64 courses with the subject eCornell CIS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
eCornell CIS301Human-Centered Design Essentials0
Human-computer interfaces have become a part of everyday life, whether we consider technology that we use at home or at work. People rely on technology to help them achieve a goal or solve a problem, and this idea is central to the emerging and rapidly expanding field of human-centered design: Who is using the interface, and for what purpose? How can we help them do that better? Answering these questions should be at the heart of the design process, as technologies are ultimately for people to use, and designers need to make this as intuitive and smooth as possible. Design doesn't happen in a lab; it happens in the world, and gathering information about the users of your product ensures better design. In this course, you will be introduced to human-computer interaction design, use practical methods for applying sound design principles, and execute the entire process. You'll discover the basics of how to identify a human need, how and why you need to keep that need at the center of the design process, uncover what can be measured to improve the design, and ensure that you conduct your research fairly and ethically.
User-centered design does not start with the design, but rather with the users: Who are they? What are their goals? What do they need to achieve those goals? In what context will they be using your design? And how would a designer discover these things from the user base? Answering these questions requires a plan. Interviewing potential users in a controlled, systematic way helps to clarify their expectations and their requirements. Doing so helps to inform your design and make it better; no one wants to invest time, effort, and money into designing a solution no one will use. In this course, you will examine best practices for planning and conducting interviews of potential users so that they yield the most insights. Over the course of the project, you will narrow down the target user group for your design, plan your interview, create meaningful and clear interview questions, and help ensure you can leave the interview sessions with the most useful insights.
After you have conducted interviews with your users, it is time to sift through your notes and transcripts in order to get at the most important insights; this is the data, which will be your guide to what the users actually need as well as what the design requirements ultimately need to be. However, one challenge we face as designers is that the data we have gathered is qualitative and not quantitative, which means that interpreting the data requires us to make associations and inferences as we read through the users' stories, thoughts, and feelings. Fortunately, we have several strategies for translating data into design requirements. In this course, you will practice analyzing data from your user interviews and identify insights that are not always immediately apparent. After you extract insights from your interview data, you will create a fictitious user called a persona that will serve as a model of your users and help guide your design decisions. Finally, you will create design requirements that will help as you move from explaining the current state of your users to imagining a future where they use your design to better meet their needs.
When designing a product, we must always consider the experience that the user will have while interacting with it, and we do this by visually sketching the product and our vision of how a user would interact with it. A pen-and-paper sketch helps us to conceptualize abstract design ideas more effectively, communicate these ideas to others, and get feedback. Regardless of your artistic skills, you can effectively, quickly, and inexpensively communicate design ideas by sketching; it is much faster (and cheaper) to modify or throw away a pen-and-paper sketch than a full prototype. In this course, you will learn numerous techniques to improve your sketching skills in order to more clearly convey your design ideas and improve your design. In your course project, you will sketch many design ideas so that you can explore alternative ways of meeting the user requirements. You will also create a design concept based on one of those sketches, then expand the idea into a full scenario and storyboard of the user completing several tasks. Additionally, you will develop a user interface design concept to better envision how your users will actually interact with your design. Finally, you will update your sketches to better fit your user's mental model, as a designer's concept of the design doesn't always match with how a user thinks about it.
Designers know the power of making improvements based on feedback. As you have seen already, this leads to much stronger designs in the end, but it also helps control costs, as it is far easier (and faster) to make necessary changes to sketches or storyboards than full-featured versions. Eventually, however, your design reaches the point where it needs to be a fully fleshed-out interface with which your users can interact. This is where prototypes come in, to further develop the user interface, examine design decisions and interaction flows, and gather feedback. There are several different considerations and decisions to make when creating prototypes based on what you want to get feedback on, how much you need to develop the interface, and the resources available to you. In this course, you will create three different interactive prototypes for your design concept: a paper prototype, a medium-fidelity software prototype, and a high-fidelity software prototype. You will also make iterative improvements to your prototypes using rapid evaluation methods, relying on different design principles and heuristics. Throughout this course, you will gain valuable experience working with two prototyping software applications (Balsamiq and Figma), and you will continue to iterate with each prototyping technique toward a more developed user interface design.
After interviewing users, gathering requirements, creating a design concept, and creating and iterating interactive prototypes, it is time for the final stage of the human-centered design process: conducting an evaluation of the usability of your design. At this stage, we examine the interaction between the user and the interactive prototype to find out what improvements are needed in order for the design to achieve its goals. We do so by observing users as they interact with the prototype to complete tasks. While we often use a high-fidelity prototype at this stage, we must resist the tendency to consider the design as close to "done," as this stage of the process is still iterative. In order to get useful feedback and make the product better, we must carefully plan and conduct a usability test session, listen to the users, and make changes in response to design flaws that we find. In this course, you will create a usability test plan, including defining its goals and any metrics you will want to use for your usability tests. Based on that, you will create a usability test protocol by choosing tasks and composing task instructions for your users to follow. You will then recruit a participant to run a usability test session with your high-fidelity prototype and form a plan for what changes you will make to improve your design.
When you think about what data analysts and data scientists do on a day-to-day basis, you might have a general understanding of types of conclusions they make, but how do they arrive at those conclusions? The statistical programming language R is widely used in data science; understanding the basics of how it works can help you manipulate and visualize data in a quick, flexible manner, and it may improve your communication with data scientists on your team. In this course, you will explore the basics of statistical programming and develop R skills. As you hone your ability to use commands in R, you will combine those basic skills to complete more complex tasks, such as data manipulation and visualization. Finally, you will examine how to repeat tasks in R, which makes it easier to manipulate large data sets. This course involves many hands-on coding exercises to help you gain confidence in your newfound programming skills. System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.
The real world is extremely complex, and revealing the patterns that underlie these complexities can be challenging. However, unlocking the power of a data set can provide you with remarkable insights and help guide decision-making. This course will prepare you to use summarization and visualization techniques to reveal patterns in real-world data, using examples from a variety of disciplines, including business and medicine. In this course, Professor Basu will guide you as you begin to understand key data collection principles and how to make conclusions from data. Choosing which analyses to use depends on your question, so you will use a framework to help you choose which methods to use with your data. Then, you will use R to perform exploratory data analyses, which will allow you to identify key patterns and trends in a ready-to-analyze data set. You will also learn the importance of quantifying the uncertainty associated with your results, and how to measure variability in your data. This course involves many hands-on coding exercises in R to help you gain confidence in your programming skills. System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course. The following course is required to be completed before taking this course: Exploring Data Sets With R
eCornell CIS447Measuring Relationships and Uncertainty0
In this course, you will explore the steps associated with testing a hypothesis and use a variety of simulation methods to test hypotheses in R; these different methods will allow you to test hypotheses for various possible scenarios. As you perform hypothesis tests, you will discover how to assess the uncertainty associated with your data set and the test. You will also analyze the relationship between two or more variables using linear regression analysis and determine how to assess these relationships with simple diagnostic tools. Throughout this course, you will perform hands-on coding exercises to practice simulations in R, which will help you gain confidence in both your programming and statistical skills. After completing this course, you will be able to test hypotheses that involve two or more variables in a ready-to-analyze data set using simulations in the programming language R. You will also understand the uncertainty associated with your hypothesis tests and how it impacts your conclusions. System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course. The following courses are required to be completed before taking this course: Exploring Data Sets With R Summarizing and Visualizing Data
Data scientists use data collected from the real world to answer questions and solve problems that would otherwise be intractable. But since the world is complex, data collected to describe the world can also be complex, which makes it messy and difficult to work with. To successfully analyze data, data scientists need to spend time cleaning or organizing and manipulating their data to put it into a form that is easier to work with and understand. In this course, you will delve into the world of data cleaning by presenting and manipulating your data with the Tidyverse in R. You will organize data by selecting only the variables you're interested in, creating new groups of data, and summarizing data in a way that makes sense for the questions you're trying to ask. You will also create high-quality plots to quickly summarize complex data. You will become familiar with the concept of tidy data and organize data sets in a way that allows for the most efficient analysis. Finally, you will work with data types of more complexity so that you can answer increasingly difficult questions as you take your new skills into your workplace. You will practice all these skills by working with four real-world, complex data sets. This course involves many hands-on coding exercises that will help you take your programming skills to the next level. System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course. The following courses are required to be completed before taking this course: Exploring Data Sets With R Summarizing and Visualizing Data
eCornell CIS449Classifying Data with Logistic Regression0
In this course, you will examine classification questions then use summarization and visualization to obtain preliminary answers to those questions. While summarizing and visualizing data can be extremely helpful for classification problems, logistic regression is a more rigorous way to answer those questions, so you will practice performing and interpreting logistic regression. You will also use logistic regression to evaluate which variables improve your model, make predictions, and evaluate those predictions. System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.
