Software Engineering for Data Science

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Course overview
  • Level: Intermediate
  • Study period: 16 weeks
  • Author: AIAT
  • Evaluation:
    Self-Assessment/Projects
  • Rich learning experiences:
    300-400 h    ours
This simulated internship course equips students to excel in next-gen data science teams, blending software engineering and data science. Projects cover agile requirement analysis, system architecture, security, and reliability measures, preparing students for career growth. 

Integrate software engineering principles with data science techniques to develop innovative solutions. 

Apply agile methodologies for requirement analysis and project management in data science projects. 

Design and implement secure and reliable data science applications with a focus on system architecture. 

Collaborate effectively in team-based projects, demonstrating strong communication and teamwork skills. 

Reflect on personal learning and career growth, identifying areas for future development in data science and software engineering. 

Unlock your career's potential with an industry-recognized professional qualification certificate

Our Facilitator

Dr. Murli Viswanathan

DIRECTOR, IT PROGRAMS

Prof. Murli has a PhD in Computer Science from Monash University and a BS in Computer Science & Information Systems from Deakin University. Prof. Murli worked with Carnegie Mellon University for 16 years before Joining AIAT where he is the Director of IT programs. He lectures students in AI, Machine Learning, Analytics, Big Data, Software Engineering, and other areas. He has managed/coordinated over two dozen student projects with the South Australian Government departments and industry partners. Prof. Murli is a senior member of the Australian Computer Society Accenture Subject Matter Expert in Analytics. He has substantial experience with research projects, course and curriculum design and has delivered a variety of courses at all levels in information systems and computer science since 1996.
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