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Master of Data Science (Global) Program

Master of Data Science (Global) Program

Master Data Science for impactful career growth

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Application closes 30th Jun 2025

What's new in this Master's in Data Science?

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    Advanced Modules on ChatGPT & Generative AI

    Discover cutting-edge ChatGPT and Generative AI modules to revolutionize Data Science. Learn to streamline workflows, extract insights, and tackle complex business problems.

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    AI & Business Analytics Modules

    Explore a curriculum that combines Data Science and AI. Gain insights into business analytics and consulting while learning to solve problems using business frameworks.

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Program Outcomes

Elevate your career with advanced Data Science & AI skills

Become a Data Scientist with advanced Data Science & AI skills

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    Develop a deep understanding of the Data Science and AI landscape

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    Master essential tools like Python to solve real-world business problems

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    Master Data Science, Deep Learning, Analytics, and GenAI to drive strategic decisions

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    Secure your dream career in Data Science with our dedicated career support

Earn a master's degree from Deakin University

  • QS 2025

    Top 1% of Universities globally (QS 2025)

  • victoria

    Victorian Government Award 2020

  • WES

    World Education Services (WES) Recognized

Key program highlights

Why choose the Master of Data Science (Global) program

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    A global masters degree & PG certificates

    Gain the recognition of a global masters degree and PG certificates from global universities at just 1/10th the cost of a 2 year on-campus masters

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    Practical, hands-on learning from world-class faculty

    Live virtual classes, Industry sessions and competency courses delivered by experts and faculty at Deakin

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    Industry-ready curriculum

    Curriculum designed in a modular structure with foundational and advanced competency track

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    Dedicated career support

    Get expert guidance to prepare for job roles with mock interviews, resume building, and e-portfolio review

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    11 hands-on projects & 22+ tools

    The program includes 11 hands-on projects, 1 capstone project, 60+ case studies, and 22+ tools to strengthen practical and conceptual knowledge.

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    On-campus graduation ceremony in Australia

    Opportunity to attend a graduation ceremony (optional) at the Deakin University campus in Melbourne.

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    Connect with your alumni community

    Join the alumni portal with over 300,000 Deakin graduates, reconnect, and meet with fellow alumni across the globe.

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    Deakin credentials and alumni benefits

    Enrolled students receive Deakin email IDs and, as alumni, are eligible for a 10% discount per unit on enrolment fees for any postgraduate award course at Deakin.*
    *Terms and Conditions apply

Skills you will learn

Machine Learning

SQL

Predictive Modeling

Python

Natural Language Processing (NLP)

Data Visualization using Tableau

Neural Networks & Computer Vision

Data Analysis

Deep Learning

Generative AI

Prompt Engineering

Model Deployment

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Supervised Learning

Unsupervised Learning

Machine Learning

SQL

Predictive Modeling

Python

Natural Language Processing (NLP)

Data Visualization using Tableau

Neural Networks & Computer Vision

Data Analysis

Deep Learning

Generative AI

Prompt Engineering

Model Deployment

Hugging face

Supervised Learning

Unsupervised Learning

view more

Secure top Data Science jobs

  • 11.5 million

    jobs in India by 2026

  • $303 billion

    market growth by 2030

  • 4 out of 5

    companies use Data Science

  • Up to 23 lakhs

    avg annual salary

Careers in Data Science

Here are the ideal job roles in Data Science sought after by companies in India

  • Data Scientist

  • Machine Learning Engineer

  • Business Analyst

  • Data Architect / Data Warehouse Architect

  • AI Architect

  • Analytics Manager

  • Data Analyst

  • Big Data Engineer

  • Business Intelligence Analyst

Our alumni work at top companies

  • Overview
  • Career Transitions
  • Learning Path
  • Curriculum
  • Projects
  • Tools
  • Certificate
  • Faculty
  • Career support
  • Fees
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This program is ideal for

The Master of Data Science (Global) program from Deakin University empowers you to align your learning with your professional aspirations

  • Young professionals & new graduates

    Build a foundation in Data Science with Python, Tableau, and Machine Learning. Gain real-world experience to kickstart your Data Science career.

