Courses tagged with "Evaluation" (733)
This course presents an intensive experience during which students build a software system they intend to be secure, and then attempt to show that other students' projects are insecure, by finding flaws in them.
In this introduction to the field of computing security, you will be given an extensive overview of the various branches of computing security. You will learn cybersecurity concepts, issues, and tools that are critical in solving problems in the computing security domain.
You will have opportunities to learn essential techniques in protecting systems and network infrastructures, analyzing and monitoring potential threats and attacks, devising and implementing security solutions for organizations large or small.
This offering is part of the RITx Cybersecurity MicroMasters Program that prepares students to enter and advance in the field of computing security.
Cybersecurity risk management guides a growing number of IT decisions. Cybersecurity risks continue to have critical impacts on overall IT risk modeling, assessment and mitigation.
In this course, you will learn about the general information security risk management framework and its practices and how to identify and model information security risks and apply both qualitative and quantitative risk assessment methods. Understanding this framework will enable you to articulate the business consequences of identified information security risks. These skills are essential for any successful information security professional.
The goal of this course is to teach students the risk management framework with both qualitative and quantitative assessment methods that concentrate on the information security (IS) aspect of IT risks. The relationship between the IT risk and business value will be discussed through several industry case studies.
First, you will learn about the principles of risk management and its three key elements: risk analysis, risk assessment and risk mitigation. You will learn to identify information security related threats, vulnerability, determine the risk level, define controls and safeguards, and conduct cost-benefit analysis or business impact analysis.
Second, we will introduce the qualitative and quantitative frameworks and discuss the differences between these two frameworks. You will learn the details of how to apply these frameworks in assessing information security risk.
Third, we will extend the quantitative framework with data mining and machine learning approaches that are applicable for data-driven risk analytics. You will explore the intersection of information security, big data and artificial intelligence.
Finally, you will analyze a series of extended case studies, which will help you to comprehend and generalize the principles, frameworks and analytical methods in actual examples.
This offering is part of the RITx Cybersecurity MicroMasters Program that prepares students to enter and advance in the field of computing security.
In this course, you will focus on the pathways to cybersecurity career success. You will determine your own incoming skills, talent, and deep interests to apply toward a meaningful and informed exploration of 32 Digital Pathways of Cybersecurity.
You will complete a self-assessment comprised of elements needed to determine essential next steps on your career path.
The Chief Information Security Officer (CISO) in any given organization serves a leadership position, protecting the data and digital systems that a company’s employees as well as its customers depend upon.
This course delves into the role that the CISO plays in cybersecurity operations.
Throughout the lessons, learners will explore answers to the following questions: How does cybersecurity work across industries? What is the professionals' point of view? How do we keep information secure?
Coursework will fully explore the CISO’s view from the top, as well as the position’s toolkit, which includes policy, procedures and practices, technologies, awareness training, and audit. It will also dive into the approaches taken in private industry, government, academia, and the military.
Once heralded as the ultimate vehicle for open communication and self-expression, the internet is rapidly becoming a globally networked surveillance device. Serious threats to national security, combined with the seemingly endless capacity of digital processing and storage, have led to levels of data capture and 24/7 monitoring of individuals’ activity that were unimaginable even a decade ago.
With resistance to such practices rising, this course will equip you to take an active part in the debate. You will gain a broad understanding of the competing tensions of the laws related to national security and personal and commercial privacy in the post-Snowden online environment. You will also grasp the looming consequences of this battle for peace, sovereignty, human rights and the internet itself.
Are you ready to leave the sandbox and go for the real deal? Have you followed EX101x Data Analysis: Take It to the MAX() and EX102x Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?
In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.
For your project, you firstly get one raw dataset, which you will turn into a dashboard, step by step. You will begin with a business question, and then touch upon different business domains, such as revenue management, planning, scheduling, management, investment, etc.
The progress of the project will be gradual – in the first weeks you will be guided by quizzes to make sure that you are on track. You will engage with creating first drafts of your work and discuss them with your peers.
In the final week, you will be in charge. You will get a second dataset and a number of questions to answer, and it is up to you to design and build the perfect dashboard.
This course will allow you to practice the skills you’ve gained in previous Data Analysis XSeries courses and prove you can be an indispensable asset in data-driven organizations.
By completing this project and EX101x and EX102x, you can obtain the XSeries Data Analysis Verified Certificate.
LICENSE
The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-
Struggling with data at work? Wasting valuable time working in multiple spreadsheets to gain an overview of your business? Find it hard to gain sharp insights from piles of data on your desktop?
