Lecture 1

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# Lecture 1
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## **DANL 100: Programming for Data Analytics**
### Byeong-Hak Choe
### August 30, 2022

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# Instructor
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# Instructor
### <p style="color:#00449E"> Current Appointment & Education </p>

- Name: Byeong-Hak Choe.
- Lecturer in Data Analytics at School of Business at SUNY Geneseo.

- Ph.D. in Economics from University of Wyoming.
- M.S. in Economics from Arizona State University.
- M.A. in Economics from SUNY Stony Brook.
- B.A. in Economics & B.S. in Applied Mathematics from Hanyang University at Ansan, South Korea
  - Minor in Business Administration.
  - Concentration in Finance.
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# Instructor
### <p style="color:#00449E"> Data Science and Climate Change </p>

- Choe, B.H., 2021. "Social Media Campaigns, Lobbying and Legislation: Evidence from #climatechange/#globalwarming and Energy Lobbies."

- Question: To what extent do social media campaigns compete with fossil fuel lobbying on climate change legislation?

- Data include:
  - 5.0 million tweets with #climatechange/#globalwarming around the globe;
  - 12.0 million retweets/likes to those tweets;
  - 0.8 million Twitter users who wrote those tweets;
  - 1.4 million Twitter users who retweeted or liked those tweets;
  - 0.3 million US Twitter users with their location at a city level;
  - Firm-level lobbying data (expenses, targeted bills, etc.).

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# Syllabus
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# Syllabus

### <p style="color:#00449E">Email, Class & Office Hours</p>

- Email: [bchoe@geneseo.edu](bchoe@geneseo.edu)

- Class Webpages: 
  - [https://canvas.geneseo.edu/courses/27499](https://canvas.geneseo.edu/courses/27499)
  - [https://bcdanl.github.io](https://bcdanl.github.io)

- Classroom: Welles, Room 24.

- Office: South Hall 117B.
- Office Hours: 
  - Mondays 3:30 PM-5:30 PM.
  - Wednesdays 1:30 PM-3:30 PM.

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# Syllabus

### <p style="color:#00449E">Required Textbooks</p>

- **Python for Everybody** by Charles R. Severance (Henceforth, `py4e`).
  - A free PDF version of this book is available at [https://bcdanl.github.io/severance-py4e.pdf](https://bcdanl.github.io/severance-py4e.pdf).
  - A free ePub version of this book is available at [https://bcdanl.github.io/severance-py4e.epub](https://bcdanl.github.io/severance-py4e.epub).
  - A free HTML version of this book is available at [https://books.trinket.io/pfe/](https://books.trinket.io/pfe/).
  - ISBN-13: 978-1530051120; ISBN-10: 1530051126
- **Python for Data Analysis** by Wes McKinney (Henceforth, `mckinney`).
  - A free HTML version of this book is available at [https://wesmckinney.com/book/](https://wesmckinney.com/book/).
  - ISBN-13: 978-1098104030; ISBN-10: 109810403X

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# Syllabus

### <p style="color:#00449E">Required Textbooks</p>

- **R for Data Science** by Hadley Wickham & Garrett Grolemund (Henceforth, `r4s`).
  - A free HTML version of this book is available at [https://r4ds.had.co.nz](https://r4ds.had.co.nz).
  - ISBN-13: 978-1491910399; ISBN-10: 1491910399

- **Strategic Analytics**: The Insights You Need from Harvard Business Review} by Harvard Business Review, Eric Siegel,  Edward L. Glaeser,  Cassie Kozyrkov, and Thomas H. Davenport (Henceforth, **HBR**).
  - An eTextbook is available at [Amazon.com](https://www.amazon.com/Strategic-Analytics-Insights-Harvard-Business-ebook/dp/B07VB56L6J/ref=tmm_kin_swatch_0?_encoding=UTF8&qid=&sr=).
  - ISBN-13: 978-1633698987; ISBN-10: 163369898X

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# Syllabus

### <p style="color:#00449E">Course Description</p>

- This course introduces the essential general programming concepts and techniques to a data analytics audience without prior programming experience.

- Topics covered include 
  1. introduction to Data Analytics thinking, 
  2. data types such as numbers, characters, and Boolean (or logical values), 
  3. control and programming structures such as loops, conditionals (e.g., if-else), and functions.

- During the course, you will work hands-on with the Python and R programming languages and its associated data analysis libraries such as `pandas` and `tidyverse`.

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# Syllabus

### <p style="color:#00449E">Course Requirements</p>

- Laptop: You should bring your own laptop (**Mac** or **Windows**) to the classroom.

- It is recommended to have 2+ core CPU, 4+ GB RAM, and 500+ GB disk storage in your laptop for this course.
  
- Homework: There will be six homework assignments.

- Exams: There will be midterm and final exams. 
  - The final exam is comprehensive.

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# Syllabus

### <p style="color:#00449E">Course Contents</p>

- There will be tentatively 28 class sessions:
  - 27 lectures;
  - 1 midterm exam.