  • Mid-senior professionals

    Boost your analytics skills with AI, Deep Learning, and Business Analytics to advance into strategic roles in the field of Data Science.

  • Non-tech professionals

    Break into the world of Data Science with beginner-friendly modules and hands-on learning. Acquire the skills to transform data into actionable insights and seamlessly switch to a data-driven career.

  • Tech Leaders

    Lead AI innovation with strategic insights, advanced AI & ML skills, and the ability to drive business transformation.

Learning Path

With credentials from globally recognised universities, graduates of Deakin’s Master of Data Science (Global) program are strong contenders for high-impact roles in the data science industry.

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    Earn PG certificates from world's leading institutions

    Learners will join the Post Graduate Program by The University of Texas at Austin and Great Lakes Executive Learning and receive the PG Certificates upon program completion.

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    Join the 12-month Deakin University program

    Post completion of the PG Program from The University of Texas at Austin and Great Lakes Executive Learning, candidates will continue their learning journey with the 12-month online Master of Data Science (Global) from Deakin University.

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    Earn a Master's Degree from Deakin University

    Once you complete the program successfully, you will receive the Master of Data Science (Global) Degree from Deakin University.

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Curriculum

Data Science Pathway

PGP-DSBA CURRICULUM


The curriculum of the PGP in Data Science and Business Analytics has been updated in consultation with industry experts, academicians and program alums to ensure you learn the most cutting-edge topics.

DATA SCIENCE FOUNDATIONS

Module 1: Statistical Methods for Data Science

  • Descriptive Statistics

  • Introduction to Probability

  • Probability Distributions

  • Hypothesis Testing and Estimation

  • Goodness of Fit


Module 2: Business Finance

  • Fundamentals of Finance

  • Working Capital Management

  • Capital Budgeting

  • Capital Structure

Module 3: Marketing and CRM

  • Core Concepts of Marketing

  • Customer Lifetime Value

Module 4: SQL Programming

  • Introduction to DBMS

  • ER Diagram

  • Schema Design

  • Key Constraints and Basics of Normalization

  • Joins

  • Subqueries Involving Joins and Aggregations

  • Sorting

  • Independent Subqueries

  • Correlated Subqueries

  • Analytic Functions

  • Set Operations

  • Grouping and Filtering

DATA SCIENCE TECHNIQUES

Module 1: Inferential Statistics

  • Analysis of Variance

  • Regression Analysis

  • Dimension Reduction Techniques


Module 2: Predictive Modeling

  • Multiple Linear Regression (MLR) for Predictive Analytics

  • Logistic Regression

  • Linear Discriminant Analysis

Module 3: Machine Learning-1

  • Introduction to Supervised and Unsupervised Learning

  • Clustering

  • Decision Trees

  • Random Forest

  • Neural Networks



Module 4: Machine Learning-2

  • Handling Unstructured Data

  • Machine Learning Algorithms

  • Bias Variance Trade-o

  • Handling Unbalanced Data

  • Boosting

  • Model Validation


Module 5: Time Series Forecasting

  • Introduction to Time Series

  • Correlation

  • Forecasting

  • Autoregressive Models

Module 6: Optimization Techniques (Self-Paced)

  • Linear Programming

  • Goal Programming

  • Integer Programming

  • Mixed Integer Programming

  • Distribution and Network Models

DOMAIN EXPOSURE

Module 1: Demystifying ChatGPT and Applications (Self-Paced)