If you are looking to enhance your efficiency in the office and improve your performance by making sense of data faster and smarter, then this advanced data analysis course is for you.
If you have already sharpened your spreadsheet skills in EX101x Data Analysis: Take It to the MAX(), this course will help you dig deeper. You will learn advanced techniques for robust data analysis in a business environment. This course covers the main tasks required from data analysts today, including importing, summarizing, interpreting, analyzing and visualizing data. It aims to equip you with the tools that will enable you to be an independent data analyst. Most techniques will be taught in Excel with add-ons and free tools available online. We encourage you to use your own data in this course but if not available, the course team can provide.
This course is part of the Data Analysis XSeries.
LICENSE
The course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.
Many people talk about the promise of “big data” to health care. But how can the application of data analytics to big data actually improve health and health care? We will show that novel data analytics based solutions can result in better diagnosis, better care and better curing. This provides fertile ground for entrepreneurship and the development of new businesses.
In our course we’ll start from the very basics of data analytics, look at different real world approaches and help you to see entrepreneurial opportunities and develop a business plan.
We will cover three important fields:
- Health care expertise: We will present medical approaches to data and give an overview of challenges where big data based solutions have been developed to improve the efficiency and effectiveness in medicine.
- Data analytics: We’ll explain the basics of data mining within the context of a wide variety of health care settings, and the types of data and data analysis challenges that you will likely encounter in each. We’ll start with gathering the data, move on to classifying, analyzing and finally visualizing it.
- Entrepreneurship: You will learn how to assess when data sciences based improvements in health care represent entrepreneurial opportunities. The development of a rigorous business plan is used to help you make that assessment.
Participants with prior experience in the medical field will learn how novel data science applications can improve healthcare, create societal value and how to spot entrepreneurial opportunities.
Participants with experience in data science or mathematics will learn about medical approaches to data and why healthcare is an exciting area to apply and develop data analytics.
Participants interested in launching their startup will learn how big data solutions in health care can provide a solid basis to build great ventures.
Whatever your motivation to enrol in this course, we care about your project and your success - that’s why we will guide you through all parts of this learning journey step by step!
Enter now to see how you can engage in data driven innovation and make an impact on improving care, outcomes and the quality of life.
Is your data messy? Do you need to learn how to clean it up? In this computer science course, we will discuss the discipline of Data Quality Assurance and Data Quality Services (DQS). You will learn why your data needs cleansing, the capabilities and features of DQS, what a DQS solution looks likes and how data cleansing integrates with an Integration Services (SSIS) data flow. We will demonstrate a variety of critical data quality activities such as knowledge discovery, domain management, matching policies for the de-duplication of data, and administration topics covering installation, configuration and security.
Apply the learned algorithms and techniques for data mining from the previous courses in the Data Mining Specialization to solve interesting real-world data mining challenges.
This course is part of the Microsoft Professional Program Certificate in Data Science.
Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft.
In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or with R, and Python on Azure stack.
This course is part of the Microsoft Professional Program Certificate in Data Science.
Showcase the knowledge and skills you’ve acquired during the Microsoft Professional Program for Data Science, and solve a real-world data science problem in this program capstone project. The project takes the form of a challenge in which you will explore a dataset and develop a machine learning solution that is tested and scored to determine your grade.
Note: This course assumes you have completed the previous courses in the Microsoft Professional Program for Data Science. For details, go to https://academy.microsoft.com/en-us/professional-program/data-science.
Are you interested in pursuing a degree in Data Science, but unsure whether you have the necessary Math and Programming skills? This assessment will help you identify your current readiness in three core areas required for the study of Data Science; Calculus, Linear Algebra, and Programming.
You can take this assessment at your own pace and receive a private score report that identifies your readiness in each specific area. We will also provide, when necessary, recommendations for additional free online study.
This assessment is free, unproctored, and not offered for credit; it is designed for enrichment and self-assessment for anyone interested in pursuing data science as a career.
Data structures play a central role in computer science and are the cornerstones of efficient algorithms. Knowledge in this area has been at the kernel of related curriculums. This course aims at exploring the principles and methods in the design and implementation of various data structures and providing students with main tools and skills for algorithm design and performance analysis. Topics covered by this course range from fundamental data structures to recent research results.
