Timestamp Anomaly Detection Using IBM Watson IoT Platform

Katiyar, Aditi; Aktar, Neha; Mayank, .; Lavanya, K.

doi:10.1007/978-981-15-0184-5_66

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Then, the students are asked to undertake programming projects for big data analytics based on the learning materials of the course (the twelfth to fifteenth weeks). The topics related to the programming projects are natural language processing (preferred in Japanese since students are majoring in Japanese), data mining Twitter, IBM Watson [27], [28] APIs, and machine learning.…”

Section: B Details Of Course Designmentioning

confidence: 99%

Developing Big Data Analytics Course for Non-ICT Major University Students

Lim¹,

Moon²

2021

International Journal on Advanced Science, Engineering and Information Technology

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In the fourth industrial revolution's education era, there is no boundary between majors or subjects, and it is common for university students to enrol in information and communication technology (ICT)-related courses as convergence education blends different disciplines. Today's job market is getting more competitive and requiring higher skills in ICT and computational thinking. Since non-ICT major students rarely have programming experiences and knowledge in regular classes, teaching a big data analytics course for non-ICT major university students is not easy. Thus, it is vital to develop a curriculum that comprises easy-to-follow and easy-to-understand modules. In this paper, we develop a big data analytics course for non-ICT major university students. The proposed big data analytics course for non-ICT major students comprises two parts: (1) basic programming skill modules with step-by-step guidelines and (2) extension to big data analytics modules with laboratory exercises, with the five principal programming modules based on the Python programming language. First, our investigation discusses the suggestions and limitations of the big data analytics course for non-ICT major university students. Then, we recommend programming languages, integrated development environments (IDEs), and useful tools that help learners perform programming exercises and milestone projects. The learning objectives and course design models are carefully selected based on Bloom's taxonomy with six thinking levels and five.

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Section: B Details Of Course Designmentioning

confidence: 99%