Sphinx-Themes template Release 1 Jan 24, 2019 Section I: 1 Getting Started 1 2 Data, Science, and Design 3 3 Introduction to Data 9 4 Command Line and UNIX 21 5 Introduction to Python 25 6 Exploratory Data Analysis 35 7 Important Libraries 49 8 Mapping Data with Folium 55 9 Exploring Data 63 10 Investigating Stop and Frisk 69 11 Folium and Mapping 77 12 Lab 04: Scraping Reviews 79 13 Web Design 87 14 Flask App 91 15 FLASK App 97 16 Learning Application 99 17 Indices and tables 101 i ii CHAPTER 1 Getting Started Please be sure you’ve downloaded and installed the following programs/applications: • the latest version of Anaconda from here • if Windows, download, install, during installation add to PATH, git for windows here • a text editor such as Sublime Text, or Atom 1 Sphinx-Themes template, Release 1 1.1 Class Schedule Class Number Topic MODULE I 1 Introduction and Overview 2 Pandas and EDA I 3 Pandas and EDA II 4 Plotting and Presentation Graphics MODULE II 5 Linear Regression I: OLS 6 Linear Regression II: Ridge, Lasso, Elastic Net 7 Classification I: Logistic Regression and KNN 8 Classification II: Evaluation Metrics 9 Modeling Practice 10 Webscraping and NLTK I 11 Webscraping and NLTK II MODULE III 12 Additional Models I 13 Additional Models II 14 Decision Trees 15 Forest Models 16 Deployment and Databases I 17 Deployment and Databases II MODULE IV 18 Artifical Intelligence I: Perceptrons and Neural Networks 19 Artificial Intelligence II: Introduction to Deep Learning 20 Final Presentations 1.2 Assignments Number Topic Due 1 Exploratory Data Analysis 6.27 2 Regression 7.9 3 Classification 7.16 4 Final Data Set and EDA 7.25 5 Final Data Set Initial Models 8.8 6 Final Data Set Models II 8.15 7 Final Presentation and Project 8.22 [ ]: 2 Chapter 1. Getting Started CHAPTER 2 Data, Science, and Design INSTRUCTOR: Jacob Frias Koehler, PhD EMAIL: [email protected] LOCATION: University Center 300 TIME: Monday and Wednesday 12:00 - 1:15 pm WEBSITE: https://github.com/jfkoehler/code-toolkit SLACK CHANNEL: https://codeliberalarts.slack.com This course aims to introduce students to the Python computing language. Students will write basic programs with Python, investigate the use of Python to perform data analysis (including machine learning and artificial intelligence), use Python to access and structure information from the web, and also use Python to build and deploy web based applications. The course will teach these skills through three projects 1) analysis of public policing data 2) a sentiment analysis and topic modeling project using news articles and social media data, and 3) design and deployment of a personal web application. No prior experience with the Python computing language is required, however students are expected to own a laptop computer, or check one out from the University and bring it with them to every class. 2.1 Course Requirements • participate in class discussions on readings and computing work ( 20 %) • contribute to slack each week (5 %) • complete weekly assignments (40%) • complete three projects (35%) 3 Sphinx-Themes template, Release 1 2.1.1 Learning Outcomes By successful completion of this course, students will be able to: • Use Python to access, load, and analyze data in a variety of formats • Use API’s to query databases and build datasets • Use Python to implement Standard Machine Learning Algorithms in supervised and unsupervised learning problems • Use Python to model with Artificial Neural Networks • Use Python to build Web Applications • Understand the Object Oriented Programming Style • Discuss historical context for important algorithms and methods in contemporary data science More generally, we hope that students will be able to: 1. use computation as a tool to enhance their liberal arts education—to better analyze, communicate, create and learn. 2. gain a broader understanding of the historical and social factors leading to the rise of coding. 3. work through the social and political implications of/embedded within computational technologies and develop an accompanying ethical framework. 4. think critically about the ways they and others interact with computation including understanding its limits from philosophical, logical, mathematical and public policy perspectives 5. engage in project-based and collaborative learning that utilizes computational/algorithmic thinking. 6. understand the intrinsic relationship between the physical world, analog environments and digital experiences 2.2 Outline This is a starting outline for the course, and is likely to change as we move through the semester. 