<table class=" lightable-paper table table-hover table-condensed" style='font-family: "Arial Narrow", arial, helvetica, sans-serif; margin-left: auto; margin-right: auto; font-family: sans-serif, helvetica, arial; margin-left: auto; margin-right: auto;'>
 <thead>
  <tr>
   <th style="text-align:left;"> Weeks </th>
   <th style="text-align:left;"> Python.Programming </th>
   <th style="text-align:left;"> HBR </th>
   <th style="text-align:left;"> HW </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 1 </td>
   <td style="text-align:left;"> Setting up Python, R, &amp; Excel </td>
   <td style="text-align:left;"> Intro </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 2 </td>
   <td style="text-align:left;"> py4e Ch.1-2 </td>
   <td style="text-align:left;"> Ch.1 </td>
   <td style="text-align:left;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 3 </td>
   <td style="text-align:left;"> py4e Ch.3-4 </td>
   <td style="text-align:left;"> Ch.2 </td>
   <td style="text-align:left;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 4 </td>
   <td style="text-align:left;"> py4e Ch.5-6 </td>
   <td style="text-align:left;"> Ch.3 </td>
   <td style="text-align:left;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 5 </td>
   <td style="text-align:left;"> py4e Ch.7-8 </td>
   <td style="text-align:left;"> Ch.4 </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 6 </td>
   <td style="text-align:left;"> py4e Ch.9-10 </td>
   <td style="text-align:left;"> Ch.5 </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
</tbody>
</table>

---
# Syllabus

### <p style="color:#00449E">Course Contents</p>

<table class=" lightable-paper table table-hover table-condensed" style='font-family: "Arial Narrow", arial, helvetica, sans-serif; margin-left: auto; margin-right: auto; font-family: sans-serif, helvetica, arial; margin-left: auto; margin-right: auto;'>
 <thead>
  <tr>
   <th style="text-align:left;"> Weeks </th>
   <th style="text-align:left;"> Python </th>
   <th style="text-align:left;"> HBR </th>
   <th style="text-align:left;"> HW </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 7 </td>
   <td style="text-align:left;"> mckinney Ch.5 </td>
   <td style="text-align:left;"> Ch.6 </td>
   <td style="text-align:left;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 8 </td>
   <td style="text-align:left;"> mckinney Ch.6 </td>
   <td style="text-align:left;"> Ch.7 </td>
   <td style="text-align:left;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 9 </td>
   <td style="text-align:left;"> Midterm Exam </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 10 </td>
   <td style="text-align:left;"> mckinney Ch.9 </td>
   <td style="text-align:left;"> Ch.8 </td>
   <td style="text-align:left;"> 4 </td>
  </tr>
</tbody>
</table>

---
# Syllabus

### <p style="color:#00449E">Course Contents</p>

<table class=" lightable-paper table table-hover table-condensed" style='font-family: "Arial Narrow", arial, helvetica, sans-serif; margin-left: auto; margin-right: auto; font-family: sans-serif, helvetica, arial; margin-left: auto; margin-right: auto;'>
 <thead>
  <tr>
   <th style="text-align:left;"> Weeks </th>
   <th style="text-align:left;"> R </th>
   <th style="text-align:left;"> HBR </th>
   <th style="text-align:left;"> HW </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 11 </td>
   <td style="text-align:left;"> Starting with R </td>
   <td style="text-align:left;"> Ch.9 </td>
   <td style="text-align:left;"> 4 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 12 </td>
   <td style="text-align:left;"> r4s Ch.3 </td>
   <td style="text-align:left;"> Ch.10 </td>
   <td style="text-align:left;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 13 </td>
   <td style="text-align:left;"> r4s Ch.3 </td>
   <td style="text-align:left;"> Ch.11 </td>
   <td style="text-align:left;"> 5 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 14 </td>
   <td style="text-align:left;"> r4s Ch.5 </td>
   <td style="text-align:left;"> Ch.12 </td>
   <td style="text-align:left;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 15 </td>
   <td style="text-align:left;"> r4s Ch.5 </td>
   <td style="text-align:left;"> Ch.13 </td>
   <td style="text-align:left;"> 6 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;"> 16 </td>
   <td style="text-align:left;"> Final Exam </td>
   <td style="text-align:left;">  </td>
   <td style="text-align:left;">  </td>
  </tr>
</tbody>
</table>

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# Syllabus

### <p style="color:#00449E">Grading</p>

- Homework assignments account for 33% of the total percentage grade.

- Exams account for 67% of the total percentage grade.

$$ 
`\begin{align}
&(\text{Total Percentage Grade}) \notag\\
=\; &0.33\times(\text{Total Homework Score}) \,+\, 0.67\times(\text{Total Exam Score}).\notag
\end{align}`
$$

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# Syllabus

### <p style="color:#00449E">Grading</p>

- The lowest homework score will be dropped when calculating the total homework score.

- Each of the five homework accounts for 20% of the total homework score.

