class: center, middle, inverse, title-slide .title[ # Introduction to Computing for the Social Sciences ] .author[ ### MACSS 30500 <br /> University of Chicago ] --- class: inverse, middle # Recap of the course --- ## Instructor - Jean Clipperton ## TA - Sarthak Dhanke: sarthakdhanke@uchicago.edu ## You --- ## Course Objectives The goal of this course is to acquire and practice **basic computational skills**. We focus on: learning R and learning practices for reproducible research. * Programming as a means to an end: analyzing social science data * You will learn fundamental computational skills and techniques, and gain the confidence necessary to learn new techniques as you encounter them in your own research --- ## Major Topics Major topics: * Basic R, Git/GitHub, R Markdown * Data Visualization and Data Transformation * Exploratory Data Analysis * Data Wrangling * R Programming: Control Structures, Functions, Data Structures * Debugging and defensive Programming * Reproducible research * Scraping * Text-Analysis --- ## How we did this We started small and progressively built more complex code ``` r print("Hello world!") ``` ``` ## [1] "Hello world!" ``` --- class: inverse, middle # Succeeding in the class --- ## General Expectations/Suggestions * Attend classes and be engaged * Have RStudio up * Complete the readings before coming to class * Please no abuse of emails, texts, social media, etc. during lectures * Collaborate and ask for help * Start early to work on the homework assignments * Understand our [Academic Integrity Policy](https://cfss-macss.netlify.app/syllabus/#chatgptcopilotllmsgenerative-ai) --- class: center, middle, inverse # Greatest hits: recap of our topics! --- ## Basic R, Git/GitHub, R Markdown * Cloning and committing in repos * add - commit - push * Markdown documents vs RMD --- ## Data Visualization and Data Transformation * Merging: joins (left, right, inner) * Mutate: new variables * Summarize: distilling down to summaries * Graphs: bars/columns, scatter (point), line, histogram, (and more!) ... --- ## Exploratory Data Analysis * Highlight interesting components * Reader can't HELP but see the pattern/obviousness --- ## Data Wrangling and Transformations * Factors * Dates * Strings --- ## R Programming: Control Structures, Functions, Data Structures * Command is function(x){code}: reduce constantly retyping * Loops: another way to reuse/move through (especially functions!!) * Multiple options for data structures (e.g. json) --- ## Debugging and defensive Programming * CLEAR COMMENTS * TEST REGULARLY * USE MESSAGES --- ## Reproducible research > ANYONE (with some R knowledge) should be able to rerun your code and get EXACTLY what you get! Everything should be SELF CONTAINED. --- ## Scraping * Using an API: pull data via an interface * Can have wrapper or not * Resulting data may be in different format * Scraping websites * Difficulty varies widely (!!!!) * Note that 'dynamic' pages (with clicking on them) are more difficult than 'static' (we covered dynamic only!) --- ## Sentiment Analysis and Topic Models * (pre-work): cleaning, tidying, etc. * Sentiment: Positive/negative valence * Emotion: specific emotions (e.g. anger, joy, disgust, surprise, trust, ..) * Topic models: looking for collections of words from our documents --- class: center, inverse, middle # ETHICS --- ## Be good, do good: * Truthful falsehoods * Role of data science in society * Some settings for professional ethics * Some principles to guide ethical action * Algorithmic bias * Data and disclosure * Reproducibility * Ethics, collectively * Professional guidelines for ethical conduct --- class: center, inverse, middle # Final Project --- # Final Project * Showcase different skills from the course * BE EXPLICIT in which skills, weeks, topics you are incorporating * This is more about showcasing your skills than a checklist BUT you do need an appropriate degree of challenge and multiple skills can help you level up * POLISH: nice project, good repo, clear comments * This is your SHOWCASE: how can you demonstrate your skills you learned? --- class: center, inverse, middle # WHAT IS NEXT?! --- ## Computational Social Scientist! * Networks! * Machine Learning! * Data Viz! * NLP! * AND MORE! --- # Conclusions and wrapping up!! * THANK YOU SARTHAK! -- * THANK **YOU** VERY MUCH: this has been a great class and it's been so fulfilling to see you all progress in your skills!