When you think of what data scientists do on a daily basis, you probably think of working with data. Yet data scientists typically derive insights on behalf of their organization or for a client, so any insights they obtain from their data need to be accessible to a broader audience. Often, that audience is less familiar with or even uncomfortable with data analysis or statistics. For this reason, communication is just as important to a data scientist's success as working with data. In this course, you will explore the process of working with a client to understand their data science needs and provide them with a summary of results. You will examine how to understand their questions and perform exploratory data analysis to begin answering their questions. You will practice using your detailed data analysis to write a report then translate that data analysis for a presentation to a data science client. This course can serve as a template for working with a client from start to finish.
eCornell CIS531Problem-Solving with Machine Learning0
This course begins by helping you reframe real-world problems in terms of supervised machine learning. Through understanding the "ingredients" of a machine learning problem, you will investigate how to implement, evaluate, and improve machine learning algorithms. Ultimately, you will implement the k-Nearest Neighbors (k-NN) algorithm to build a face recognition system. Tools like the NumPy Python library are introduced to assist in simplifying and improving Python code.
eCornell CIS532Estimating Probability Distributions0
In this course, you will use the Maximum Likelihood Estimate (MLE) to approximate distributions from data. Using the Bayes Optimal Classifier, you will learn how the assumptions you make will impact your estimations. You will then learn to apply the Naive Bayes Assumption to estimate probabilities for problems that contain a high number of dimensions. Ultimately, you will apply this understanding to implement the Naive Bayes Classifier in order to build a name classification system. The course Problem-Solving with Machine Learning is required to be completed prior to starting this course.
In this course, you are introduced to and implement the Perceptron algorithm, a linear classifier that was developed at Cornell in 1957. Through the exploration of linear and logistic regression, you will learn to estimate probabilities that remain true to the problem settings. By using gradient descent, we minimize loss functions. Ultimately, you will apply these skills to build a email spam classifier. The courses Problem-Solving with Machine Learning and Estimating Probability Distributions are required to be completed prior to starting this course.
eCornell CIS534Decision Trees and Model Selection0
In this course, you will be introduced to the classification and regression trees (CART) algorithm. By implementing CART, you will build decision trees for a supervised classification problem. Next, you will explore how the hyperparameters of an algorithm can be adjusted and what impact they have on the accuracy of a predictive model. Through this exploration, you will practice selecting an appropriate model for a problem and dataset. You will then load a live dataset, select a model, and train a classifier to make predictions on that data. The courses Problem-Solving with Machine Learning , Estimating Probability Distributions , and Learning with Linear Classifiers are required to be completed prior to starting this course.
eCornell CIS535Debugging and Improving Machine Learning Models0
In this course, you will investigate the underlying mechanics of a machine learning algorithm's prediction accuracy by exploring the bias variance trade-off. You will identify the causes of prediction error by recognizing high bias and variance while learning techniques to reduce the negative impacts these errors have on learning models. Working with ensemble methods, you will implement techniques that improve the results of your predictive models, creating more reliable and efficient algorithms. These courses are required to be completed prior to starting this course: Problem-Solving with Machine Learning Estimating Probability Distributions Learning with Linear Classifiers Decision Trees and Model Selection
In this course, you will explore support-vector machines and use them to find a maximum margin classifier. You will then construct a mental model for how loss functions and regularizes are used to minimize risk and improve generalization of a learning model. Through the use of feature expansion, you will extend the capabilities of linear classifiers to find non-linear classification boundaries. Finally, you will employ kernel machines to train algorithms that can learn in infinite dimensional feature spaces. These courses are required to be completed prior to starting this course: Problem-Solving with Machine Learning Estimating Probability Distributions Learning with Linear Classifiers Decision Trees and Model Selection Debugging and Improving Machine Learning Models
In this course, you will investigate the fundamental components of machine learning that are used to build a neural network. You will then construct a neural network and train it on a simple data set to make predictions on new data. We then look at how a neural network can be adapted for image data by exploring convolutional networks. You will have the opportunity to explore a simple implementation of a convolutional neural network written in PyTorch, a deep learning platform. Finally, you will yet again adapt neural networks, this time for sequential data. Using a deep averaging network, you will implement a neural sequence model that analyzes product reviews to determine consumer sentiment. These courses are required to be completed prior to starting this course: Problem-Solving with Machine Learning Estimating Probability Distributions Learning with Linear Classifiers Decision Trees and Model Selection Debugging and Improving Machine Learning Models Learning with Kernel Machines
eCornell CIS538Generative AI and Transformer Models0
With today's advancements in technology, the realm of generative AI has become more and more prominent. In this course, you will explore the foundation for creating transformer models to generate text and images. You will be guided through each process to generate text using transformers, generate images from images, and generate images from noise. You will be introduced to the building blocks that make up transformers as well as to options for fine-tuning your model to achieve better output results. Through activities and a hands-on project, you'll practice implementing your own generative models and gain the skills and understanding to support your work. The following courses are required to be completed prior to starting this course:
In this course, students go beyond the code to understand what is really important in web development. Through creating a mental model of how web development works, student are exposed to the big picture of how users interact with websites. By building empathy and an understanding of target audience needs, students recognize the benefits of approaching design from the perspective of the user and the importance of accessibility, biases, and cultural sensitivity. Finally, students dive into the inner workings of the web to remove the mystery and build a clear picture of how websites work. Ultimately, the goal of this course is to prepare students to design and build beautiful, functional websites that meet the needs of their target audience.
