This course will be a supervised studio-style class, with the goal of helping students push forward their own design and development practice as such, the course will support students through the process of concept development, design iteration, technical implementation, critique, and refinement. Note: By permission of the instructor with the Registrar. Students will be introduced to key disciplines that provide techniques used for working with small, medium and big data today--classical statistics, contemporary data science, machine learning, and data visualization. Students will complete a total of 12 credits across four courses: +1 877-428-6942 What do data analysts need to know about the data landscape to be effective? We will dive into cleaning and structuring unruly data sets, identify which chart types work best for different types of data, and unpack the tactics behind effective visual communication. Thursday, 6:30 - 7:30 PM, 1 Credit, Prof. Stephen Zweibel (Szweibel@gc.cuny.edu) The course will be taught in Python, primarily using the scikit-learn library. prior to starting this course. First class session: June 1st. This course has a very hands-on approach, and students are expected to engage with exploratory analysis both in the class and out of the class. We'll focus mainly on analysis of text in our coding work; this is the best place to begin to understand the choices we make as researchers and analysts in applied settings. Credit for Non-Collegiate Learning | CUNY School of Professional DATA 78000 - Special Topics: "Large Language Models and ChatGPT". Monday, 4:15 - 6:15 PM, 3 credits, Prof. Lisa Rhody (lrhody@gc.cuny.edu) Interactive Data Visualization is one of the most important forms of communication today allowing users to better engage with data, detect patterns, and quickly gain insight into complicated topics. These developments have also led to the emergence of a number of new research fields in the end of 2000s: social computing, computational social science, digital humanities, cultural analytics, and culturomics. Readings may include: Johanna Drucker, Mark Sample, Zach Whalen, Jason Mittell, Deb Verhoeven,Michael J. Kramer, Stephen Ramsay, Lev Manovich, Julia Flanders, Eric Hoyt, Shane Denson, GiorgiaLupi and Stefanie Posavec, Virginia Kuhn, and Bethany Nowviskie.DATA 78000 - Special Topics: "Introduction to GIS: Methods and Applications" #64009 In person/online dates TBD, Note: DATA 71000 satisfies as a Data Analysis distribution core course. Contribute to the broader conversation about digital practices in academic research; As this course focuses heavily on learning how to make custom charts with D3.js, it assumes that students already have a working familiarity of HTML/CSS and basic JavaScript. Accordingly, students will be introduced to selected concepts from these areas so they understand how visualization interacts with them. Courses | CUNY Graduate Center Google. etc.) You'll learn about: Data types and structures Using data to solve problems How to analyze data Data storytelling with visualizations By the end of this class, students will be able to: The course will also explore fundamental theoretical questions that arise when we attempt to represent social or cultural phenomena as data. Frequently Asked Questions | CUNY Graduate Center Cross-listed with DHUM 73700 CUNY Hunter College | Certificate in Data Analytics Courses Viewing Your Record - CUNY BA 3 CreditsWebsite How can we use big cultural data to question what we know about culture and generate new questions? DATA 71000 satisfies as a Data Analysis distribution core courseNote: This course will be online with synchronous class sessions. DATA 71200 satisfies as a Data Analysis distribution core course. This course is intended for students enrolled in the MS Program in Data Analysis & Visualization. During last 20years data visualization has also become an important part ofcontemporary visual and data cultures, entering the worlds of art, visual communication, interactives and interface design. Throughout the semester, students will work towards creating a portfolio of beautiful and analytically sound data visualizations, while also developing their own iterative design process. Contact. Supplementary information about programming and text analysis will be provided to complete in a self directed way using a free DataCamp account. The course will be structured around four broad concepts that inform data studies: defining data, algorithms, networks, and terms ofservice. $599.00 REGISTER BY CLICKING HERE This certificate is designed to provide an introductory-level understanding of data science and give insights into how the discipline can be used to solve a variety of challenges in business and beyond. has created many new possibilities in many fields including computer science, social science, humanities, business, economics, and medicine. To achieve these goals students will be introduced to the principles of probabilistic reasoning, sampling, experimental design, descriptive statistics and statistical inference. DATA 73000 satisfies as a Data Visualization distribution core courseNote: Hybrid, with option to take purely online. Virtual session dates: 2/2, 2/9, 2/16, 2/23, 3/2, 3/9, 3/16, 3/23. We'll begin with a broader examination of data and society. Course Dates: 6/7, 6/14, 6/21, 7/5, 7/12, 7/19, 7/26, 8/2, 8/9, 8/16. Data Science and Engineering - The City College of New York Note: This is a 1-credit summer lab course. Online dates 2/28, 3/7, and 3/14. Apply Now Academic Director, Data Science and Information Systems Arthur O'Connor Build interactive data visualization dashboards that answer a clear and purposeful research question; Choose which chart type works best for different types of data; Iterate with fluidity in Tableau Software leveraging visualization, aesthetic, and user interface best practices; Structure thoughtful critiques and communicate technical questions and solutions; Leverage collaborative tools, including Tableau Public, Wordpress, and repositories of public data sets; Contribute to the broader conversation about digital practices in academic research; Critically read a wide range of chart types with an eye for accuracy, audience, and effectiveness; Identify