Foundations of Data Analytics
Master fundamental data analytics techniques including data collection, extraction, integration, and machine learning using Python in this comprehensive course.
Master fundamental data analytics techniques including data collection, extraction, integration, and machine learning using Python in this comprehensive course.
This comprehensive course provides essential foundations in data analytics, covering crucial techniques for the modern data professional. Students will learn to manage and optimize the analytics value chain, from data collection and extraction to integration and cleansing. The course emphasizes practical skills with Python programming and introduces core concepts in data security and privacy. Through hands-on experience with data preprocessing and basic machine learning techniques, learners will develop the fundamental skills needed to work with big data technologies. This course combines theoretical knowledge with practical application, preparing students for real-world data analytics challenges.
4.4
(8 ratings)
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What you'll learn
Master data preprocessing techniques including collection extraction and cleansing
Develop proficiency in Python programming for data analysis
Apply basic machine learning techniques to real-world datasets
Understand fundamental concepts of data integration and security
Implement statistical analysis methods using Python libraries
Optimize the analytics value chain from data collection to insights
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive foundation in data analytics, focusing on essential techniques and tools used in the industry. Students learn fundamental concepts of data preprocessing, including collection, extraction, integration, and cleansing. The curriculum covers basic machine learning techniques and statistical analysis, with practical implementation using Python programming. Special attention is given to data security and privacy concerns. The course emphasizes hands-on experience with Python libraries for data preprocessing and analysis, preparing students for real-world data analytics challenges.
Fee Structure
Instructor
7 Courses
A Leading Expert in Information Extraction and Text Mining
Cecia Ki Chan serves as a Lecturer in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology, where she has been teaching since 2012 after completing her PhD in Systems Engineering and Engineering Management from the Chinese University of Hong Kong. Her research expertise spans information extraction, text mining, machine learning, and knowledge systems, with significant contributions to document classification, coreference resolution, entity extraction, and domain adaptation. As an instructor in multiple programs, she teaches courses ranging from introductory programming to advanced topics like Data Mining and Big Data Analytics, while supervising numerous award-winning final year projects including the Mobile App for Health and Well Being and TRAP: A VR and Mobile Cross-platform Networking Game. Her teaching portfolio includes core computer science courses such as Programming with C++, Object-Oriented Programming and Data Structures, and Exploring Artificial Intelligence, demonstrating her commitment to both undergraduate and postgraduate education. Beyond her academic responsibilities, she actively engages in developing practical applications, particularly in areas of virtual reality systems and health technology, showcasing the real-world impact of her expertise in computer science and engineering.
Testimonials
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4.4 course rating
8 ratings
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