  • Overview of ChatGPT and OpenAI

  • Timeline of NLP and Generative AI

  • Frameworks for Understanding ChatGPTand Generative AI

  • Implications for Work, Business, and Education

  • Output Modalities and Limitations

  • Business Roles to Leverage ChatGPT

  • Prompt Engineering for Fine-Tuning Outputs

  • Practical Demonstration and Bonus Section on RLHF


Module 2: Marketing and Retail Analytics

  • Marketing and Retail Terminologies

  • Customer Analytics

  • KNIME

  • Retail Dashboard

  • Customer Churn

  • Association Rules Mining


Module 3: Web and Social Media Analytics

  • Web Analytics: Understanding the Metrics

  • Basic and Advanced Web Metrics

  • Google Analytics: Demo and Hands-On

  • Campaign Analytics

  • Text Mining


Module 4: Finance and Risk Analytics

  • Why Credit Risk: Using a Market Case Study

  • Comparison of Credit Risk Models

  • Overview of Probability of Default (PD) Modeling

  • PD Models, Types of Models, Steps to Make a Good Model

  • Market Risk

  • Value at Risk: Using Stock Case Study


Module 5: Supply Chain and Logistics Analytics

  • Introduction to Supply Chain

  • RNNs and its Mechanisms

  • Designing an Optimal Strategy Using Case Study

  • Inventory Control and Management

  • Inventory Classification

  • Inventory Modeling

  • Costs Involved in Inventory

  • Economic Order Quantity

  • Forecasting

  • Advanced Forecasting Methods

  • Examples and Case Studies

VISUALIZATION AND INSIGHTS

Module 1: Data Visualization Using Tableau

  • Introduction to Data Visualization

  • Introduction to Tableau

  • Basic Charts and Dashboard

  • Descriptive Statistics, Dimensions and Measures

  • Visual Analytics: Storytelling through Data

  • Dashboard Design and Principles

  • Advanced Design Components/ Principles: Enhancing the Power of Dashboards

  • Special Chart Types

  • Case Study: Hands-On Using Tableau

  • Integrate Tableau with Google Sheets

AIML Pathway

PGP-AIML CURRICULUM

The curriculum of the PGP in Artificial Intelligence & Machine Learning has been updated in consultation with industry experts, academicians & program alums to ensure you learn the most cutting-edge topics:

Python and GenAI Prep Work

  • Python Bootcamp for Non-programmers

  • Python Prep Work

  • Generative AI Prep Work

Course 1: Introduction to Python

  • Python Programming

  • Python for Data Science

  • Exploratory Data Analysis (EDA)

  • Analyzing Text Data


Course 2: Machine Learning

  • Linear Regression

  • Decision Trees

  • K-Means Clustering

Course 3: Advanced Machine Learning

  • Bagging

  • Boosting

  • Model Tuning

Course 4: Introduction to Neural Networks

  • Introduction to Neural Networks

  • Optimising Neural Networks

Course 5: Natural Language Processing with Generative AI

  • Word Embeddings

  • Attention Mechanism and Transformers

  • Large Language Models and Prompt Engineering

  • Retrieval Augmented Generation

Course 6: Introduction to Computer Vision

  • Image Processing

  • Convolutional Neural Networks

Course 7: Model Deployment

  • Introduction to Model Deployment

  • Containerisation

Course 8: Introduction to SQL

  • Querying Data With SQL

  • Advanced Querying

  • Enhancing Query Proficiency

Course 9:Applied Statistics

  • Inferential Statistics Foundations

  • Estimation and Hypothesis Testing

  • Common Statistical Tests

Course 10: Advanced Machine Learning and MLOPS

  • Model Interpretability

  • Introduction to MLOps and DevOps

  • Building ML Pipelines

Course 11:Advanced Generative AI for Natural Language Processing

  • AI Assistant Development

  • Fine-tuning LLMS

Course 12:Capstone

Additional Modules (Learn at your own Pace)

Course 1: Introduction to Data Science and AI Course 2: Multimodal Generative AI

Course 3: Recommendation Systems

Course 4: Object Detection and Segmentation

Course 5: Reinforcement Learning

Course 6: Time Series Forecasting


Second Year: Masters of Data Science (Global)

TRIMESTER 1

ENGINEERING AI SOLUTIONS

LEARNING OUTCOMES OF THIS UNIT:

  • Explain the process and key characteristics of developing an AI solution, and the contrast with traditional software development, to inform a range of stakeholders

  • Design, develop, deploy, and maintain AI solutions utilising modern tools, frameworks, and libraries

  • Apply engineering principles and scientific method with appropriate rigour in conducting experiments as part of the AI solution development process

  • Manage expectations and advise stakeholders on the process of operationalising AI solutions from concept inception to deployment and ongoing product maintenance and evolution