potential weaknesses in the collection methods and structure of underlying data sets Locate the original source of a visualization and its data. Google Will Serve as First Tech-Anchor Partner for Expanded Cohort of FutureReadyNYC Schools and CUNY Tech Equity. As such, this class will be both technically and conceptually challenging. Learning about data visualization field, becoming familiar with most well-known designers and data artists, classic visualization projects, relevant organizations and available software. Query information from multiple tables, using JOIN and UNION clauses. Tuesday & Thursday, 6:30 - 8:00 PM, 1 Credit. The Fall 2023 Application Application is available. has created many new possibilities in many fields including computer science, social science, humanities, business, economics and medicine. What new theoretical concepts do we need to deal with the new scale of born-digital culture? Wednesday, 6:30 - 8:30 PM, 3 Credits, Prof. Rachel Daniell (rdaniell@gradcenter.cuny.edu)DATA 73200 satisfies as a Data Visualization distribution core courseNote: This is a hybrid course. Regardless of academic concentration, students develop a portfolio of interactive and dynamic data visualization dashboards and an interdisciplinary skill set ready to leverage in academic and professional work. This course will offer students the opportunity to develop a professional level data visualization project of their choice. Thursday, 4:15 - 6:15 PM, 3 credits, Prof. Liza Steele (lsteele@jjay.cuny.edu) It is strongly recommended that students complete Interactive Data Visualization prior to taking this course, or have comparable experience with Javascript, HTML, and CSS. Topics covered include: HTML/CSS/Javascript, interactivity, APIs, data visualization, and the web as a system. Data Analytics Certificate & Training - Grow with Google This class will look at open source tools for creating custom web maps with html, css, and javascript. The arrival of social media and the gradual move of knowledge and media distribution and cultural communication to digital networks in the early 21st century has created a new digital landscape which challenges our existing methods for the study of and assumptions about culture. Tuesday 6:30-8:30pm, 3 Credits, Prof. Omar Nema (omarwnema@gmail.com) Cross-listed with DHUM 73700 #64164Website The course will begin with a lesson on the fundamentals of mapmaking, which includes a 101 on mapping concepts and an overview of mapping ethics. Tuesday, 6:30 - 8:30 PM, 3 Credits. We will discuss the concepts behind data collection, organization, analysis, and publication. You will have the opportunity to receive a Data Analytics and Visualization Training Certificate after completing Accentures North America Virtual Experience, where you will work on real-life projects that require . Remember to specify the product, the product version, and the platform that you desire. Structure thoughtful critiques and communicate technical questions and solutions; Leverage collaborative tools, including Tableau Public, Wordpress, and repositories of public data sets; This course will introduce students to the tools, skills, and concepts necessary for making state-of-the-art interactive data visualizations. prior to starting this course. This course will offer students the opportunity to develop a professional level data visualization project of their choice. We will discuss the concepts behind data collection, organization, analysis, visualization, and publication. Career Planning and Professional Development. THE PROGRAM. Thursday, 6:30 - 7:30 PM, 1 Credit. In person/online dates TBD The courses's main text will be the O'Reilly book "Introduction to Machine Learning with Python" by Sarah Guido and Andreas C. Mller, along with the book's corresponding Jupyter notebooks. This course will introduce students to the tools, skills, and concepts necessary for making state-of-the-art interactive data visualizations. The expectation is that students are motivated and prepared to develop their own project and goals. After that, we will introduce Python for Text Analysis with the NLTK library and for Data Analysis with the Pandas library. The supervised methods will focus primarily on "classic" machine learning techniques where features are designed rather than learned, although we briefly look at recent deep learning models with neural networks. Emphasis is placed on digital mapping technologies, including online and offline computer based geographic information science tools. Certification in Business Data Analytics (CBDA) is the first data analytics certification provided by the International Institute of Business Analysis (IIBA) to recognize one's ability to effectively analyze work in business analytics initiatives. Topics covered include: HTML/CSS/Javascript, interactivity, and APIs. Build interactive data visualization dashboards that answer a clear and purposeful research question; Google Data Analytics Professional Certificate. Students will also need access to a computer that they can install free software on. Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Aucher Serr (aucher.serr@gmail.com) Cross-listed with DHUM 70600 By the end of this course, you will be able to read JavaScript you find online, and adapt it to your needs. Exploring terms such as "non-consumptive" and "black box algorithms," this course takes up the affordances and costs of computationally enabled modeling, representation, querying, and interpretation of texts. This class offers an introduction to website development using HTML, CSS, and JavaScript with a focus on JavaScript. Using Tableau Software, we will build a series of interactive visualizations that combine data and logic with storytelling and design. Well combine a critical sociological view of data practices with examples that illustrate the logic of analysis as an introduction to quantitative research methods. Data is everywhere and the ability to manipulate, visualize, and communicate with data effectively is an essential skill for nearly every sectorpublic, private, academic, and beyond. According to the World Economic Forum, the entire digital universe will soon reach 44 zettabytesthe equivalent of 44 sextillion bytes. Choose which