Data Science Immersive

Your best course for career transformation. This full-time, award-winning data science course is designed to help you launch a career in one of the most in-demand fields today.

Dig Deeper Into The Curriculum
Data Science Immersive

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About this course

Consistently topping LinkedIn’s Emerging Jobs Report, data science roles have seen a 37% year-on-year increase in demand.

Advanced
Course

Individualized
Instructor
Support

500+ Students
Enrolled

SOLVE THE WORLD'S MOST INTERESTING PROBLEMS

  • Ranked as one of the Best Data Science Bootcamps by SwitchUp, this course teaches students to dive deeper into advanced analytics, statistical modelling, and machine learning in Python. Harness the predictive power of data to begin a career in one of the most exciting fields today and work at the forefront of diverse industries.

 

REFINE YOUR DATA SCIENCE SKILLS TO THE NEXT LEVEL

  • This is an intermediate-level 12-week online course with some prerequisites. Students are recommended to have a strong mathematical foundation and familiarity with Python and programming fundamentals. Students are given Data Science Fundamentals online pre-work to prepare for class.

 

DELIVER A PROFESSIONAL CAPSTONE PROJECT

  • Showcase your skills in predictive modelling, pattern recognition, and data visualisation, wrangling massive data sets to forecast trends and inform strategy.

 

CONNECT WITH INDUSTRY EXPERTS

  • Tap into GA's growing global network of tech experts, instructors, hiring partners, and alumni, and equip yourself to succeed in a rapidly expanding field.

 

 

 

What you'll learn

Complete hands-on exercises with access to real-world data sets to reinforce new skills
Create a portfolio project based off a real-world data problem
Explore the the process of processing data to uncover future propositions to power business

Check out our elite team of instructors

Avinnaash Suresh

Data Science Instructor

Avinnaash Suresh

Data Science Instructor

Shilpa Sindhe

Data Science Instructor

Shilpa Sindhe

Data Science Instructor

Darryl Ma

Data Science Instructor

Darryl Ma

Data Science Instructor

These experts bring in-depth experience from the field to the classroom each day, providing invaluable insights into succeeding on the job.

GA instructors* are committed to providing personalised feedback and support to help you gain confidence with key concepts and tools.

*GA instructors are subject to their availability

19K+ Premier Hiring Partners From Around the World

Course Outline

Dive into a series of self-paced lessons on the essentials of Python programming and applied math for data science before the course begins.

• