library(tidyverse) ## or library(dplyr)
<- tempfile()
tf download.file("https://github.com/berkorbay/datasets/raw/master/stock_fundamentals/fundamentals_20231024.rds",tf)
<- readRDS(tf)
f_df
%>% glimpse() f_df
BDA 503 Fall 2023
Week 7
Guest Lecture: Onur Karadeli - Director of Digital Transformation and Data Analytics at OYAK Indisol
Project presentations!
Week 6
Guest Lecture: Eda Ocak - Partner at ThinkNeuro
This week’s lecture is about introduction to Operations Research and, if time permits, introduction to machine learning. Operations Research is both an historical and an emerging field of AI.
OR Assignment - Examine a Case (Deadline: Jan 4, 23:59)
In this individual assignment you are asked to choose a real life case study solved with Operations Research and briefly describe it with your own words.
- Select a case study from this list or another source.
- Write a descriptive summary of the business case, problem, how it is solved and the benefits
- Give proper reference (include link and title) to the original case study source.
- Some additional sources
Week 5
Guest Lecture: Barbaros Yet - Associate Professor at Middle East Technical University, Graduate School of Informatics
This week’s lecture is more about some intermediate topics about data processing/manipulation. We will mainly learn about joins, long/wide tables. In addition, if time permits, some data parsing from web site sources.
- dplyr joins
- Brief tutorial on pivot_longer/pivot_wider
- (if time permits) rvest
Week 4
Guest Lecture: Burak Yitgin - Business Development Manager and Senior Consultant at APLUS Enerji
This week’s lecture is focused on Shiny, an R package to develop interactive dashboards and web pages. Shiny is also available for Python, so what you learn here is transferrable to Python.
Additional resources
- shinylive
- shinylive R Package
- Mastering Shiny
golem
R package- Engineering Shiny (advanced)
shinydashboard
R package for additional dashboard capabilities.shinyMobile
R package for mobile app-like capabilities.
Build a shiny
app using your proposed data sets in Assignment 1 and deploy it to shinyapps.io. Add a link to your Shiny app in your Progress Journal as In-class assignment 3.
Week 3
Guest Lecture: Ahmet Tunçel - Senior Data Architect Manager at Sky Deutschland GmbH
- Supplementary resources
- Voluntary Self Exercise (YOK Foreign Students by Nationality Data Set)
Update your analysis using both dplyr
and ggplot2
on your proposed data sets in Assignment 1. Open a new .qmd document named inclass2.qmd
and make it visible on your PJ (update _quarto.yml
file). (DO NOT TOUCH inclass1.qmd
)
Global Dietary Database provides a wealth of data regarding nutrition intake of a large number of countries. Your assignment is to prepare a brief exploratory data analysis on Vitamin B12 intake of Former Soviet Union countries using dplyr
and ggplot2
. Relevant subset and explanations are given in the below links.
Week 2
- Introduction to dplyr
- dplyr Lecture Notes
- Supplementary resources
- dplyr Cheat Sheet
- Book: R for Data Science by Hadley Wickham & Garrett Grolemund
- dplyr with Election Data
- Turkish dplyr R resource: Eskişehir R User Group
- Exercise on basic Stock Fundamentals data. Run the following code to get the data.
Prepare three simple but striking analyses using dplyr
on your proposed data sets in Assignment 1. Open a new .qmd document named inclass1.qmd
and make it visible on your PJ (update _quarto.yml
file).
Week 1
- Intro to R Presentation (Archive)
- You can try to code these challenges in R
- Turkish R resource: Eskişehir R User Group
RMarkdown/Quarto Assignment (Deadline Oct 26, 18:30): This is your first assignment.
- Prepare a Quarto (.qmd) 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). Add your Linkedin account link.
- Watch some demo and tutorial videos from Posit Youtube channel playlist and write one of them down on your Quarto document.
- Propose a dataset (provide a link to source) which we should use in the class for demonstration and teaching purposes. Briefly explain the educational value of the dataset.
- Find 3 R posts relevant to your interests and describe them. Get the html output and put it in your progress journal repository.
- Provide a link from your Progress Journal page.
Example Progress Journals from previous year: Berk Özcan - Uğur Özata - Mehmet Kemal Ucuzcu
Week 0
This course benefits from DataCamp for the Classroom program. See details here.
Some light reading (blog posts)
- Instructor’s opinion about GPT use for the classroom
- About recent developments with RStudio (Posit) and R’s future (2022)
- Student projects of previous years (2020)
- How this course is structured in previous years (2018)
- Instructor’s view on R (2017)
This semester course webpage went under a significant refurbishment. Course archive is in another repository.