chart type works best for different types of data; News and Events. With an eye towards critical evaluation of both data and method, projects and discussions will be geared towards humanities and social science research. Cross-listed with DHUM 70600Note: This is a 1-credit, 1-hour lab course. Tuesday, 6:30 - 8:30 PM, 3 Credits, Prof. Shipeng Sun (shipeng.sun@hunter.cuny.edu) We will also discuss possibilities, limitations, and implications of using big data-centric methods in social science and humanities research, and the already developed work in computational social science, digital humanities and cultural analytics fields. Interactive Data Visualization is one of the most important forms of communication today allowing users to better engage with data, detect patterns, and quickly gain insight into complicated topics. +1 212-817-7000, Click to expose navigation links on mobile, Collaborative and Interdisciplinary Programs, Career Planning and Professional Development, Student Consumer Information/Right to Know, More in Curriculum and Degree Information, DATA 78000 - Special Topics: Software Design Lab: Creative Computing, DATA 70600 - Special Topics in Computational Fundamentals: JavaScript (6:30 - 7:30 PM). Well begin with a broader examination of data and society. The topics of these workshops will be informed by the tools students need in order to push their work forward. Some of the discussions will use as starting points Manovich's own selected articles and chapters from his booksThe Language of New Media,Software Takes Command,Instagram and Contemporary Image, andCultural Analytics(forthcoming). Living and Learning in NYC. We will also discuss possibilities, limitations, and implications of using big data-centric methods in social science and humanities research, and the already developed work in computational social science, digital humanities and cultural analytics fields. Using Tableau Software, students will build a series of interactive visualizations that combine data and logic with storytelling and design. By the end of this course, you will be able to read JavaScript you find online, and adapt it to your needs. The class is designed for anyone interested in developing a website, or creating an interactive data visualization. The course will be organized according to the stages of the research process as articulated in our fist week reading, to be completed in advance of our first meeting: "How we do things with words: Analyzing text as social and cultural data," which can be downloaded here. Format. The term Public Interest Technology (PIT) is most notably associated with New Americas financial investments in the field; therefore, we will consider the tensions between public and private funding, and their influence in developing technologies for the common good. This course introduces students to fundamental concepts and practical techniques and skills needed to work with data. This course will be a supervised studio-style class, with the goal of helping students push forward their own design and development practice as such, the course will support students through the process of concept development, design iteration, technical implementation, critique, and refinement. By the end of this class, students will be able to: Monday, 6:30 - 8:30 PM, 3 Credits, Prof. Jeremy Porter (jporter@gc.cuny.edu)Note: This course is a recommended elective. Another topic which we will also cover is the use of visualization in recently emerged fields devoted to analyzing big cultural data - digital humanities, computational social science, and cultural analytics. Readings will include articles by Sarah Ahmed, Mary Beard, Meredith Broussard, Lauren Klein, Wendy Chun, Tanya Clement, Miriam Posner, Liz Losh, Tara MacPherson, Johanna Drucker, Andrew Goldstone, Safiya Noble, Bethany Nowviskie, Andrew Piper, Steve Ramsay, Laura Mandell, Susan Brown, Richard Jean So, and Ted Underwood. DATA 74000 satisfies as a Data Studies distribution core courseNote: This course will be online with synchronous class sessions. Note:By Permision of Instructor with the Registrar, ProfessorsYuri Gorokhovich& Elia Machado Emphasis is placed on digital mapping technologies, including online and offline computer based geographic information science tools. For qualitative data, a person with content and thematic analysis experience will be very helpful in identifying themes and commonalities. Online The topics of these workshops will be informed by the tools students need in order to push their work forward. Students maycomplete exploratory projects in ImageJ (Java), Python, and/or R, although no prior expertise isrequired of students. Students will pursue their individual interests while working in the context of a hands-on studio environment where they will interact and share ideas with peers. Data Analytics and Applied Social Research, MA This course has a very hands-on approach, and students are expected to engage with exploratory analysis both in the class and out of the class. Data are everywhere and the ability to manipulate, visualize, and communicate with data effectively is an essential skill for nearly every sectorpublic, private, academic, and beyond. The course will also explore fundamental theoretical questions that arise when we attempt to represent social or cultural phenomena as data. Students will explore various statistical methods and techniques for analyzing data and practice applying these methods to real-world data-driven problems. In person In this course, we explore the social, political, and cultural impact of our societys reliance on massive (and often real-time) data analysis. New York City College of Technology serves the city and the state by providing technically proficient graduates in the technologies of the arts, business, communications . CUNY SPS also awards credit for currently reviewed licenses and certifications in the fields of Health Information Management, IT, Project Management, Real Estate, Business, and Emergency Medical Technician. This course introduces students to the development and use of LLMs in natural language processing (NLP), covering fundamental topics in probability, machine learning, and NLP that make LLMs possible.
امکان ارسال دیدگاه وجود ندارد!