MATHEMATICS FOR ARTIFICIAL INTELLIGENCE


LEARNING OUTCOMES OF THIS UNIT:

  • Explain the role and application of mathematical concepts accociated with artificial intelligence

  • Identify and summarise mathematical concepts and technique covered in the unit needed to solve mathematical problems from artificial intelligence applications

  • Verify and critically evaluate results obtained and communicate results to a range of audiences 

  • Read and interpret mathematical notation and communicate the problem-solving approach used

TRIMESTER 2

MACHINE LEARNING

LEARNING OUTCOMES OF THIS UNIT:

  • Use Python for writing appropriate codes to solve a given problem

  • Apply suitable clustering/dimensionality reduction techniques to perform unsupervised learning on unlabelled data in a real-world scenario

  • Apply linear and logistic regression/classification and use model appraisal techniques to evaluate develop models

  • Use the concept of KNN (k-nearest neighbourhood) and SVM (support vector machine) to analyse and develop classification models for solving real-world problems

  • Apply decision tree and random forest models to demonstrate multi-class classification models 

  • Implement model selection and compute relevant evaluation measure for a given problem


MODERN DATA SCIENCE


LEARNING OUTCOMES OF THIS UNIT:

  • Develop knowledge of and discuss new and emerging fields in data science

  • Describe advanced constituents and underlying theoretical foundation of data science

  • Evaluate modern data analytics and its implication in real-world applications

  • Use appropriate platform to collect and process relatively large datasets

  • Collect, model and conduct inferential as well predictive tasks from data


TRIMESTER 3

REAL-WORLD ANALYTICS

LEARNING OUTCOMES OF THIS UNIT:

Apply knowledge of multivariate functions data transformations and

data distributions to summarise data sets

  • Analyse datasets by interpreting summary statistics, model and function parameters

  • Apply game theory, and linear programming skills and models, to make optimal decisions

  • Develop software codes to solve computational problems for real world analytics

  • Demonstrate professional ethics and responsibility for working with real world data



DATA WRANGLING

LEARNING OUTCOMES OF THIS UNIT:

  • Undertake data wrangling tasks by using appropriate programming and scripting languages to extract, clean, consolidate, and store data of different data types from a range of data sources

  • Research data discovery and extraction methods and tools and apply resulting learning to handle extracting data based on project needs

  • Design, implement, and explain the data model needed to achieve project goals, and the

processes that can be used to convert data from data sources to both technical and

non-technical audiences

  • Use both statistical and machine learning techniques to perform exploratory analysis on

data extracted, and communicate results to technical and non-technical audiences

  • Apply and reflect on techniques for maintaining data privacy and exercising ethics in data handling


Work on 11 hands-on projects

Dive into Data Science, AI and Machine Learning projects to sharpen skills and build a unique portfolio

  • 11

    hands-on projects

  • 60+

    case studies

  • 22+

    domains

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FOOD AND BEVERAGES

Customer Demand Insights for FoodHub Restaurants

About the Project

Conduct exploratory data analysis to assess demand for restaurants and cuisines, providing insights to enhance customer experience and boost business for a food aggregator.

Skills you will learn

  • Python
  • Numpy
  • Pandas
  • Seaborn
  • Univariate Analysis
  • Bivariate Analysis
  • Exploratory Data Analysis
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BFSI

Loan Purchase Prediction from Marketing Campaign Data

About the Project

Analyze historical marketing campaign data of a bank and build a machine learning model that will help to identify the customers of a bank who are exposed to a marketing campaign and have a higher probability of purchasing a loan.

Skills you will learn

  • Exploratory Data Analysis
  • Decision Trees
  • Pruning
  • Scikit-Learn
  • Pandas
  • Seaborn
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Immigration

Predictive Model for Visa Status Determination

About the Project

Analyze visa applicant data to build a predictive model that identifies key factors influencing visa status and recommends whether an applicant should be approved or denied.

