Master of Project Management
The Master of Project Management offered by Swinburne University is a ...
Bachelor of Computer Science Professional (Data Science)
Hawthorn
INTAKE: Mar & Jul
Program Overview
Swinburne University of Technology's Bachelor of Computer Science Professional (Data Science) program prepares students for careers in the rapidly expanding field of data science. It combines foundational computer science principles with specialized knowledge in data analytics, machine learning, and statistical modeling. The program emphasizes both theoretical understanding and practical application of data-driven solutions, equipping graduates with the skills to extract valuable insights from complex data sets.
Curriculum: The curriculum covers core computer science subjects such as programming, algorithms, and database management, tailored to data science applications. Advanced coursework includes topics in data mining, big data analytics, predictive modeling, and data visualization. Students engage in hands-on projects and real-world case studies, applying statistical methods and machine learning algorithms to analyze and interpret data.
Research Focus: Swinburne promotes a research-oriented approach to data science education, encouraging students to explore emerging trends and methodologies in the field. The program supports research initiatives in areas such as deep learning, natural language processing, bioinformatics, and business analytics. Students have opportunities to collaborate with faculty on research projects and contribute to advancements in data science theory and practice.
Industry Engagement: The university maintains strong ties with industry leaders and data-driven organizations through partnerships, internships, and collaborative projects. Students gain practical experience and industry insights through guest lectures, workshops, and industry placements. This engagement ensures the program remains aligned with industry demands and prepares graduates for diverse roles in data science across sectors such as finance, healthcare, marketing, and technology.
Global Perspective: Swinburne's data science program integrates a global perspective by addressing international data privacy laws, ethical considerations in data usage, and global data trends. Students learn to analyze data from diverse geographical regions and cultural contexts, preparing them for careers in multinational companies or roles that require understanding of global data markets. The program fosters cross-cultural collaboration and prepares graduates to address global challenges through data-driven solutions.
Location
Hawthorn
Score
IELTS 6.5
Tuition Fee
AUD 35160
Undergraduate Entry Requirements
Academic Qualifications: Applicants for undergraduate programs typically require a minimum academic achievement of 65% or above in their previous academic qualifications.
English Language Proficiency:
- IELTS: Overall band score of 6.0 or 6.5 with a minimum of 6.0 in each component.
- TOEFL: Overall score of 79 with a minimum of 20 in each section.
- PTE (Pearson Test of English): Overall score of 58 with a minimum of 50 in each section.
Students must provide:
- academic marksheets & transcripts
- letters of recommendation
- a personal statement - SOP
- passport
- other supporting documents as required by the university.
It's important to note that entry requirements can vary by program and may change over time. Additionally, some programs may have additional requirements, such as interviews, portfolios, or work experience.
Swinburne University offers a range of scholarships designed to assist international students in achieving their educational goals. These scholarships are awarded based on various criteria, including academic merit, financial need, and specific areas of study.
Swinburne International Excellence Scholarship: This merit-based scholarship recognizes outstanding academic achievements. It provides financial support to high-achieving international students pursuing undergraduate or postgraduate degrees.
Swinburne Sports Excellence Scholarship: Aimed at supporting talented athletes, this scholarship assists international students who excel in sports while pursuing their academic studies at Swinburne.
Swinburne Student Achievement Award: Recognizing not only academic achievements but also contributions to the community, this scholarship provides financial support and acknowledges a holistic approach to personal growth and success.
Government Scholarships: Swinburne University collaborates with various governments and organizations worldwide to provide scholarships and sponsorships for international students from specific countries. These opportunities vary by nation and are offered in conjunction with external partners.
Graduates from Swinburne University of Technology's Bachelor of Computer Science Professional (Data Science) program are well-prepared for diverse and in-demand roles within the data science field. The program equips students with a strong foundation in computer science, alongside specialized skills in data analysis, machine learning, and statistical modeling.
Data Scientist: Data scientists utilize advanced analytical techniques to extract insights from large datasets. They clean, process, and analyze data to uncover patterns, trends, and correlations that inform business decisions. Graduates proficient in programming languages (such as Python or R), statistical methods, and machine learning algorithms are highly sought after in this role.
Data Analyst: Data analysts focus on interpreting data to help organizations make informed decisions. They gather, clean, and analyze data using statistical techniques and data visualization tools. Graduates skilled in SQL, Excel, and data querying techniques play a crucial role in transforming raw data into actionable insights for businesses across various industries.
Machine Learning Engineer: Machine learning engineers design and implement algorithms that allow machines to learn from data and make predictions or decisions. They develop models, train algorithms, and optimize performance for specific applications like recommendation systems or image recognition. Proficiency in programming, data manipulation, and understanding of neural networks are key for this role.
Business Intelligence (BI) Developer: BI developers create and manage business intelligence solutions that provide insights into organizational data. They design data warehouses, develop dashboards and reports, and ensure data accuracy and accessibility. Graduates skilled in BI tools like Tableau or Power BI, as well as data modeling and ETL (Extract, Transform, Load) processes, excel in this analytical role.
Data Engineer: Data engineers design and build data pipelines to collect, process, and store large datasets. They develop scalable architectures for data storage and integration, ensuring data quality and accessibility for analytical purposes. Proficiency in programming languages (e.g., Python, Scala), database management systems (e.g., SQL, NoSQL), and cloud platforms (e.g., AWS, Azure) is crucial for data engineering roles.
Quantitative Analyst (Quant): Quants apply mathematical and statistical models to financial data to develop trading strategies, risk models, and financial forecasts. They use programming languages and quantitative techniques to analyze market trends and optimize investment decisions. Graduates with strong quantitative skills, financial knowledge, and programming proficiency are sought after in quantitative finance roles.
Research Scientist: Research scientists focus on advancing data science methodologies and techniques through research and development. They explore new algorithms, machine learning models, and applications of data science in domains like healthcare, biology, or social sciences. Graduates with a background in research, strong analytical skills, and a passion for innovation contribute to groundbreaking discoveries in their respective fields.
Career Advancement: With experience and continuous professional development, graduates can advance to senior roles such as Data Science Manager, Chief Data Officer (CDO), or Head of Analytics. Industry certifications (e.g., Certified Analytics Professional, AWS Certified Data Analytics) and specialization in emerging technologies (e.g., AI, big data) further enhance career prospects in the evolving field of data science.
