Courses tagged with "Computer Science" (735)
In this course, you’ll be introduced to virtual network configuration through the Microsoft Azure Portal and network configuration files. You’ll also see how to use network services to configure and load balance network traffic using tools such as Azure DNS. Load Balancer, Azure Traffic Manager, and Application Gateway. And because this is about the cloud, you’ll see how to connect your on-premises computers to Azure virtual networks as well as establishing connectivity between sites.
This course focuses on Azure Storage as a service that scales to meet the data storage demand, allows data access anywhere at any time based on an internet connection, provides a platform for building internet-scale applications, and can store structured and non-structured data in the appropriate format in the cloud.
You’ll be introduced to managing storage through Azure Storage accounts as well as the different types of accounts a storage account can contain.
Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. The collection of skills required by organizations to support these functions has been grouped under the term ‘data science’.
This statistics and data analysis course will attempt to articulate the expected output of data scientists and then teach students how to use PySpark (part of Spark) to deliver against these expectations. The course assignments include log mining, textual entity recognition, and collaborative filtering exercises that teach students how to manipulate data sets using parallel processing with PySpark.
This course covers advanced undergraduate-level material. It requires a programming background and experience with Python (or the ability to learn it quickly). All exercises will use PySpark (the Python API for Spark), and previous experience with Spark equivalent to Introduction to Apache Spark, is required.
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.
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.
Social physics is a big data science that models how networks of people behave and uses these network models to create actionable intelligence. It is a quantitative science that can accurately predict patterns of human behavior and guide how to influence those patterns to (for instance) increase decision making accuracy or productivity within an organization. Included in this course is a survey of methods for increasing communication quality within an organization, approaches to providing greater protection for personal privacy, and general strategies for increasing resistance to cyber attack.
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.
Organizations now have access to massive amounts of data and it’s influencing the way they operate. They are realizing in order to be successful they must leverage their data to make effective business decisions.
In this course, part of the Big Data MicroMasters program, you will learn how big data is driving organisational change and the key challenges organizations face when trying to analyse massive data sets.
You will learn fundamental techniques, such as data mining and stream processing. You will also learn how to design and implement PageRank algorithms using MapReduce, a programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. You will learn how big data has improved web search and how online advertising systems work.
By the end of this course, you will have a better understanding of the various applications of big data methods in industry and research.
Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from high content molecular and phenotype profiling of human cells.
This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.
This course teaches the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in life sciences. The lectures are taught in both Mandarin Chinese and English with slides in English. 生物信息学是一门新兴的生命科学与计算科学的前沿交叉学科。本课程讲授生物信息学主要概念和方法，以及如何应用生物信息学手段解决生命科学问题。本课程同时提供中文普通话授课和英文授课两个版本，以及英文幻灯片。
Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gentler-paced alternative to the first course in our Bioinformatics Specialization (Finding Hidden Messages in DNA).
Want to learn how to serve data to your client applications using Web API? Perhaps you are considering creating applications for mobile devices but your data needs will exceed the storage capacity of the device. Perhaps you want tighter control over the data and wish to provide options for devices with limited or not always-on connectivity.
This course offers insight into the use of Web APIs using ASP.NET and C#. You’ll start with a review of client/server architectures and learn about data serialization and deserialization with JSON as the data format.
You will then be introduced to REST and RESTful concepts with discussions on synchronous and asynchronous programming.
The third module introduces you to ASP.NET Core and using Entity Framework for data access.
Finally, you will learn how to use Cross-Origin Resource Sharing (CORS) with your services and how to secure your Web APIs
Technologies are always being defeated.
If you own an information asset that’s valuable enough to the right adversary, it’s only a matter of time before there’s a breach. Today’s technologies attempt to keep adversaries out, but the sad fact is they will inevitably be defeated. This means a successful cybersecurity professional needs to have an expanded arsenal in their toolkit that extends far beyond technical proficiency.
Cybersecurity professionals need to be agile, multifunctional, flexible, and dynamic given how quickly things can change. They need to be able to adapt to change and problem solve quickly, have diverse knowledge to perform many activities, respond to new threats and shift priorities to meet the challenge of the day.
The purpose of this course is to give learners insight into these type of characteristics and skills needed for cybersecurity jobs and to provide a realistic outlook on what they really need to add to their “toolkits” – a set of skills that is constantly evolving, not all technical, but fundamentally rooted in problem-solving.
Students will learn from thought leaders from both the academic and practitioner communities.