In this course, students will start by identifying the various types of elements that exist in a web page and the importance of information architecture. They will immediately begin organizing and structuring web page content and then markup the content using HTML. This process will introduce the document object model, which enables students to build a mental model of how web pages are built and how web browsers render those pages. Important facets of writing valid HTML that is also semantically sound are introduced through a number of code writing activities. From single page coding, we zoom out to explore how websites with multiple pages, hyperlinks, and navigation are organized and developed. Students will take these new skills and apply them to building a simple multi-page website with valid code and a functional navigation.
In this course, students will engage in a design-first approach, focusing on understanding the needs of the user and prioritizing designing over coding. By engaging with personas and gaining empathy for a website's audience, students identify the needs of their users and apply visual design principles to make sites accessible. With designs in hand, students will learn to write Cascading Style Sheets (CSS) and create rules to implement their design. Along with writing and implementing CSS code, students will explore validating, troubleshooting, and improving their CSS.
In this course, you will employ visual design practices and principles to create attractive, functional websites styled with CSS. Through a thorough investigation of the CSS box model, you are introduced to concepts such as padding, margin, whitespace, and element sizing. These concepts are then given the context of visual design principles to help you make layout and design choices that are user-centric and functional. You will then practice the layout design process to create sketches and wireframes of your designs. With plans in place, you will then write CSS to implement the design to effectively style and layout HTML elements accurately. Finally, you will go a step further to ensure this design is flexible and responsive on the many devices that could be used to visit your site.
eCornell CIS545Improving User Experience with Interactivity0
In this course, you will explore the potential of interactivity by adding JavaScript to a website. First, you will consider the use of interactivity and identify some common best practices and pitfalls. You will then dive into the JavaScript language, practicing the basics of JS syntax and jQuery. Next, you will discover event-based programming concepts such as event handlers, state, and conditionals. Finally, you will debug both the syntax and logic of your JS code.
In this course, you will build a form to collect data input. This process begins with analyzing how web requests work and identifying the parameters of the hypertext transfer protocol (HTTP). Next, you will design and code a form to collect user data. You'll use advanced topics such as client-side form validation and error messages to improve the user experience and overall validity of collected data. You will then evaluate your form and implementation to ensure the final design is appropriate and functional.
This course provides an introduction to the programming environment and explores the basics of Python. After learning how to run a script, you will work with Python expressions, functions, and variables in interactive mode. By the end of the course, you will be able to write a basic Python script that includes built-in functions and modules.
This course explores Python functions. As you expand your technical vocabulary, you will practice visualizing Python executions. In addition, you will examine the rules for writing functions and recognize a properly formatted specification. You will explore writing simple functions to process text and be able to turn an English description into code. You will also practice testing and debugging code and learn how to interpret error messages.
You will take the concepts you have learned up to this point and use them in a real-world application, creating a program that can convert amounts of currency. You will define and design your own functions, design an interactive script, and test these functions and script in the development of a currency converter. You will also explore how Python can be used to work with web services. This course serves as a capstone experience to two courses: Python Fundamentals and User-Defined Functions in Python .
This course shows you how to move beyond straight line code and write programs that require complex decisions. These might occur within a business workflow or a complex scientific computation. You will write conditional, try-except, for-loop, and while-loop statements, as well as use them to design functions.
This course introduces you to mutable data structures, which are advanced Python types that enable faster updating and search than basic types like ints and strings. These types are necessary for working with large data sets but can be difficult to master. You will explore multiple methods to work with these objects, which include lists, sets, and dictionaries. You will also write expressions and employ extensive use of visualization.
You will begin by examining several types of files and objects. You will then apply the concepts you have learned in the previous courses to solve a real-world business problem: auditing an organization's regulatory compliance. Working with heterogeneous data, you will first read a series of disparate data files and determine how to integrate the data. You will then write a sequence of scripts that pull information from these files and inform the user on whether the organization has fully complied with regulations. This course serves as a capstone experience to five courses: Python Fundamentals User-Defined Functions in Python Developing a Currency Converter Establishing the Control Flow Mastering Data Structures
In this course, you will practice writing simple instructions for a computer. Through the use of a blocks-based programming language, you will implement instructions in code to create programs that complete an action. You will practice using repetition and variables to provide more flexibility and simplicity to your program. In the project, you will build a form that can use variables and basic arithmetic expressions to generate a report in HTML. Through these exercises, you will explore good programming habits and debugging techniques on your way to writing JavaScript syntax.
eCornell CIS562Writing Programs that Make Decisions0
In this course, you will plan and create programs that can respond to interaction in differing ways depending on the user input. Through the use of conditional and logical expressions, you will code programs that can perform more complex actions, such as a web form that displays certain information depending on user input. You will also practice tracing and debugging to identify, distinguish, and resolve logical and syntax errors.
