Courses tagged with "Computer Science" (15)
In this UX capstone course, you’ll conduct a multi-stage user experience project to design a product from scratch, incorporating Design and Research methods within the context of Iterative User-Centered Design. You will employ interviews, inspection methods, and user testing, along with ideation, design, and prototyping methods to gain and communicate valuable insight that can be used to deliver a compelling product. You can propose your own product, or you can choose from a list provided by the instructional team.
This course is part of the User Experience (UX) Research and Design MicroMasters Program offered by MichiganX.
This capstone includes the evaluation of the competencies and performance tasks, which define a successful cybersecurity defense.
This capstone is part of the RITx Cybersecurity MicroMasters Program that is specifically designed to teach cybersecurity techniques and tools needed to effectively defend systems and networks of a corporate environment or enterprise.
In order to qualify for the MicroMasters Credential you will need to successfully earn a Verified Certificate in each of the four RITx Cybersecurity courses as well as pass this final capstone.
The capstone will test knowledge and skills across all 4 courses in the Cybersecurity MicroMasters Program. It will include hands-on lab exercises that build on the assessments in the previous four courses.
Gain essential skills in today’s digital age to store, process and analyse data to inform business decisions.
In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Hadoop, R and MOA (Massive Online Analysis).
Topics covered in this course include:
- cloud-based big data analysis;
- predictive analytics, including probabilistic and statistical models;
- application of large-scale data analysis;
- analysis of problem space and data needs;
- understanding of ethical and social concerns of data mining.
By the end of this course, you will be able to approach large-scale data science problems with creativity and initiative.
The Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.
Working with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.
This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.
This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.
You’ll need to reflect on the nature of your data and identify any social and ethical concerns and identify appropriate ethical frameworks for data management.
By communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written oral presentation of your project design, plan, methodologies, and outcomes.
Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world?
In this course, part of the Data Science MicroMasters program, you will learn a variety of supervised and unsupervised learning algorithms, and the theory behind those algorithms.
Using real-world case studies, you will learn how to classify images, identify salient topics in a corpus of documents, partition people according to personality profiles, and automatically capture the semantic structure of words and use it to categorize documents.
Armed with the knowledge from this course, you will be able to analyze many different types of data and to build descriptive and predictive models.
All programming examples and assignments will be in Python, using Jupyter notebooks.
This course, part of the Software Development MicroMasters Program, introduces how teams design, build, and test multi-version software systems.
You will learn software engineering principles that are applicable to the breadth of large-scale software systems. The course explores topics such as agile development, REST and Async programming, software specification, design, refactoring, information security, and more.
By the end of this course, learners will work in teams, applying an agile software development process to specify, design, and test multiple versions of complex software systems.
Learners who enroll in the Verified track will receive staff grading and increased interaction with the instructor and staff.
In this introductory course, you will learn programming with Java in an easy and interactive way.
You will learn about fundamental data structures, such as lists, stacks, queues and trees, and presents algorithms for inserting, deleting, searching and sorting information on these data structures in an efficient way.
Emphasis is put on immediate feedback and on having a fun experience. Programming knowledge is not only useful to be able to program today’s devices such as computers and smartphones. It also opens the door to computational thinking, i.e. the application of computing techniques to every-day processes.
This course is designed taking into account the subset and recommendations of the College Board in order to prepare learners for the Advanced Placement (AP) Computer Science A exam.
Learn how to create a culture of experimentation, where data is swiftly gathered to assess business value and drive innovation.
In this course, you will learn how to use Object and Service Oriented design principles and your development team to increase system flexibility so you can efficiently run experiments at the technical level and also refine business processes and models.
Good design enables a capability for experimentation that would otherwise be infeasible as it speeds up learning and decreases the development time needed to realize the necessary technical changes to drive the next experiment. This capability produces an increase in optionality and paths for innovation; and so overall increases business value.
The course, part of both the Digital Product Management and Digital Leadership MicroMasters programs, addresses both the digital (technical) and social (people) infrastructures and the essential interfaces between them. Managing these interfaces requires designing varying capacities to transfer, translate or transform the knowledge being used to develop experiments. This course focuses on two aspects of the social infrastructure:
- the capacity of the technical infrastructure to engage user and identify their needs;
- the ability to manage the interfaces between the development team and the technical infrastructure over time.
We will focus on how modular design is essential to project, process, and business model experimentation. Most importantly, you will learn how the synthesis of design, management and experimentation can create real business value.
As Cloud Computing shapes businesses of all sizes, it is vital to understand the technologies behind cloud infrastructure, both public and private.
In this course, part of the Cloud Computing MicroMasters program, you will learn to evaluate and compare cloud systems, technologies and providers. In doing so, you will build an understanding of the concepts of elasticity and availability through cloud orchestration.
Some industry leading cloud platforms will be covered in this class, including: Amazon Web Services, VMware vSphere, Microsoft Azure, Google Cloud, and OpenStack. You will use the built-in tools and management consoles within those platforms to configure and manage the infrastructure.
Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment.
For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to use statistical tools to gain meaningful information from them.
In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets.
In this project course, the final course in the Software Development MicroMasters program, you will learn how to input, manipulate, and return data with a modern web development stack. Using TypeScript and Node, you will manipulate large amounts of information using a domain-specific querying language. Backend, REST, and front-end technologies will be required to successfully complete the project.
In teams, students will work through the project in several sprints. In each sprint, students will produce a deliverable that is evaluated using an automated test suite. The feedback you will receive from this suite will be limited. To succeed at the project you will need to create your own private test suite to further validate each deliverable.
By working through such a large-scale development project, you will learn technical development skills, and gain experience with how teams develop software in the industry.
This is the largest project in the Software Development MicroMasters program. Verified learners will have access to greatly increased staff coaching to help complete the project.
Want to gain software quality skills used in mission critical systems?
Modeling checking, symbolic execution and formal methods are techniques that are used for mission critical systems where human life depends upon the system working correctly.
In this course, part of the Software Testing and Verification MicroMasters program, you will learn how to perform these techniques manually and by using automation tools.
No previous programming knowledge needed. The concepts from this course can be applied to any programming language and testing software. This course will use Java, Java Path Finder and Java Modeling Language, however, for examples and assignments.
How do you protect the critical data that is increasingly being stored in the cloud? Learn how to build a security strategy that keeps data safe and mitigates risk.
In this course, part of the Cloud Computing MicroMasters program, you will be introduced to industry best practices for cloud security and learn how to architect and configure security-related features in a cloud platform. Case studies and government standard documents will be reviewed to help ensure appropriate levels of security are implemented.
You’ll develop the necessary skills to identify possible security issues in the cloud environment. You will also gain experience with tools and techniques that monitor the environment and help prevent security breaches such as monitoring logs and implementing appropriate security policies.
In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).
In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
Have you ever wondered how your favorite mobile applications are developed?
Join us on a gentle journey through the mobile application development landscape, using Android as the platform. Along the way we will learn to use Android Studio, the integrated development environment (IDE) for Android apps. This course is intended for students who have some prior programming experience. The course will introduce you to the basics of the Android platform, Android application components, Activities and their lifecycle, UI design, Multimedia, 2D graphics and networking support in Android.
This course covers the following topics:
- Android platform and the Android Studio IDE
- Android components, Activities and their lifecycle, Intents
- UI Design: Widgets and Layouts, UI Events, Event Listeners
- 2D graphics: Canvas/Drawing using a view
- Multimedia: Audio playback and MediaPlayer
- Network support: AsyncTask, HttpURLConnection
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