Student and Group Work

You can access code and reports of students and project groups from their Progress Journals.

Progress Journals

Course Essentials

Week 7 (Dec 15, 2021)

Guest Lecture Tamer Emre Director of Market Operations at EPİAŞ/EXIST

Presentations!

Week 6 (Dec 1, 2021)

Week 5 (Nov 17, 2021)

Guest Lecture Ecem Korkmaz Gelal Co-Founder & CEO at TalentGrid

rvest Mini Tutorial
- Voluntary Exercise: Try the exercises at the end of the rvest mini tutorial.
Joins tutorial
- Mini live lecture
Mini tutorial on pivot longer/wider
Voluntary Exercise data: ATP World Tour 2017 Tennis data (RData) (Source) (Example analysis)
- Voluntary exercise (see the desired outputs on dummy data)
  1. Create a matrix (or data frame) of “top 20” (top winners by quantity) players with the number matches among themselves as the value. Plot a heatmap of this matrix.
  2. Create a matrix (or data frame) of top 20 players with win percentages (rows are winners).
  3. Do the same for top 5 countries.

Week 4 (Nov 3, 2021)

Guest lecture by Kemal Davaslıoğlu, Senior Research Scientist at Intelligent Automation.

This week we will learn about shiny to create interactive dashboards on web browsers (official tutorial). Also see the Shiny Cheatsheet from RStudio. (Bonus: Check https://shinyapps.io to deploy your shiny apps)
Shiny in-class exercise starter code.
Example run code from a Shiny application shiny::runGitHub("BOUN-IE48A/boun-ie48a.github.io",subdir="files/shinyExample/")
Bonus: You may look at shinydashboard for dashboard web apps.
Bonus: You may look at shinyMobile for mobile optimized web apps.

Group Assignment (Deadline Nov 17, 18:30)

Create a Shiny app using the Izmir’s Fish Market data (see previous week’s in-class example). Use your imagination but keep it simple enough. Upload it to shinyapps.io and give a link from your Group Progress Journal.

Week 3 (Oct 20, 2021)

Guest lecture by Gizem Gür, Co-Founder & Senior System Architect of Sufle.

Introduction to ggplot2
- Extra Material: Introduction to ggplot2 with election data
- Extra Material: Introduction to ggplot2 with weather data

In-Class Exercise (Bonus) (Due Date: ~~Oct 20~~ postponed to Oct 25, 2021 21:30) This exercise will provide up to 5% bonus to your final grades. Prepare a report which analyzes Izmir’s Fish Market prices. Your report should not be long but it should tell a good story. You are expected to use your dplyr & ggplot2 skills and present an HTML output generated by RMarkdown. It is highly recommended that you do the Datacamp or relevant reading before the lecture. For more information click here

Download Data Set. Please inspect the data set before you start coding. You will see some titles and explanations after the data (this is the raw export).
You can use read_csv command of the readr package.
Your report should include an introduction with max 2-3 bullet points about your findings (e.g. “Fish prices are most volatile for BARBUN and AHTAPOT and average min-max price spread is 5% of the mid price.”). Support your findings with one, or at most two visualizations. Have a brief conclusion part.
Reports not linked from your Progress Journals will not be graded.
Bonus deadline is strict. But you are welcome to put your analysis to your Progress Journals afterwards. They will be taken into account to determine your letter grades regardless of the bonus.

Netflix Assignment (Deadline Nov 3, 18:30)

You are going to analyze Netflix shows with a given dataset.
Gather Netflix data from this link.
Refer to Yigit Erol’s Medium post and Github repository. It is an RMarkdown project and Rmd files and his analyses might help you.
Prepare your own analysis. It might be about a single genre, distribution and benchmark of genres, ratings, durations, or anything else that you think is creative and analytical. (Pro tip: End your analysis with some show suggestions to make it more interesting.)
Post the RMarkdown HTML output and upload it to your Progress Journal. (You need to give a link on your PJ to your assignment, otherwise it won’t be evaluated.) Add a reference link to Yigit’s work.

Week 2 (Oct 6, 2021)

Introduction to dplyr v1.0.0
- Extra Material: Introduction to dplyr with election data
- Extra Material (in Turkish): R ile Veri Analizi 101

Week 1 (Sep 22, 2021)

Introduction to BDA503
RMarkdown Assignment (Deadline Oct 6, 18:30): Prepare an RMarkdown document. Introduce yourself in one paragraph (Your name surname, your work, your data interests and how you (plan to) use data science skills in your current/future work). Plus, add your Linkedin account link. Watch some UseR-2021 videos (Main Link - Recordings Link) and write one of them down on your RMarkdown document. Find 3 R posts relevant to your interests and describe them. Get the html output and put it in your progress journal repository. Provide link from your Progress Journal page. Click for (Example 1) and Example 2.
Form teams of 4-5 and prepare for major projects (we will discuss in week 2).
GitHub Desktop
Cheat Sheet Heaven
Introduction to R - Brief Presentation
Introduction to R (html | pdf)
R Fundamentals Exercises