In this course, you will begin by organizing data in the form of indexed and associative arrays. From here, you will begin combining programming constructs in order to perform more complex actions on data through the use of loops and nesting. In order to build more resilient programs, you will practice implementing means of recognizing and recovering from errors in your programs. Finally, you will tie this all together by writing code to create a program based on a specification document that incorporates the constructs and error handling covered throughout the course.
In this course, you will begin using functions to create cleaner and more efficient code. You will begin using built-in functions as well as writing your own functions to accomplish a specific task. In order to find solutions to more complex problems, you will practice breaking down large programming tasks into smaller, well-defined pieces. At the same time you will define and execute plans to build those solutions. You will further practice planning, coding, and debugging as a full end-to-end process in order to strengthen your skills as a novice programmer.
eCornell CIS565Expanding Functionality with Libraries0
In this course, you will begin using libraries to expand the functionality of your programs, such as using an API to dynamically generate content. You will use multidimensional data with a library to simplify the implementation of your programs. Through this work, you will also learn how to evaluate whether a new library will provide you the functionality you need for your specific problem or implementation. Finally, you will use various methods of programming to load data asynchronously.
In this course, you will differentiate JavaScript in the browser from implementation on the local computer. You will begin writing command line scripts to automate tasks and overcome development challenges when working locally. You will also explore working with existing codebases as well as the social and ethical considerations of programming. In the project, you will build a simple progressive web app, extending JavaScript beyond the web.
eCornell CIS571Natural Language Processing Fundamentals0
In this course, you will explore the foundational vocabulary of natural language processing (NLP) and start writing code right away by finding patterns in strings using both simple functions and regular expressions. This will prepare you for an important component of NLP work, which is preprocessing text to reduce the size of the vocabulary being analyzed: The fewer total words that need to be analyzed, the more computationally efficient your work will be. You will then tag sentences so that you will be able to relate keywords to one another. You will also gain extensive hands-on experience writing Python, first by practicing on individual sentences then working up to a larger body of text. Overall, your understanding of and skill in NLP with Python will support you as you continue through your career and meet your goals in this area and beyond.
eCornell CIS572Transforming Text Into Numeric Vectors0
If you want to compare two large bodies of text with each other, you can do that by making comparisons with the text itself: Turn the text into tokens then compare the overlap in tokens. Sometimes, however, you don't just want to know that two texts are different (a binary comparison), but you want to know how different, which is a fuzzy comparison. In this course, you will transform text into numeric vectors, which allows us to perform arithmetic operations on textual information to calculate similarity. This is a classical natural language processing (NLP) technique, and it begins by creating different kinds of vectors. You will create both sparse and dense vectors, and you will compare vectors of different sizes to see how information is captured. Finally, you will measure similarity between document vectors, which is the real power of turning text into vectors. The ability to determine how similar two or more documents are is a common use of NLP, and you will practice this technique through hands-on exercises and projects.
eCornell CIS573Classifying Documents With Supervised Machine Learning0
In this course, you will start to use machine learning methods to further your exploration of document term matrices (DTM). You will use a DTM to create train and test sets with the scikit-learn package in Python an important first step in categorizing different documents. You will also examine different models, determining how to select the most appropriate model for your particular natural language processing task. Finally, after you have chosen a model, trained it, and tested it, you will work with several evaluation metrics to measure how well your model performed. The technical skills and evaluation processes you study in the course will provide valuable experience for the workplace and beyond.
eCornell CIS574Topic Modeling With Unsupervised Machine Learning0
Can a computer tell the difference between an article on jaguar the animal and Jaguar the car? It can if we teach it how. In this course, you will extract key phrases or words from a document, which is a key step in the process of text summarization. Part of what makes natural language processing (NLP) so powerful is that it processes text at scale, when a human would simply take too long to perform the same task given the sheer number of text documents to be read and processed. A classic use of NLP, then, is to summarize long documents, whether they are articles or books, in order to create a more easily readable abstract, or summary. Extracting keywords or key phrases is a first step in this direction, which is where you will start in this course. Once you train a computer what the most important words in a document might be, you have to train it to identify the most important sentences. This is the second step in extracting information from a document to help create an abstract, and you will also perform this step on larger text documents. Finally, you will calculate and interpret similarity metrics to compute the degree of similarity between documents that are possibly related to one another. The techniques you use throughout this course will prove useful in specific situations at work and beyond as you support your team or achieve your personal goals.