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# Syllabus

### <p style="color:#00449E">Grading</p>

- The total exam score is the maximum between the following two average scores:
  1. the simple average of two exam scores;
  2. the weighted average of them with one-fourth weight on the midterm exam score and three-fourth weight on the final exam score.
  
$$
`\begin{align}
&(\text{Total Exam Score}) \notag\\
=\; &\max\,\left\{0.5\times(\text{Midterm Exam Score}) \,+\, 0.5\times(\text{Final Exam Score})\right.,\notag\\ 
&\qquad\;\,\left.0.25\times(\text{Midterm Exam Score}) \,+\, 0.75\times(\text{Final Exam Score})\right\}.\notag
\end{align}`
$$

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# Syllabus

### <p style="color:#00449E">Grading</p>

- Letter grades will be determined by the total percentage grade:
  
$$
`\begin{align*}
\! 100\; \geq\; A\;\;\, \geq \;93>\; A- \geq 90;\\
90 >\; B+ \geq 87\; >\; B\;\;\, \geq \;83 >\; B- \geq 80;\\
80 >\; C+ \geq 77\; >\; C\;\;\, \geq \;73 >\; C- \geq 70;\\
70\; >\; D\;\;\, \geq\; 60 >\; E.\qquad\quad\;
\end{align*}`
$$

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# Syllabus

### <p style="color:#00449E">Make-up exams</p>

-  Make-up exams will not be given unless you have either a medically verified excuse or an absence excused by the University.

- If you cannot take exams because of religious obligations, notify me by email at least two weeks in advance so that an alternative exam time may be set.

- A missed exam without an excused absence earns a grade of zero.

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# Syllabus

### <p style="color:#00449E">Academic Integrity and Plagiarism</p>

-  All homework assignments and exams must be the original work by you.

- Examples of academic dishonesty include:
  - *representing the work, thoughts, and ideas of another person as your own*
  - *allowing others to represent your work, thoughts, or ideas as theirs*, and
  - *being complicit in academic dishonesty by suspecting or knowing of it and not taking action*.

- Geneseo’s Library offers frequent workshops to help you understand how to **paraphrase**, **quote**, and **cite** outside sources properly. 
  - See [https://www.geneseo.edu/library/library-workshops](https://www.geneseo.edu/library/\\library-workshops).

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# Syllabus

### <p style="color:#00449E">Accessibility</p>

-  The Office of Accessibility will coordinate reasonable accommodations for persons with physical, emotional, or cognitive disabilities to ensure equal access to academic programs, activities, and services at Geneseo.

- Please contact me and the Office of Accessibility Services for questions related to access and accommodations.

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# Syllabus

### <p style="color:#00449E">Well-being</p>

- You are strongly encouraged to communicate your needs to faculty and staff and seek support if you are experiencing unmanageable stress or are having difficulties with daily functioning.

- Liz Felski, the School of Business Student Advocate ([felski@geneseo.edu](felski@geneseo.edu), South Hall 303), or the Dean of Students (585-245-5706) can assist and provide direction to appropriate campus resources.

- For more information, see [https://www.geneseo.edu/dean_students](https://www.geneseo.edu/dean_students).

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# Syllabus

### <p style="color:#00449E">Career Design</p>

- To get information about career development, you can visit the Career Development Events Calendar ([https://www.geneseo.edu/career_development/events/calendar](https://www.geneseo.edu/career_development/events/calendar)).

- You can stop by South 112 to get assistance in completing your Handshake Profile [https://app.joinhandshake.com/login](https://app.joinhandshake.com/login).
  - Handshake is ranked #1 by students as the best place to find full-time jobs.
  - 50% of the 2018-2020 graduates received a job or internship offer on Handshake.
  - Handshake is trusted by all 500 of the Fortune 500.

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# Data Science Process
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# Data Science Process

### <p style="color:#00449E">Data Science Process</p>

- Data science is a cross-disciplinary practice that draws on methods from data cleaning, exploratory data analysis, and machine learning analysis.
  
- Data science focuses on implementing data-driven decisions and managing their consequences.

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# Data Science Process

### <p style="color:#00449E">Data science project roles and responsibilities</p>

- **Project sponsor** represents the business interests; champions the project;
- **Client** represents end users' interests;
- **Data scientist** sets and executes analytic strategy; communicates with sponsor and client;
- **Data architect** manages data and data storage and sometimes manages data collection;
- **Operations** manage infrastructure and deploy final project results.

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# Data Science Process

### <p style="color:#00449E">Stages of a data science project</p>

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# Data Science Process

### <p style="color:#00449E">Motivational example of data science project</p>

- Suppose you're interested in how much social media campaigns are effective on climate change legislation.

- The fossil fuel industry may feel that it's losing too much money because of regulations related to climate change and wants to reduce its losses via lobbying.

- To what extent do social media campaigns competite against fossil fuel lobbying on climate change legislation?

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# Social Media Campaigns, Lobbying and Legislation: Evidence from #climatechange/#globalwarming and Energy Lobbies
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# Social Media

### <p style="color:#00449E">The Rise of Social Media</p>

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# Social Media