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost
  • Gradient Boosting
  • XGBoost
  • GridSearchCV
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Energy

Optimizing Wind Turbine Maintenance with Neural Network Models

About the Project

Analyze data from a wind energy provider to build neural network models that predict equipment failures, enabling timely repairs and reducing maintenance costs

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Tensorflow
  • Keras
  • Artificial Neural Networks
  • Regularization
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BFSI

Optimizing Investment Strategies with Stock News Sentiment Analysis

About the Project

Develop an AI-driven sentiment analysis system to process stock news and prices, gauge market sentiment, and provide weekly summaries to help financial analysts optimize investment strategies and enhance client outcome

Skills you will learn

  • Exploratory Data Analysis
  • Word Embeddings
  • Transformers
  • Sentence Transformers
  • Large Language Models
  • Prompt Engineering
  • Sentiment Analysis
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Agriculture

Plant Seedling Classification using CNN

About the Project

Build a robust image classifier using CNNs to efficiently classify different plant seedlings and weeds to maximise crop yields and minimize human involvement.

Skills you will learn

  • Image Processing
  • Image Classification
  • Keras
  • Tensorflow
  • Convolutional Neural Networks
  • Transfer Learning
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BFSI

Churn Prediction for Credit Card Users Using Machine Learning

About the Project

Analyze historical customer data, build a predictive model that predicts whether or not a customer will discontinue using a bank's credit card services, and identify the key factors affecting the customer’s decision

Skills you will learn

  • Exploratory Data Analysis
  • Random Forest
  • Hyperparameter Tuning
  • Scikit-Learn
  • Model Deployment
  • Docker
  • Flask
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Retail

Exploring Vehicle Resale Trends Through Data Analysis

About the Project

Analyze a vehicle resale company's listing and customer feedback data, answer business questions, and provide recommendations for the leadership to enable data-driven decision-making.

Skills you will learn

  • Querying Data
  • SQL Functions
  • Data Aggregation
  • Joins
  • Subqueries
  • Window Functions
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News and Media

Statistical Analysis of Landing Page Effectiveness for Subscriber Growth

About the Project

Explore the data provided and perform statistical analysis to decide whether the new landing page of an online news portal is effective enough to gather new subscribers as compared to the old one.

Skills you will learn

  • Data Visualization
  • Exploratory Data Analysis
  • Hypothesis Testing
  • A/B Testing
  • Statistical Inference
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Hospitality

Building a Predictive Model for Hotel Booking Cancellations

About the Project

Analyze INN Hotels data to identify factors influencing booking cancellations, build a predictive model to forecast cancellations, and support the creation of profitable cancellation and refund policies

Skills you will learn

  • Exploratory Data Analysis
  • Data Preprocessing
  • Logistic regression
  • Multicollinearity
  • Decision trees
  • Pruning
  • Feature Importance
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Retail

Sentiment Analysis for Product Improvement in E-commerce

About the Project

Enhance product offerings by performing aspect-based sentiment analysis on e-commerce customer reviews to extract sentiments for individual product components and identify improvement areas

Skills you will learn

  • Exploratory Data Analysis
  • Transformers
  • Large Language Models
  • Prompt Engineering
  • Parameter-Efficient Fine Tuning

Master in-demand Data Science and AI tools

Gain hands-on experience with 22+ top Data Science & AI tools to optimize models and build innovative solutions

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    Python

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    SQL

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    NumPy

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    Pandas

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    Seaborn

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    Skitlearn

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    Keras

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    Tensorflow

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    Transformers

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    ChatGPT

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    OpenCV

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    SpaCy

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    LangChain

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    Docker

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    Flask

  • tools-icon

    Whisper

  • tools-icon

    ML Flow

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    Github

  • tools-icon

    Gemini

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    Dall.E

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    Matplotlib

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    Statsmodels

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    Hugging Face

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    Tensorflow

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    R

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    Gradio

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    Tableau

  • And More...

Earn a Degree and PG certificates from world's leading institutions

  • Earn a globally recognized Master of Data Science degree from Deakin University.

  • Get PG Certificates in “AI and Machine Learning” or “Data Science and Business Analytics” from McCombs School of Business at The University of Texas at Austin and Great Lakes Executive Learning.

  • Stand out globally with an AQF level 9 degree from Deakin University, officially recognised through Australian Higher Education Graduation Statement (AHEGS).

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* Image for illustration only. Certificate subject to change.