eCornell CIS575Clustering Documents With Unsupervised Learning0
In the previous course in the Natural Language Processing With Python certificate program, you calculated similarity scores on different documents to determine whether different texts are related topically to one another. In this course, you will turn your attention to measuring distance. This can be considered the opposite of measuring similarity, as you are measuring the dissimilarity of the documents. The goal is still to discover how alike or unlike various groups of text documents are to one another. At scale, this is a problem you might encounter if you need to group thousands of products together purely by using their product description or if you would like to recommend a movie to someone based on whether they liked a different movie. In this course, you will work with several different data sets and use both hierarchical and k-means clustering to create clusters, and you will practice with several distance measures to analyze document similarity. Finally, you will create visualizations that help to convey similarity in powerful ways so stakeholders can easily understand the key takeaways of any clustering and distance measure that you create.
eCornell CIS576Conducting Semantic and Sentiment Analysis0
We have all been misunderstood when sending a text message or email, as tone often does not translate well in written communication. Similarly, computers can have a hard time discerning the meaning of words if they are being used sarcastically, such as when we say great weather when its raining. If you are automatically processing reviews of your product, a negative review will have many of the same key words as a positive one, so you will need to be able to train a model to distinguish between a good review and a bad review. This is where semantic and sentiment analysis come in. In this course, you will examine many kinds of semantic relationships that words can have (such as hypernyms, hyponyms, or meronyms), which go a long way toward extracting the meaning of documents at scale. You will also implement named entity recognition to identify proper nouns within a document and use several techniques to determine the sentiment of text: Is the tone positive or negative? These invaluable skills can easily turn the tide in a difficult project for your team at work or on the path toward achieving your personal goals.
In this course, you will examine the foundations of ethics in both people and organizations. By acquiring the skills to identify the sources of your own ethics, you will strengthen and clarify your ethical stance in the workplace. Through this lens, you will deploy "micro-ethics" in a decisive, purposeful way to situations you might encounter as a citizen in diverse communities such as teams, professional associations, organizations, or employers. This process will be informed by a survey of the "virtue ethics" framework along with mechanisms that help you handle ethical dilemmas. By the end of this course, you will have the necessary foundation to engage with ethics on a deeper level in your personal and professional contexts.
When a data project leaves your hands, the ethical choices you made will travel with it, and those choices can sometimes lead to significant consequences. In this course, you will apply your knowledge to situations where seemingly small ethical choices made by individuals result in large, "macro-ethics" problems of fairness, justice, privacy, and consent. You will trace the data science lifecycle to anticipate consequences and discuss the importance of transparency and accountability in your work. Finally, you will practice applying moral imagination to a data lifecycle and ecosystem then develop recommendations for monitoring and intervention based on that context. By recognizing the connections between desk-level choices and world-level impacts, you will acquire the skill to move your data science work in a positive direction.
eCornell CIS593Integrating Virtue Ethics Into Data Science Practice0
What can you do to cultivate the right ethical choices? This course addresses this question by delving into the concept of "virtue ethics" and how it is applied in practical situations. You will explore how the virtue ethics framework relates to existing principles, practices, and codes of conduct in data science as well as how it can be used to inform decisions. First, you will develop the skill to analyze your own habits so they support your beliefs about your ethical character. You will then apply these concepts to a work setting, recognizing the guidelines that exist for your professional practice. Finally, you will discover how to navigate common situations where ethics are at odds with your professional goals or client needs, including situations when organizational ethics and individual ethics are unaligned. By integrating these concepts in practical ways, you will be set up with the tools and practices needed for success in your role and beyond.
eCornell CIS594Creating an Ethical Data Science Practice and Workplace0
Managing the dynamic between individual and organizational ethics can feel complicated without the proper tools and foundations. In this course, you will be introduced to the necessary tools to understand and engage with these frequently opposing contexts. You will apply virtue ethics concepts across individual, team, and organizational levels to create an environment that encourages all stakeholders to thrive. You will discover techniques for cultivating habits, reviewing processes for ethical flags, creating low-stakes mechanisms to raise ethical concerns, and building an ethical climate in performance reviews. Finally, you will engage with workplace practices around ethics, identifying strategies for handling situations in which ethics are central, including deploying rewards for ethical practices. By the end of this course, you will have the tools necessary to apply ethical concepts in workplace settings, helping you manage the dynamic between individual and organizational ethics to help every stakeholder succeed.
