ObjectivesThis 5-day course helps data engineers focus on essential design and architecture while building a data lake and relevant processing platform.Participants will learn various aspects of data engineering while building resilient distributed datasets. Participants will learn to apply key practices, identify multiple data sources appraised against their business value, design the right storage, and implement proper access model(s). Finally, participants will build a scalable data pipeline solution composed of pluggable component architecture, based on the combination of requirements in a vendor/technology agnostic manner. Participants will familiarize themselves on working with Spark platform along with additional focus on query and streaming libraries.This course is part of the Analytics and Intelligent Systems series offered by NUS-ISS.Upon effective completion of the course, participants will be able to:- Understand the growth of big data and need for a scalable processing framework. Understand the fundamental characteristics, storage, analysis techniques and the relevant distributions;- Understand the distributed storage essentials, storage needs, and relevant architectural mechanism in processing large amounts of structured, semi-structured and unstructured data;- Gain expertise with the fault-tolerant computing framework (E.g. YARN) by setting up pseudo cluster nodes or cloud based nodes for processing big data;- Construct configurable and executable tasks using the In Memory Processing frameworks (E.g. Understand the nuances of writing functional programs and use the core libraries to manipulate the large corpse of unstructured data residing as Resilient Distributed Datasets;- Organize, store and manipulate the collected data using processing libraries.
Big Data Course Syllabus Book
Resident evil 2 soundtrack download. For example, using special statistical operation and stream processing data tools (E.g. Spark Special Libraries);- Understand various data processing, querying and persistence (E.g. Spark QL APIs) available for usage in RDD’s context. Perform tasks such as filtering, selection and categorization.
Who Should AttendThis is an intermediate course, suitable for professionals with some experience in any programming language and data design. If the participants have some business exposure, they can appreciate the case studies discussed better.This course targets analytics professional including:- Business and IT professionals seeking analytical skills to handle large amounts of unstructured data (Data lake e.g. Customer feedbacks, product reviews on social media, phone call recordings, etc.) for insights to improve business process and decision-making;- Individuals who have no knowledge or experience in data engineering for analytics and would like to gain some practical skills in this area so that they may explore work opportunities in data engineering;- Data analysts and Data Engineers, who want to move from the structured to large amounts of unstructured data engineering. Entry RequirementsThis is an intensive, intermediate course. Our proposed course targets the higher value chain professionals such as: data engineers; data application architects; integration architects; software engineers working on data pipeline processing; key technology decision makers;Participants with experience in programming languages such as Python or Java or Scala will benefit more from the course. Participants also need to have a strong interest in building functional pipelines and be comfortable working with Hadoop platform and Spark framework.
Big Data Courses Free
Course ObjectiveThis two-week summer programme aims to help students adapt to the changes in an industry that demands for Business Finance Knowledge and Financial Data Analytics skills. Students will be immersed in real-life investment and finance case studies from NUS Business School professors, as well as hands-on coding tuition and exercise with real financial data. Trx header not found dead.
In addition, they will have opportunities to study in a world-class trading lab run by the Centre for Asset Management Research and Investments (CAMRI), as well as network with our Finance faculty and industry practitioners.Target AudienceThis summer programme is designed primarily for local and overseas undergraduate students who want to take advantage of their summer break to advance their knowledge of finance, financial data analytics and programming. Non-undergraduates may apply too.
The intake is limited to 40 students.Course FeesSGD 6,000 (excl GST) for full-time undergraduate studentsSGD 7,000 (excl GST) for all other participants. Sumit is the Head of Finance department and a Professor in the departments of Economics, Finance and Real Estate. His research on issues related to household finance, behavioural finance, international finance, real estate markets, and capital markets has been published in top tier journals and cited in international media. He has consulted with the World Bank, was a senior financial economist with the Federal Reserve Bank in Chicago and held the position of Senior Vice President in the Small Business Risk Solutions Group of Bank of America.
Weina is a senior lecturer with the Department of Finance. Her research focuses on fixed income securities, asset pricing and sustainable & social finance. She has published many academic papers in leading finance and economics journals.
She has taught Investments Analysis and Portfolio Management, Corporate Finance, Research Methods in Finance, and International Financial Management, and impact investing to a wide range of students. She has also published many teaching cases and textbooks. She is a recipient of several best paper awards and teaching awards. Weiqi is a senior lecturer with the Department of Finance. She has taught undergraduate, graduate, executive education courses to a large cross section of students, and her areas of expertise include Personal Finance & Wealth Management, Financial Markets, Investments and Portfolio Management, and Technologies and Data Analytics in Finance.
Her research interests include empirical asset pricing, asset allocation, portfolio management and FinTech. Her research has been published in leading academic journals, including Management Science.
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