With more Internet-connected devices, machines, and applications talking to one another than ever before, corporations are amassing unprecedented mountains of information. The need among industry to manage data effectively is growing. As McKinsey Global Institute reports:
The United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.
About 85% of Fortune 500 companies today have a big data initiative underway or in the works. What is your organization doing to train and develop your staff?
Our 3-course, 9-credit online Data Science Certificate of Completion is aimed at providing your employees a strong foundation to analyze large-scale data sets, and create useful insights for your organization. Courses include big data analysis, machine learning and principles of data systems. Principles of database systems can be substituted with visualization or cloud computing courses if a student already has a database background.
Students who complete the certificate will learn:
Students who enter the program without background in databases will acquire knowledge of relational database systems and how to use such systems to build online services and communities
Students who enter the program with a background in databases can choose to acquire basic knowledge in either data visualization or cloud-computing technologies.
Courses taken in this program are for credit and can be transferred toward a qualifying Master’s degree at NYU Tandon School of Engineering. All three courses are required to obtain the certificate.
Principles of Database Systems can be substituted for Visualization or Cloud Computing based upon student knowledge in databases. See description below.
Broadly introduces you to database systems, including the relational data model, query languages, database design, index and file structures, query processing and optimization, concurrency and recovery, transaction management and database design. You acquire hands-on experience working with database systems, building Web-accessible database applications.
Big Data requires the storage, organization, and processing of data at a scale and efficiency that go well beyond the capabilities of conventional information technologies. You will review the state of the art in Big Data analytics, covering different platforms, models, and languages. You will look at real applications that perform massive data analysis and how they can be implemented on Big Data platforms. Covers Map reduce/Hadoop, NoSQL stores, languages, such as Pig Latin and JAQL, large-scale data mining and visualization. Offers insights into key technical readings, together with programming projects that you prototype using data-intensive applications from Big Data tools and platforms.
Introduces you to the field of machine learning, covering standard machine-learning techniques, such as decision trees, nearest neighbor, Bayesian methods, support vector machines and logistic regression. Offers basic concepts in computational learning theory, including the PAC model and VC dimension. You will emerge with wide-ranging knowledge of methods for evaluating and comparing machine learning techniques.
Provides you with significant knowledge about how to use a cloud, write cloud applications, and build your own private cloud. Covers both Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) cloud technologies and concepts. Starts with a review of basic building blocks, such as virtualization technologies, virtual appliances, automated provisioning, elasticity, and cloud monitoring, using and extending capabilities available in real clouds, such as Amazon AWS, Google App Engine, and OpenStack. Continues with more advanced topics, emphasizing ultralarge-scale systems, computational models, and storage for Big Data. Covers storage cloud, cloud security, Hadoop for Big Data and software-defined networking. You gain hands-on experience with several real-world applications and research innovations, including Facebook Cassandra, Amazon Dynamo, Google Big Table, Hadoop HDFS, and Yahoo Zookeeper.
An introductory, modern and cohesive view of information visualization. Covers visualization design, data principles, visual encoding principles, interaction principles, single/multiple view methods, item/attribute, attribute reduction methods, toolkits, and evaluation. Provides overviews and examples drawn from state-of-the-art research. The course is designed for those who either intend to specialize in visualization or for others who wish to apply visualization principles and existing techniques in their work.
This program is designed for professionals with a technical background, including the following:
Courses are offered starting in January, May and September. Inquire with our Enterprise Learning Department on how your employees can participate.
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