Master of Project Management
The Master of Project Management offered by Swinburne University is a ...
Bachelor of Computer Science (Data Science)
Hawthorn
INTAKE: Mar & Jul
Program Overview
Swinburne University of Technology's Bachelor of Computer Science (Data Science) program is designed for students interested in harnessing data to extract insights, solve complex problems, and drive decision-making across industries. The program integrates foundational computer science principles with specialized knowledge in data analytics, machine learning, and statistical modeling. Students learn to collect, manage, analyze, and interpret large datasets to inform business strategies and innovation.
Curriculum: The curriculum covers core computer science subjects such as programming, algorithms, data structures, and database management, tailored specifically to data science applications. Advanced coursework includes topics in data mining, machine learning algorithms, data visualization, and predictive analytics. Students engage in practical projects and case studies, gaining hands-on experience with tools and techniques used in data-driven decision-making.
Research Focus: Swinburne emphasizes 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 big data analytics, deep learning, natural language processing, and ethical considerations in data science. 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 partners and data-driven organizations through collaborations, internships, and guest lectures by industry experts. Students benefit from industry-relevant workshops, hackathons, and real-world projects that address industry challenges. This engagement ensures the program remains aligned with industry demands and prepares graduates for successful careers in data science.
Global Perspective: Swinburne integrates a global perspective into its data science program by addressing international data privacy laws, global data governance frameworks, and cross-cultural data analytics challenges. Students learn to analyze datasets from diverse cultural and regional perspectives, preparing them for roles in multinational corporations or projects that require understanding of global data dynamics. The program fosters cross-cultural collaboration and prepares graduates to contribute effectively in a globalized data-driven economy.
Location
Hawthorn
Score
IELTS 6
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 (Data Science) program are well-prepared for diverse and high-demand roles within the rapidly growing field of data science and analytics. The program equips students with strong analytical, statistical, and programming skills necessary to extract meaningful insights from large datasets and drive data-driven decision-making across industries.
Data Scientist: Data scientists analyze complex datasets to uncover patterns, trends, and correlations that help organizations make informed decisions. They use statistical techniques, machine learning algorithms, and data visualization tools to derive actionable insights and solve business challenges. Graduates proficient in programming languages (e.g., Python, R), big data platforms (e.g., Hadoop, Spark), and data manipulation tools (e.g., SQL, Tableau) excel in this role.
Machine Learning Engineer: Machine learning engineers develop and deploy machine learning models and algorithms to automate data analysis and predictive analytics. They design algorithms, train models on large datasets, and optimize model performance for real-world applications. Proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch), deep learning techniques, and software engineering principles is essential for this role.
Data Analyst: Data analysts collect, clean, and interpret data to help organizations understand trends, identify opportunities, and make strategic decisions. They create reports, dashboards, and data visualizations to communicate findings to stakeholders effectively. Graduates skilled in data querying languages (e.g., SQL), data cleaning techniques, and statistical analysis methods are highly sought after for data analyst roles.
Business Intelligence (BI) Developer: BI developers design and maintain data systems and tools that support business intelligence and reporting functions. They build data warehouses, develop ETL (Extract, Transform, Load) processes, and create dashboards and reports for business users. Proficiency in BI platforms (e.g., Power BI, Tableau), data modeling techniques, and knowledge of business processes are critical for BI developer positions.
Data Engineer: Data engineers design and manage large-scale data infrastructure and systems to support data processing and analysis. They develop pipelines for data integration, transformation, and storage, ensuring data quality and reliability. Skills in big data technologies (e.g., Apache Kafka, Apache Hadoop), cloud platforms (e.g., AWS, Azure), and distributed computing frameworks are essential for data engineering roles.
Quantitative Analyst (Quant): Quants apply mathematical and statistical models to analyze financial markets, risk management, and investment strategies. They develop algorithms and quantitative models to optimize trading strategies and financial decision-making. Graduates with strong quantitative skills, knowledge of financial markets, and proficiency in programming languages (e.g., MATLAB, Python) are suited for quant roles in finance and investment firms.
Data Privacy and Security Analyst: Data privacy and security analysts focus on protecting sensitive information and ensuring compliance with data protection regulations (e.g., GDPR, CCPA). They assess security risks, implement data encryption measures, and develop policies and procedures to safeguard data privacy. Proficiency in cybersecurity principles, legal frameworks, and risk assessment techniques is essential for data privacy and security analyst positions.
Career Advancement: With ongoing professional development, industry certifications (e.g., Certified Data Scientist, Google Professional Data Engineer), and specialization in emerging technologies (e.g., AI ethics, blockchain analytics), graduates can advance to senior roles such as Chief Data Officer (CDO), Data Science Manager, or Principal Data Scientist. Continued learning and staying updated with advancements in data science tools and techniques are crucial for career progression in the dynamic field of data science.