Nonlinear regression models are essential for capturing complex relationships between predictor and response variables that linear regression cannot adequately describe. In this course, you will engage with the theoretical foundations of these models, gain practical experience in their application, and develop the skills necessary to interpret and evaluate their results. This course is designed to equip you with a comprehensive understanding of nonlinear regression models, with a focus on polynomial regression, splines, and generalized additive models (GAMs).
eCornell CIS602Modeling Interactions Between Predictors0
In this course, you will explore strategies for incorporating categorical predictors in a regression model, including using dummy variables to represent different categories. You will inspect binary and nonbinary categorical variables and discover how to interpret the estimated coefficients of dummy variables. As you progress through the course, you will practice modeling and interpreting interactions between categorical and quantitative predictors in a linear model. Finally, you will focus on defining and implementing decision trees, which are advantageous for capturing complex interactions between predictors that linear models may be unable to capture. By the end of the course, you will be equipped to transform categorical variables into numerical variables, fit regression models with categorical predictors, interpret dummy variable coefficients, and use decision trees for modeling complex relationships between predictors.
eCornell CIS603Foundations of Predictive Modeling0
The goal of this course is to introduce you to the fundamental concepts and techniques used in predictive modeling. Throughout this course, you will evaluate the balance between model flexibility and interpretability, examine how to select the best parameters using cross-validation, and practice building models that generalize well to new data. You will also explore techniques for splitting datasets, selecting tuning parameters, and fitting models using loss functions. By the end of the course, you will have a solid understanding of model flexibility, interpretability, and the bias-variance trade-off, equipping you to effectively build and evaluate predictive models.
When working with real-world datasets, more than a single model may be required to capture the complexity of the data. Ensemble methods prove to be extremely useful with complex datasets by allowing us to combine simpler models to fully grasp the patterns in the data, thereby improving the predictive power of the models. In this course, you'll discover how to use two ensemble methods: random forests and boosted decision trees. You'll practice these ensemble methods with datasets in R and apply the ensemble techniques you've learned to build robust predictive models. You'll practice improving decision tree performance using random forest models and practice interpreting those models. You'll then use another technique and apply boosting to reduce errors and aggregate predictions to decision trees.
With the rapid growth of text data across industries, knowing how to clean and process it is key to extracting valuable insights. This course gives you hands-on experience with text preprocessing, the foundation of any natural language processing (NLP) workflow. You will start the course by using regular expressions to identify and edit patterns in text before tackling tasks like converting text to lowercase, replacing characters, and removing unwanted elements. As you progress, you will handle more advanced tasks such as tokenizing text into words or n-grams and filtering out irrelevant stop words. Finally, you will clean messy text by standardizing variations and using techniques like stemming. By the end of the course, you will be equipped to prepare large text datasets for deeper analysis, paving the way for sentiment analysis and other advanced NLP tasks.
eCornell CIS612Exploring Summarization and Visualization0
Summarizing and visualizing text data is a key skill for professionals looking to uncover meaningful insights from large volumes of information. In this course, you will master the tools and techniques to condense and display text data, making complex patterns easier to interpret. Starting with the tidytext package in R, you will tokenize unstructured text data and convert it into structured data for analysis. You will then summarize word distributions within individual documents and bring them to life with visualizations like word clouds. As you progress, you will explore advanced techniques for summarizing and comparing text across multiple documents, using tools such as document-feature matrices. By the end of the course, you will have the skills to compare word usage across texts and track how language patterns evolve over time, helping you reveal deeper trends in your data.
eCornell CIS613Transforming Text to Numeric Sentiments0
In today's data-driven world, being able to quantify and analyze sentiment in text is a powerful skill for understanding customer feedback, social media trends, and more. This course gives you the expertise to transform text into meaningful sentiment scores using key libraries like AFINN, Bing, and NRC. You will begin by working with these sentiment analysis tools to categorize and quantify emotional tones in documents. From there, you will calculate and visualize sentiment scores using tools like line plots, bar charts, and word clouds. Finally, you will compare sentiment across multiple documents and track changes over time. By the end of the course, you will be ready to interpret and act on sentiment trends in real-world applications, offering valuable insights for business strategies, customer relations, and market analysis.
In this course, you will discover how to work directly with some of today's most powerful large language models (LLMs). You'll start by exploring online LLM-based systems and seeing how they handle tasks ranging from creative text generation to language translation. You'll compare how models from major organizations like OpenAI, Google, and Anthropic differ in their outputs and underlying philosophies. You will then move beyond web interfaces to identify how to find and load various foundation models through the Hugging Face hub. By mastering Python scripts that retrieve and run these models locally, you'll gain deeper control over prompt engineering and understand how different model architectures respond to your requests. Finally, you'll tie all these skills together in hands-on projects where you generate text, analyze tokenization details, and assess outputs from multiple LLMs.
eCornell CIS622Language Models and Next-Word Prediction0
In this course, you will use Python to quantify the next-word predictions of large language models (LLMs) and understand how these models assign probabilities to text. You'll compare raw scores from LLMs, transform them into probabilities, and explore uncertainty measures like entropy. You'll also build n-gram language models, handle unseen words, and interpret log probabilities to avoid numerical underflow. By the end of this course, you will be able to evaluate entire sentences for their likelihood, implement your own model confidence checks, and decide when and how to suggest completions for real-world text applications.