Meet your faculty

Meet our expert faculty with in-depth Data Science & AI knowledge and a passion to help you succeed

Masters Faculty
PGP-AIML/PGP-DSBA Faculty
  • Dr. Sutharshan Rajasegarar - Faculty Director

    Dr. Sutharshan Rajasegarar

    Senior Lecturer in Computer Science Course Director Master of Data Science

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  • Dr. ye Zhu - Faculty Director

    Dr. ye Zhu

    Senior Lecturer, Computer Science

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  • Dr. Bahareh Nakisa - Faculty Director

    Dr. Bahareh Nakisa

    Senior Lecturer, Applied Artificial Intelligence

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  • Dr. Asef Nazari - Faculty Director

    Dr. Asef Nazari

    Senior Lecturer, Applied Artificial Intelligence

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  • Gang Li - Faculty Director

    Gang Li

    Professor, School of Info Technology

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  • Dr. Marek Gagolewski - Faculty Director

    Dr. Marek Gagolewski

    Senior Lecturer, Applied Artificial Intelligence

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  • Maia Angelova Turkedjieva - Faculty Director

    Maia Angelova Turkedjieva

    Professor, Real-World Analytics

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  • Dr. Kumar Muthuraman - Faculty Director

    Dr. Kumar Muthuraman

    Faculty Director, Center for Analytics and Transformative Technologies, McCombs School of Business, the University of Texas at Austin

    Faculty Director, Center for Analytics and Transformative Technologies

    21+ years' experience in AI, ML, Deep Learning, and NLP.

    Know More
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  • Dr. Abhinanda Sarkar - Faculty Director

    Dr. Abhinanda Sarkar

    Senior Faculty & Director Academics, Great Learning

    30+ years of experience in data science, ML, and analytics.

    Ph.D. from Stanford, taught at MIT, ISI, and IIM Bangalore.

    Know More
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  • Mr. R Vivekanand - Faculty Director

    Mr. R Vivekanand

    Co-Founder and Director

    Expert in data visualization and marketing econometrics with 10+ years

    Qualified Tableau trainer passionate about teaching business analytics

    Know More
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  • Prof. Raghavshyam Ramamurthy - Faculty Director

    Prof. Raghavshyam Ramamurthy

    Industry Expert in Visualization

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  • Dr. Daniel A Mitchell - Faculty Director

    Dr. Daniel A Mitchell

    Clinical Assistant Professor, Department of Information, Risk & Operations Management, McCombs School of Business, The University of Texas at Austin

    Research Director, Center for Analytics and Transformative Technologies

    15+ years of experience in financial engineering and quantitative finance.

    Know More
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  • Dr. Pavankumar Gurazada - Faculty Director

    Dr. Pavankumar Gurazada

    Senior Faculty, Academics, Great Learning

    15+ years of experience in marketing, digital marketing, and machine learning.

    Ph.D. from IIM Lucknow; MBA from IIM Bangalore; IIT Bombay graduate.

    Know More
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Get industry ready with dedicated career support*

*Note: Provided by Great Learning

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    Resume Building Sessions

    Build your resume to highlight your skill-set along with your previous academic and professional experience.

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    Access to curated jobs

    Access a list of jobs relevant to your experience and domain.

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    Interview preparation

    Learn to crack technical interviews with our interview preparation sessions.

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    Career Guidance

    Get access to career mentoring from industry experts. Benefit from their guidance on how to build a rewarding career.

Course fees

The course fee is 8,500 USD

Invest in your career

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    Develop a deep understanding of the Data Science landscape

  • benifits-icon

    Master essential tools like Python to solve real-world business problems

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    Master Data Science, Deep Learning, Analytics, and GenAI to drive strategic decisions

  • benifits-icon

    Secure your dream career in Data Science with our dedicated career support

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Easy payment plans

Avail our EMI options & get financial assistance

Payment Partners

Check our different payment options with trusted partners

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*Subject to partner approval based on applicable regions & eligibility

Take the next step

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Unlock exclusive course sneak peek

Application Closes: 30th Jun 2025

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Application Closes: 30th Jun 2025

Talk to our advisor for offers & course details

Schedule a call

Admission Process

Admissions close once the required number of participants enroll. Apply early to secure your spot

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    1. Apply

    Fill out an online application form

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    2. Get Reviewed

    Go through a screening call with the Admission Director’s office.

