DANL 200 Lecture 1

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# DANL 200 Lecture 1
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## **DANL 200: Introduction to 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 Homepage: 
  - [https://canvas.geneseo.edu/](https://canvas.geneseo.edu/)
  - [https://bcdanl.github.io](https://bcdanl.github.io)
  
- 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">Course Prerequisites</p>

- Business & Economics Statistics or Equivalent:
  - Economics 205, Geography 278, Mathematics 242, Mathematics 262, Political Science 251, Psychology 250, or Sociology 211.
  
- Programming for Data Analytics:
  - Data Analytics 100.

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# Syllabus

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

- **Practical Data Science with R, 2nd Edition** by Nina Zumel & John Mount (henceforth, ZM)
  
- **R for Data Science** by Hadley Wickham & Garrett Grolemund (henceforth, r4s)
  - A free online version of this book is available at [https://r4ds.had.co.nz/](https://r4ds.had.co.nz/).

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# Syllabus

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

- The goal of this course is to help you unlock the potential of data analysis and improve the ability to transform data into a powerful tool in decision making.

- You will develop foundational data analytics skills to prepare for a career or future learning that involves more advanced topics in data analytics.

- During the course, you will work hands-on with the R programming language and its associated data analysis libraries.

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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.

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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;"> Lecture </th>
   <th style="text-align:left;"> Contents </th>
   <th style="text-align:left;"> HW </th>
  </tr>
 </thead>
<tbody>
  <tr grouplength="4"><td colspan="3" style="border-bottom: 1px solid #00000020;"><strong>Data Exploration</strong></td></tr>
<tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 3-4 </td>
   <td style="text-align:left;"> r4s Ch.3 Data visualization </td>
   <td style="text-align:left;"> 1 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 5-7 </td>
   <td style="text-align:left;"> r4s Ch.5 Data transformation </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 8-9 </td>
   <td style="text-align:left;"> r4s Ch.7 Exploratory data analysis </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 10-11 </td>
   <td style="text-align:left;"> ZM Ch.3 Exploring data </td>
   <td style="text-align:left;"> 2 </td>
  </tr>
</tbody>
</table>

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# 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;"> Lecture </th>
   <th style="text-align:left;"> Contents </th>
   <th style="text-align:left;"> HW </th>
  </tr>
 </thead>
<tbody>
  <tr grouplength="3"><td colspan="3" style="border-bottom: 1px solid #00000020;"><strong>Data Wrangling</strong></td></tr>
<tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 12-14 </td>
   <td style="text-align:left;"> WG  Ch.12 Tidy Data </td>
   <td style="text-align:left;"> 3 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 15-16 </td>
   <td style="text-align:left;"> WG  Ch.13 Relational Data </td>
   <td style="text-align:left;"> 4 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 17-18 </td>
   <td style="text-align:left;"> WG Ch.14-16. Strings, Factors, Dates and Time </td>
   <td style="text-align:left;"> 4 </td>
  </tr>
  <tr grouplength="2"><td colspan="3" style="border-bottom: 1px solid #00000020;"><strong>Machine Learning Analysis</strong></td></tr>
<tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 19 </td>
   <td style="text-align:left;"> ZM Ch.6. Choosing and evaluating models </td>
   <td style="text-align:left;"> 5-6 </td>
  </tr>
  <tr>
   <td style="text-align:left;font-weight: bold;border-right:1px solid;padding-left: 2em;" indentlevel="1"> 20-27 </td>
   <td style="text-align:left;"> ZM Ch.7.1 Linear regression and its applications </td>
   <td style="text-align:left;"> 5-6 </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