2.3 Section I: Introduction to Python and Data 2.3.1 WEEK 1 • Introduction to Python: Foundations. data types: int, str, float, boolean 2.3.2 Week 2 • Introduction to Python: Control Flow and Functions • Introduction to Python: Collections 2.3.3 Week 3 • Introduction to Pandas I • Introduction to Pandas II 4 Chapter 2. Data, Science, and Design Sphinx-Themes template, Release 1 2.3.4 Week 4 • Visualization with Bokeh I • Visualization with Bokeh II 2.3.5 Week 5 Exploring Policing Data Project • Mapping Workshop with Folium • Presenting with Code 2.4 Section II: Prediction 2.4.1 Week 6 • Introduction to Machine Learning: Regression • Introduction to Machind Learning: Classification 2.4.2 Week 7 • Machine Learning with Text I • Machine Learning with Text II 2.4.3 Week 8 • Machine Learning with Images I • Machine Learning with Images II 2.4.4 Week 9 • Introduction to Neural Networks I • Introduction to Deep Learning I 2.4.5 Week 10 Predicting the Future • Keras and PyTorch samples 2.4. Section II: Prediction 5 Sphinx-Themes template, Release 1 2.5 Section III: Web Deployment 2.5.1 Week 11 • Intro to Flask I • Intro to Flask II 2.5.2 Week 12 • Advanced Flask • Advanced Flask 2.5.3 Week 13 FINAL PROJECT WORKSHOPS 2.5.4 Week 14 2.5.5 Week 15 PRESENTATIONS 2.6 Resources The university provides many resources to help students achieve academic and artistic excellence. These resources include: - University Libraries: http://library.newschool.edu - University Learning Center: http://www.newschool.edu/ learning-center - University Disabilities Service: www.newschool.edu/student-disability-services/ In keeping with the university’s policy of providing equal access for students with disabilities, any student with a disability who needs academic accommodations is welcome to meet with me privately. All conversations will be kept confidential. Students requesting any accommodations will also need to contact Student Disability Service (SDS). SDS will conduct an intake and, if appropriate, the Director will provide an academic accommodation notification letter for you to bring to me. At that point, I will review the letter with you and discuss these accommodations in relation to this course. Student Ombuds: The Student Ombuds office provides students assistance in resolving conflicts, disputes or com- plaints on an informal basis. This office is independent, neutral, and confidential. 2.6.1 Academic Honesty and Integrity Compromising your academic integrity may lead to serious consequences, including (but not limited to) one or more of the following: failure of the assignment, failure of the course, academic warning, disciplinary probation, suspension from the university, or dismissal from the university. Students are responsible for understanding the University’s policy on academic honesty and integrity and must make use of proper citations of sources for writing papers, creating, presenting, and performing their work, taking exami- nations, and doing research. It is the responsibility of students to learn the procedures specific to their discipline for 6 Chapter 2. Data, Science, and Design Sphinx-Themes template, Release 1 correctly and appropriately differentiating their own work from that of others. The full text of the policy, including adjudication procedures, is found at http://www.newschool.edu/policies/ Resources regarding what plagiarism is and how to avoid it can be found on the Learning Center’s website: http: //www.newschool.edu/university-learning-center/avoiding-plagiarism.pdf Intellectual Property Rights: http://www.newschool.edu/provost/accreditation-policies/ Grade Policies: http://www.newschool.edu/registrar/academic-policies/ 2.6.2 Attendance “Absences may justify some grade reduction and a total of four absences mandate a reduction of one letter grade for the course. More than four absences mandate a failing grade for the course, unless there are extenuating circumstances, such as the following: an extended illness requiring hospitalization or visit to a physician (with documentation); a family emergency, e.g. serious illness (with written explanation); observance of a religious holiday. The attendance and lateness policies are enforced as of the first day of classes for all registered students. If registered during the first week of the add/drop period, the student is responsible for any missed assignments and coursework. For significant lateness, the instructor may consider the tardiness as an absence for the day. Students failing a course due to attendance should consult with an academic advisor to discuss options. Divisional and/or departmental/program policies serve as minimal guidelines, but policies may contain additional elements determined by the faculty member.” Please note: Writing Faculty have a slightly different policy, please check with your chair. Responsibility Students are responsible for all assignments, even if they are absent. Late papers, failure to complete the readings assigned for class discussion, and lack of preparedness for in-class discussions and presentations will jeopardize your successful completion of this course. Participation Class participation is an essential part of class and includes: keeping up with reading, contributing meaningfully to class discussions, active participation in group work, and coming to class regularly and on time. Student Course Ratings During the last two weeks of the semester, students are asked to provide feedback for each of their courses through an online survey.