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    3. Join the program

    Your profile will be shared with the Program Director for final selection

Course Eligibility

  • Applicant must meet Deakin’s minimum English Language requirement.
  • Candidates should have a bachelors degree (minimum 3-year degree program) in a related discipline OR a bachelors degree in any discipline with at least 2 years of work experience

Limited seats available

Apply today and become a data science expert

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Become a Data Science Expert

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Got more questions? Talk to us

Connect with a program advisor and get your queries resolved

Speak with our expert +1 512 890 1269 or email to deakin.mds@mygreatlearning.com

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Master of Data Science (Global) Program - Deakin University, Australia

Deakin University in Australia offers its students a top-notch education, outstanding employment opportunities, and a superb university experience. Deakin is ranked among the top 1% of universities globally (Shanghai Rankings) and is one of the top 50 young universities worldwide.

The curriculum designed by Deakin University strongly emphasizes practical and project-based learning, which is shaped by industry demands to guarantee that its degrees are applicable today and in the future.

The University provides a vibrant atmosphere for teaching, learning, and research. To ensure that its students are trained and prepared for the occupations of tomorrow, Deakin has invested in the newest technology, cutting-edge instructional resources, and facilities. All students will obtain access to their online learning environment, whether they are enrolled on campus or studying only online.

Why pursue the Data Scientist Masters Program at Deakin University, Australia?

The Master's Degree from Deakin University provides a flexible learning schedule for contemporary professionals to achieve their upskilling requirements. In addition, you can:

  • Get a globally recognized Master's Degree from Deakin University and a Post Graduate Certificate from UT Austin.
  • Learn Data Science and Business Analytics from reputed faculty through live, interactive online sessions.
  • Develop your skills with a curriculum created by eminent academicians and industry professionals.
  • Gain practical knowledge and skills by engaging in project-based learning.
  • Become market-ready with mentorship sessions from industry experts.
  • Learn alongside a diverse group of peers and professionals for a rich learning experience.
  • Secure a Global Data Science Master’s Degree at 1/10th the cost of a 2-year traditional Master’s program.

Benefits of Deakin University Master’s Course of Data Science (Global)

Several benefits are offered throughout this online Data Science degree program, which include:

PROGRAM STRUCTURE
Deakin University's Master of Data Science (Global) Program has a modular structure that separates the curriculum into basic and advanced competency tracks, allowing students to master advanced Data Science skills alongside Business Analytics.

INDUSTRY EXPOSURE
Candidates gain exposure and insights through industry workshops and competency classes led by world-class industry experts and faculty.

WORLD-CLASS FACULTY
The faculty's extensive experience in both academia and industry enables them to effectively teach the most current, in-demand skills.

CAREER ENHANCEMENT SUPPORT
The program offers career development support through workshops and mentorship sessions to help applicants identify their strengths and career paths.

The Advantage of Great Learning in this Deakin University’s Master of Data Science Course

Great Learning is the world’s leading ed-tech platform providing industry-relevant programs for professional learning and higher education. This course gives you access to a comprehensive network of industry experts and committed career support.

E-PORTFOLIO
An e-portfolio illustrates the skills you have acquired and the knowledge you have gained, which can be shared on social media to showcase your expertise to potential employers.

CAREER SUPPORT
The course provides students with career support, empowering them to advance their professions.

RESUME BUILDING AND INTERVIEW PREPARATION
The program assists in creating a resume that highlights your abilities and work experience, while interview preparation workshops help you ace your interviews.

CAREER MENTORSHIP
Access personalized career mentorship sessions from highly skilled industry experts who guide you towards developing a lucrative career.

Eligibility for Master of Data Science (Global) Program

The following are the requirements for program eligibility:

  • Interested candidates must hold a bachelor's degree (minimum 3-year program) in a related field or a bachelor's degree in any discipline with at least 2 years of professional work experience.
  • Candidates must meet Deakin University’s minimal English language requirement.