数据结构是计算机科学的关键内容，也是构建高效算法的必要基础。其覆盖的知识，在相关专业的课程体系中始终处于核心位置。本课程旨在围绕各类数据结构的设计与实现，揭示其中的规律原理与方法技巧；同时针对算法设计及其性能分析，使学生了解并掌握主要的套路与手法。讲授的主题从基础的数据结构，一直延伸至新近的研究成果。
This course is presented in Mandarin.
FAQ
In what language will this course be offered?
Mandarin.
Will the text of the lectures be available?
Yes. All of our lectures will have transcripts synced to the videos.
Do I need to watch the lectures live?
No. You can watch the lectures at your leisure.
Will certificates be awarded?
Yes. Online learners who achieve a passing grade in a course can earn a certificate of mastery. These certificates will indicate you have successfully completed the course, but will not include a specific grade. Certificates will be issued by edX under the name of DelftX, designating the institution from which the course originated.
Can I contact the Instructor or Teaching Assistants?
Yes, but not directly. The discussion forums are the appropriate venue for questions about the course. The instructors will monitor the discussion forums and try to respond to the most important questions; in many cases response from other students and peers will be adequate and faster.
Is this course related to a campus course at Tsinghua?
Yes. This course corresponds to the campus courses 00240074 (elective for undergraduates of all majors) and 30240184 (required for CS undergraduates), both named Data Structures.
What is the textbook of the course?
Junhui DENG, Data Structures in C++, Sep. 2013, 3rd edn., Tsinghua University Press, ISBN: 7-302-33064-6. (in Chinese)
What is the grading breakdown?
60% - 12 problem sets
40% - 4 programming assignments
Knowing how to code is only part of the skills needed to become a professional software developer.
This course, part of the CS Essentials for Software Development Professional Certificate program, will take your skills to the next level by teaching you how to write “good” software that appropriately represents and organizes data, is easy to maintain, and is of high quality.
As the purpose of most computer programs is to manipulate data, sometimes large quantities of it, the manner in which programs represent and organize data can have an enormous effect on the simplicity and efficiency of the code. In this course, you will learn about important core data structures such as arrays, lists, stacks, queues, sets, maps, trees, and graphs, and learn how to evaluate them and reason about their behavior and efficiency.
Most importantly, you will learn how to determine which data structure is the most appropriate for solving the problem at hand, and see how to use the implementations that are part of the Java library.
However, choosing the right data structure is only part of the challenge of developing high quality software: you must also consider the design of the classes that use those data structures. You will learn about software design principles such as modularity, functional independence, and abstraction, and apply those concepts toward writing programs that are easy to understand, easy to modify, and easy to test.
Although it is important to know how to write high quality code, professional software developers often spend a majority of their time maintaining existing code. You will also learn about software refactoring techniques for improving the design of existing code, and see how to improve code efficiency.
This course will use Java but the concepts you learn can be applied to almost all modern programming languages.
Learn how to transform information from a format efficient for computation into a format efficient for human perception, cognition, and communication. Explore elements of computer graphics, human-computer interaction, perceptual psychology, and design in addition to data processing and computation.
This course presents an example of how to apply a database application development methodology to a major real-world project.
All the database concepts, techniques and tools that are needed to develop a database application from scratch will be introduced along the way as you apply them to your own major class team project.
In addition to the development methodology, techniques and tools learned in this course will include the Extended Entity Relationship Model, the Relational Model, Relational algebra, calculus and SQL, database normalization, efficiency and indexing. Finally, techniques and tools for metadata management and archival will be presented.
Machine learning uses computers to run predictive models that learn from existing data to forecast future behaviors, outcomes, and trends. Deep learning is a sub-field of machine learning, where models inspired by how our brain works are expressed mathematically, and the parameters defining the mathematical models, which can be in the order of few thousands to 100+ million, are learned automatically from the data.
Deep learning is a key enabler of AI powered technologies being developed across the globe. In this deep learning course, you will learn an intuitive approach to building complex models that help machines solve real-world problems with human-like intelligence. The intuitive approaches will be translated into working code with practical problems and hands-on experience. You will learn how to build and derive insights from these models using Python Jupyter notebooks running on your local Windows or Linux machine, or on a virtual machine running on Azure. Alternatively, you can leverage the Microsoft Azure Notebooks platform for free.
This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology. At the same time, you will learn simple yet powerful “motifs” that can be used with lego-like flexibility to build an end-to-end deep learning model. You will learn how to use the Microsoft Cognitive Toolkit — previously known as CNTK — to harness the intelligence within massive datasets through deep learning with uncompromised scaling, speed, and accuracy.
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