You will have an opportunity to work with common JavaScript libraries/tools. This course will cover both supervised methods (e.g., k-Nearest neighbors, nave Bayes classifiers, decision trees, and support vector machines) and unsupervised methods (e.g., principal component analysis, non-negative matrix factorization, and k-means clustering). After taking this course, students will be able to: Practical topics will include: descriptive and inferential statistical methods, sampling and data collection, and an array of statistical modeling techniques such as correlational analysis, multivariate regression, logistic regression, and exploratory data analysis. etc.) * have a general understanding of how to use quantitative data to research topics in many fields; What data analysis and visualization techniques developed by industry and sciences are most useful for cultural analysis? Students will become familiar with the history and basic concepts of the fundamental paradigms developed by modern societies to analyze patterns in datastatistics, visualization, data mining, and machine learning. DATA 74000 satisfies as a Data Studies distribution core courseNote: This course will be online with synchronous class sessions. The course examines the storage, processing, compilation, and symbolization of spatial data; basic spatial analysis; and visual design principles involved in conveying spatial information. Our graduate offerings allow students to choose from numerous areas of study and advanced training such as Arts, Computer Science, Data Analytics, Education, Library Science, Media, Psychology, Risk Management, and much more. This way, we will look at problems such as: how do we define data and where does it reside, what cultural values are encoded in the algorithms that present data to us, how does data travel unequally in the world, and how does big cultural data impact individuals and society. 3 CreditsWebsite Academics . I will recommend online resources (tutorials and short online classes) suitable for students backgrounds and previous knowledge. This is all to make predictions, and decisions, and derive insights from both structured and unstructured data. 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. In this course, we explore the social, political, and cultural impact of our societys reliance on massive (and often real-time) data analysis. Well begin with a broader examination of data and society. The course will also explore fundamental theoretical questions that arise when we attempt to represent social or cultural phenomena as data. Prof. Jonathan Peters (jonathan.peters@csi.cuny.edu) A portion of the semester will also consist of a series of advanced technical workshops. Practical topics include: descriptive and inferential statistics, sampling, experimental design, statistical models, parametric and non-parametric tests, ordinary least squares regression, logistic regression, and explorative data analysis. Cross-listed with DHUM 71000. DATA 71200 satisfies as a Data Analysis core course. We'll combine a critical view of data with examples that illustrate the logic of analysis. CUNY School of Professional Studies | CUNY SPS No previous programming knowledge is required. By the conclusion of this course, students will be able to: These are typically acceptable to use for applications that ask for an unofficial or student copy of a transcript. Prof. Howard Everson (HEverson@gc.cuny.edu)Website Taking a completely different approach to the topic "methods of text analysis," this course will consider what it means to "analyze" a "text" with computers within a humanistic context, with an emphasis on shaping effective research questions over programming mastery. Our daily existence is increasinglystructured by software systems that process massive amounts of data and generate results such as music and book recommendations,search engines outputs, car routes, airline prices, and advertising content. One-Stop Services. As such, this class will be both technically and conceptually challenging. DATA 71200 satisfies as a Data Analysis distribution core course Prof. Timothy Shortell (dr.timothy@shortell.nyc)DATA 73500 - Working with Data #61122(Note: there are two sections on Monday) An associate's degree requires approximately 60 college credits while a bachelor's typically requires 120 college credits. The overarching objective of this course is to familiarize students with GIS and spatial analysis tools and techniques used in professional and scholarly fields. Tuesday, 6:30 - 8:30 PM, 3 credits, Prof. Ellie Frymire (ellie.frymire@gmail.com) Cross-listed with DHUM 73700Website CUNY offers more than 2,800 top-notch academic programs for degree-seeking students. . Accordingly, students will be introduced to selected concepts from these areas so they understand how visualization interacts with them. Our data sets will be geared towards humanities and social science research, and Tableaus drag-and-drop interface will not require coding. +1 877-428-6942 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. Next, we will cover principles and techniques of inferential statistics. By considering how data visualization mightfruitfully embrace subjective perspectives in order to create meaning, this course will ask students tomore deeply consider how and why we visualize complex data sets, including sets of objects such asliterary corpora, photographs, motion pictures, and music. Hybrid,Room 5383 Particular attention will be focused on working with social network services data, user generated content, and other types of data about societies and individuals that have emerged recently (such as sensor data) and massive media datasets (images, video, text, sound, code, etc.). We will discuss the concepts behind data collection, organization, analysis, visualization, and publication. Wednesday, 6:30 - 8:30 PM Students will explore various statistical methods and techniques for analyzing data and practice applying these methods to real-world data-driven problems. 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. This course combines an introduction to basic cartographic theory and techniques in humanities contexts with practical experience in the analysis, manipulation, and the graphical representation of spatial information. Readings will include selections from such works as Black Software: The Internet & Racial Justice, From the AfroNet to Black Lives Matter (2019) by Charlton McIlwain, Design Justice: Community-Led Practices to Build the Worlds We Need (2020) by Sasha Costanza-Chock, Automating Inequality: How High Tech Tools Profile, Police, and Punish the Poor (2018) by Virginia Eubanks, Artificial Unintelligence (2016) and More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (2023) by Meredith Broussard, Race After Technology (2019) by Ruha Benjamin, and Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition (2021) by Wendy Hui Kong Chun. Applicants must demonstrate skill and experience in: linear algebra, probability theories, discrete math, computer programming languages Application deadline: June 1 (Fall admission only) 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. We will also be referencing "The Elements of Statistical Learning" by Trevor Hastie, jerome H. Friedman, Robert and Tibshirani for examining some of the topics in more depth (this book is available for free form the first author's website: https://web.stanford.edu/~hastie/Papers/ESLII.pdf [web.stanford.edu]DATA 73200 - Interactive Data Visualization #61138(Note: two sections offered--same day and time) 12. prior to starting this course. Data Science/Analytics - Zicklin School of Business | Baruch College CUNY Online - The City University of New York $470. Degree Type. * use cartographic theory to interpret, analyze, and critique graphical representations of spatial phenomena; The course is run in a studio format, which means all students are expected to participate in the making, discussing, and critiquing work. MAIN CONTENT. This course will introduce students to the tools, skills, and concepts necessary for making state-of-the-art interactive data visualizations. Over the course of four weeks, we will dive into cleaning and structuring unruly data sets, identifying which chart types work best for different types of data, and unpacking the tactics behind effective visual communication. Data Analytics Certificate & Training - Grow with Google Well begin with a broader examination of data and society. Enrollment Type: Full-Time . Faculty The M.S. To achieve these goals students will be introduced to the principles of probabilistic reasoning, sampling, experimental design, descriptive statistics and statistical inference. 3 letters of recommendation, transcripts from all post-secondary institutions, and a statement of purpose. The course will begin with an overview of regression analyses, including logistic regression, and continues with latent variable analysis and related exploratory data analytic methods. Note:All Spring 2021 courses will be online.DATA 71000 - Data Analysis Methods #64005 The Master of Science in Data Analysis and Visualization offers three areas of study: Data Analysis, Data Visualization, and Data Studies. You will have an opportunity to work with common JavaScript libraries/tools. Cross-listed with DHUM 73700 #64164Website We offer a wide range of elective courses focused on ways to analyze data. 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. Students maycomplete exploratory projects in ImageJ (Java), Python, and/or R, although no prior expertise isrequired of students. The data revolution has transformed the way we understand and interact with the world around us. See program information on your program of interest for more . DATA 74000 satisfies as a Data Studies distribution core course. How can we use big cultural data to question what we know about culture and generate new questions?DATA 78000 - Special Topics: "Alternative Data Cultures" #64008 Topics / Academic Papers as noted. 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. What new theoretical concepts do we need to deal with the new scale of born-digital culture? This certificate will present the basics of quantitative analysis and its increasing use in today's professional landscape. Data Management Staff - The City University of New York This course will examine alternative trajectories of data visualization that lie outside of the traditionalapproaches that aim to represent data as neutrally and naturally as possible. We offer daytime, evening and weekend courses which are affordable and convenient for your lifestyle. Mayor Adams, Chancellor Banks, Chancellor Matos-Rodriguez Announce Living and Learning in NYC. 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. The course will introduce students to advanced data analytic methods and toolkits, including machine learning methods using the scikit learning library, that will equip them with the ability to perform analyses of complex data from business, industry, and the arts and sciences. The topics of these workshops will be informed by the tools students need in order to push their work forward. The course will explore fundamental database technologies and more recent techniques for working with real-time data flows. The Office of Assessment and Planning is available to assist with analyzing both types of data, if needed. Online Choose which chart type works best for different types of 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. How can we use big cultural data to question what we know about culture and generate new questions? Tuesday & Thursday, 4:15 - 5:45 PM, 1 Credit, Prof. Will Field (wfield@gc.cuny.edu) Throughout the course we will explore the intersection of aesthetics, art, and alternative ways ofperforming data to reveal new insights, drawing on surrealist and other avant-garde traditions thatbegin with defamiliarization as a critical practice. This course is intended for students enrolled in the MS Program in Data Analysis & Visualization. By the end of this class, students will be able to: Online Well begin with a broader examination of data and society. Academic Programs. Ultimately, the goal is for each student to finish the semester with a professional level project they feel proud of. Students will be encouraged to use Python and sci-kit learning tools to produce readable and sensible code that will enable others to replicate and extend their analyses. This course introduces students to fundamental concepts and practical techniques and skills needed to work with data using Python and Google's Colab programming environment. Non-Degree and Visiting Students; Certified Business Data Analytics (CBDA) Prep Course - ed2go DATA 71000 satisfies as a Data Analysis distribution core course. Online The course will develop students understanding of the fundamental concepts underlying modern statistics thereby allowing for the analysis of a variety of data types and data sources, as well as gaining insights through the visualization of trends and patterns in data. * understand both the benefits and limitations of using quantitative methods in research; We will also discusspossibilities, limitations, and implications of using big data-centric methods in social science and humanities research, and the alreadydeveloped work in computational social science, digital humanities and cultural analytics fields. Data Analytics Analyzing vast amounts of data is a key part of data science. This course will provide students will skills necessary to apply machine learning techniques to data, and interpret and communicate their results. In-person, June 28th - August 4th 3 Credits 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. Students can enroll in up to three 1-credit lab courses. What is the difference between "text analysis" and "philology"? Practical topics include: descriptive and inferential statistics, ordinary least squares regression, logistic regression, and exploratory data analysis. Introduction to the fundamentals of Geographic Information Systems (GIS) including vector and raster data formats and applicable analytical techniques. The emphasis throughout will be on the development of statistical reasoning, i.e., thinking like a data scientist. Build interactive data visualization dashboards that answer a clear and purposeful research question; , students will begin with basics of working with data"cleaning" data, preparing it for analysis, and working with a variety of data formats. First class session: June 1st. The goal of the course is to provide students with an introduction to basic statistical techniques for analyzing numerical or quantitative data. Advanced Certificate in Disability Services in Higher Education. New York City College of Technology (City Tech) is the designated college of technology of The City University of New York, currently offering both baccalaureate and associate degrees, as well as specialized certificates.

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