><a href="https://www.youtube.com/watch?v=jZ952vChhuI">Quick Introduction to Jupyter Notebooks</a></li>
<li><a href="https://rstudio.com/resources/cheatsheets/">Cheatsheets by RStudio</a></li>
<li><a href="http://faculty.marshall.usc.edu/gareth-james/ISL/">An Introduction to Statistical Learning</a></li>
<li><a href="https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html">Introduction to dplyr</a></li>
<li><a href="https://rmarkdown.rstudio.com/lesson-1.html">Introduction to R Markdown</a></li>
<li><a href="https://ggplot2.tidyverse.org/">ggplot2 Overview</a></li>
<li><a href="https://cran.r-project.org/web/packages/Amelia/Amelia.pdf">Amelia Package Documentation</a></li>
</ul>
</div>
</div>
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