In this course, you will discover how to adapt and refine large language models (LLMs) for tasks beyond their default capabilities by creating curated training sets, tweaking model parameters, and exploring cutting-edge approaches such as preference learning and low-rank adaptation (LoRA). You'll start by fine-tuning a base model using the Hugging Face API and analyzing common optimization strategies, including learning rate selection and gradient-based methods like AdaGrad and ADAM. As you progress, you will evaluate your models with metrics that highlight accuracy, precision, and recall, then you'll extend your techniques to include pairwise preference optimization, which lets you incorporate direct user feedback into model improvements. Along the way, you'll see how instruction-tuned chatbots are built, practice customizing LLM outputs for specific tasks, and examine how to set up robust evaluation loops to measure success. By the end of this course, you'll have a clear blueprint for building and honing specialized models that can handle diverse real-world applications.
In this course, you will analyze how large language models are constructed from diverse text sources and examine the entire model life cycle, from pretraining data collection to generating meaningful outputs. You'll explore how choices about data type, genre, and tokenization affect a model's performance, discovering how to compare real-world corpora such as Wikipedia, Reddit, and GitHub. Through hands-on projects, you will design tokenizers, quantify text characteristics, and apply methods like byte-pair encoding to see how different preprocessing strategies shape model capabilities. You'll also investigate how models interpret context by studying keywords in context (KWIC) views and embedding-based analysis. By the end of this course, you will have a clear understanding of how data selection and processing decisions influence the way LLMs behave, preparing you to evaluate or improve existing models.
In this course, you will investigate the internal workings of transformer-based language models by exploring how embeddings, attention, and model architecture shape textual outputs. You'll begin by building a neural search engine that retrieves documents through vector similarity then move on to extracting token-level representations and visualizing attention patterns across different layers and heads. As you progress, you will analyze how tokens interact with each other in a large language model (LLM), compare encoder-based architecture with decoder-based architectures, and trace how a single word's meaning can shift from input to output. By mastering techniques like plotting similarity matrices and identifying key influencers in the attention process, you'll gain insights enabling you to decode model behaviors and apply advanced strategies for more accurate, context-aware text generation.
eCornell CIS641Developing AI Intuition Using LLMs and Prompt Engineering0
Large language models (LLMs) are changing how we interact with artificial intelligence, and understanding how they work is the first step to making them work for you. This course focuses on hands-on experimentation, introducing you to the nuts and bolts of LLMs and how they process data, handle context, and respond to prompts. You will put key concepts into practice, discovering how to craft thoughtful, targeted interactions with LLMs and building your intuition for how they behave. Through prompt engineering and applied projects, you'll develop the skills to create AI-powered solutions that meet real-world needs. Whether you're new to AI or looking to refine your abilities, this course will help you gain confidence in working with LLM technology.
eCornell CIS642Context Engineering with Retrieval Augmented Generation (RAG)0
LLMs are powerful, but they're limited by their training data and lack access to your organization's private information. In this course, you'll master Retrieval-Augmented Generation (RAG), the industry-standard architecture for giving AI access to proprietary knowledge. You'll also explore advanced techniques, including RAG over relational databases (Text-to-SQL) and GraphRAG, to improve accuracy and reduce errors. Through hands-on projects, you'll develop production-ready RAG applications that ground AI responses in trusted, up-to-date information while dramatically reducing hallucinations.
eCornell CIS643Building AI Agents with Tools, Memory, and Agentic Architecture0
This course bridges the gap between AI that thinks and AI that acts. You'll learn to build AI agents. LLMs equipped with tools, memory, and reasoning capabilities that can execute workflows autonomously. The course covers the core components of agents (the model, system prompt, tools, and memory) and explores practical architectural patterns such as prompt chaining, routing, parallelization, orchestrator worker designs, and reflection loops. You'll also explore how agents communicate with one another through protocols and handoffs, and learn the Model Context Protocol (MCP), which enables you to build and consume standardized tool interfaces. Through progressive projects, you'll develop everything from focused AI workflows to more autonomous agents capable of tackling open-ended objectives.
eCornell CIS644AI Strategy, Governance, and Ethics0
Unlocking the full potential of AI isn't just about technical skills. This course focuses on the strategic, ethical, and organizational dimensions of deploying AI, preparing you to lead AI initiatives that deliver value while managing risk. You'll develop frameworks for evaluating AI opportunities across impact, feasibility, and ethical considerations, and learning how to identify high-ROI projects to avoid costly failures. Through an in-depth case study on AI-assisted hiring, you'll confront the complexities of algorithmic fairness and values alignment firsthand. Finally, you'll address the human side of AI transformation, including workforce deskilling, resistance to change, and the risk of apprenticeship loss. You'll emerge equipped to lead AI adoption in ways that enhance human potential.
