Program start date | Application deadline |
2024-07-15 | - |
Program Overview
This two-semester program in Data Science equips graduates with skills in data management, predictive modeling, and algorithm development. It emphasizes hands-on learning and prepares students for careers as data scientists, analysts, and engineers in various industries. The program is designed for graduates with a background in Computer Science or Statistics and offers a pathway to the Master of Data Science program.
Program Outline
Outline:
Program Content:
- The program focuses on data science, including data management, predictive modelling, and algorithm development.
- It aims to meet the growing demand for data scientists and equip graduates with relevant skills and knowledge.
- Students will be able to apply principles and practices from various data science fields to solve real-world problems.
Program Structure:
- The program consists of two semesters, each with a duration of 12 weeks.
- Each semester offers one course, resulting in a total of two courses.
- Completion of the program takes as little as two semesters.
Course Schedule:
- Semester 1: DATASCI 709 Data Management
- Semester 2: STATS 709 Predictive Modelling (for Computer Science background) or COMPSCI 717 Fundamentals of Algorithmics (for Statistics background)
- Utilizing R and SQL, students learn real-world applications of data management concepts.
STATS 709 Predictive Modelling:
- Provides an advanced introduction to statistics and data analysis, followed by modern predictive modelling techniques and machine learning.
COMPSCI 717 Fundamentals of Algorithmics:
- Focuses on algorithm design techniques like greedy algorithms, divide-and-conquer, and dynamic programming.
- Explores data structures for efficient algorithm implementation.
- Teaches essential tools for algorithm analysis, including worst- and average-case analysis of space and time.
Assessment:
Assessment Methods:
- The program employs various assessment methods to evaluate student learning outcomes.
- These may include assignments, exams, projects, presentations, and participation.
Assessment Criteria:
- Students are assessed based on their understanding of data science concepts, ability to apply them in practical scenarios, and critical thinking skills.
- Other factors like communication skills and teamwork may also be considered.
Teaching:
Teaching Methods:
- The program utilizes diverse teaching methods to cater to different learning styles.
- This may include lectures, tutorials, workshops, guest lectures, and online resources.
Faculty:
- The program is taught by experienced and qualified faculty with expertise in data science, statistics, and computer science.
Unique Approaches:
- The program emphasizes hands-on learning through practical exercises and projects.
- It provides opportunities for students to collaborate and network with peers and industry professionals.
Careers:
Career Paths:
- The program prepares graduates for diverse career paths in data science and related fields.
- Possible career options include data scientist, data analyst, business intelligence analyst, statistician, and machine learning engineer.
Opportunities and Outcomes:
- Graduates will be equipped with the necessary skills and knowledge to pursue rewarding careers in the rapidly growing data science industry.
- They will be able to contribute to various industries and sectors, including healthcare, finance, marketing, and technology.
Other:
- The program is specifically designed for graduates with a background in either Computer Science or Statistics.
- It provides a direct pathway to the Master of Data Science program for those who wish to further advance their studies.
- Entry requirements include a Bachelor's degree with a GPA of 4.0 or higher in 75 points above Stage II in the relevant field.
University of Auckland Summary
Overview:
The University of Auckland is New Zealand's leading university, renowned for its academic excellence and commitment to research. It offers a wide range of undergraduate and postgraduate programs across various disciplines.
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