Lecture 2

Prologue; DANL Tools; Building a Website; Markdown

Byeong-Hak Choe
bchoe@geneseo.edu

SUNY Geneseo

January 24, 2025

Prologue

Why Data Analytics?

  • Fill in the gaps left by traditional business and economics classes.
    • Practical skills that will benefit your future career.
    • Neglected skills like how to actually find datasets in the wild and clean them.
  • Data analytics skills are largely distinct from (and complementary to) the core quantitative works familiar to business undergrads.
    • Data visualization, cleaning and wrangling; databases; machine learning; etc.
  • In short, we will cover things that I wish someone had taught me when I was undergraduate.

You, at the end of this course

Why Data Analytics?

  • Data analysts use analytical tools and techniques to extract meaningful insights from data.
    • Skills in data analytics are also useful for business analysts or market analysts.
  • Breau of Labor Statistics forecasts that the projected growth rate of the employment in the industry related to data analytics from 2021 to 2031 is 36%.
    • The average growth rate for all occupations is 5%.

The State of the Art

Generative AI and ChatGPT

Data Science and Big Data Trend

From 2008 to now

Programmers in 2025

The State of the Art

Generative AI and ChatGPT

  • Users around the world have explored how to best utilize GPT for writing essays and programming codes.
  • Is AI a threat to data analytics?
    • Fundamental understanding of the subject matter is still crucial for effectively utilizing AI’s capabilities.
  • If you use Generative AI such as ChatGPT, please try to understand what ChatGPT gives you.
    • Copying and pasting it without any understanding harms your learning opportunity.

DANL Tools

What is Git?

\(\quad\)

  • Git is the most popular version control tool for any software development.
    • It tracks changes in a series of snapshots of the project, allowing developers to revert to previous versions, compare changes, and merge different versions.
    • It is the industry standard and ubiquitous for coding collaboration.

What is GitHub?

  • GitHub is a web-based hosting platform for Git repositories to store, manage, and share code.

  • Out class website is hosted on a GitHub repository.

  • Course contents will be posted not only in Brightspace but also in our GitHub repositories (“repos”) and websites.

  • Github is useful for many reasons, but the main reason is how user friendly it makes uploading and sharing code.

What is GitHub?

What is Python?

  • Python is a versatile programming language known for its simplicity and readability.

  • Python has become a dominant tool in various fields including data analysis, machine learning, and web development.

    • It is widely used among developers, data scientists, and researchers for building applications and performing data-driven tasks.
    • Python is open source and has a vast ecosystem of libraries and frameworks.

What is Jupyter?

  • Jupyter is an open-source integrated development environment (IDE) primarily for Python, though it supports many other languages.
    • An IDE is a software application that provides comprehensive facilities (e.g., text code editor, graphical user interface (GUI)) to users for a programming-related project.
    • Jupyter provides a notebook interface that allows users to write and execute code in a more interactive and visual format.

What is Jupyter Notebook?

  • Jupyter Notebook (*.ipynb) is a user-friendly environment that enhances coding, data analysis, and visualization.
    • It offers a web-based interface that combines live code, equations, visualizations, and narrative text.
    • Jupyter Notebook is widely used for data science, machine learning, and research, enabling easy sharing and collaboration.
  • We will use Google Colab, a free cloud version of Jupyter.

What is RStudio?

  • RStudio is an IDE mainly for R programming.

  • RStudio is a user-friendly interface that makes using R easier and more interactive.

    • It provides a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging, and workspace management.

  • We will use RStudio to manage a personal website, where HTML files are rendered from Quarto Document and Jupyter Notebook.

Installing the Tools

Installing the Tools

R programming

Installing the Tools

R Studio

  • For Mac users, try the following steps:
    1. Run RStudio-*.dmg file.
    2. From the Pop-up menu, click the RStudio icon.
    3. While clicking the RStudio icon, drag it to the Applications directory.

Installing the Tools

RStudio Environment

  • Script Pane is where you write R commands in a script file that you can save.

    • An R script is simply a text file containing R commands.
    • RStudio will color-code different elements of your code to make it easier to read.
  • To open an R script,
    • File \(>\) New File \(>\) R Script
  • To save the R script,
    • File \(>\) Save

Installing the Tools

RStudio Environment

  • Console Pane allows you to interact directly with the R interpreter and type commands where R will immediately execute them.

Installing the Tools

RStudio Environment

  • Environment Pane is where you can see the values of variables, data frames, and other objects that are currently stored in memory.

  • Type below in the Console Pane, and then hit Enter:

a <- 1

Installing the Tools

RStudio Environment

  • Plots Pane contains any graphics that you generate from your R code.

Installing the Tools

R Packages and tidyverse

  • R packages are collections of R functions, compiled code, and data that are combined in a structured format.
  • The tidyverse is a collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures.
    • The tidyverse packages work harmoniously together to make data manipulation, exploration, and visualization more.
    • We will use several R packages from tidyverse throughout the course. (e.g., ggplot2, dplyr, tidyr)

Installing the Tools

Installing R packages with install.packages("packageName")

  • R packages can be easily installed from within R using functions install.packages("packageName").
    • To install the R package tidyverse, type and run the following from R console:
install.packages("tidyverse")
  • While running the above codes, you may encounter the question below from the R Console:
  • Mac: “Do you want to install from sources the packages which need compilation?” from Console Pane.
  • Windows: “Would you like to use a personal library instead?” from Pop-up message.
  • Type no in the R Console, and then hit Enter.

Installing the Tools

Loading R packages with library(packageName)

  • Once installed, a package is loaded into an R session using library(packageName) so that its functions and data can be used.
    • To load the R package tidyverse, type and run the following command from a R script:
library(tidyverse)
df_mpg <- ggplot2::mpg
  • mpg is the data.frame provided by the R package ggplot2, one of the R pakcages in tidyverse.

Installing the Tools

RStudio Options Setting

  • This option menu is found by menus as follows:
    • Tools \(>\) Global Options
  • Check the boxes as in the left.
  • Choose the option Never for Save workspace to .RData on exit:

Installing the Tools

Anaconda

Building a Personal Website on GitHub

Let’s Practice Markdown!

  • Jupyter Notebook, Quarto, and GitHub-based Discussion Boards use markdown as its underlying document syntax.

  • Let’s do Classwork 2.