COM SCI 245
Big Data Analytics
Description: Lecture, four hours; outside study, eight hours. Requisites: courses 143 or 180 or equivalent. With unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is emerging economic and scientific need to extract useful information from it. Data analytics is process of automatic discovery of patterns, changes, associations, and anomalies in massive databases, and is highly inter-disciplinary field representing confluence of several disciplines, including database systems, data warehousing, data mining, machine learning, statistics, algorithms, data visualization, and cloud computing. Survey of main topics in big data analytics and latest advances, as well as wide spectrum of applications such as bioinformatics, E-commerce, environmental study, financial market study, multimedia data processing, network monitoring, social media analysis. Letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2024 - It is a cool class and you learn quite a lot. The professor is fairly ok at explaining concepts; however, the guest lecturer was bad (who lectures 2 weeks). Some of the big data algos near the end were interesting and pretty relevant. We start off with clustering and classification, mainly going into the different types of algos you wouldnt see in undergrad, like DBSCAN, CLARA, etc. Then we move to the interesting topics like graph learning, language models and pattern mining. And then we have a final at the end of week 7, which was a bit too tough and probably double the length of the practice. Then followed by weeks of Paper and Project presentations. Imo I dont think we need so much time for Paper presentations. The TAs were very good. The grading was very slow. • Assignments 30% (2 big homeworks) • Exam 25% • Paper presentation 10% • Final project 35% I think the curve was very generous, given the shitty final scores.
Fall 2024 - It is a cool class and you learn quite a lot. The professor is fairly ok at explaining concepts; however, the guest lecturer was bad (who lectures 2 weeks). Some of the big data algos near the end were interesting and pretty relevant. We start off with clustering and classification, mainly going into the different types of algos you wouldnt see in undergrad, like DBSCAN, CLARA, etc. Then we move to the interesting topics like graph learning, language models and pattern mining. And then we have a final at the end of week 7, which was a bit too tough and probably double the length of the practice. Then followed by weeks of Paper and Project presentations. Imo I dont think we need so much time for Paper presentations. The TAs were very good. The grading was very slow. • Assignments 30% (2 big homeworks) • Exam 25% • Paper presentation 10% • Final project 35% I think the curve was very generous, given the shitty final scores.
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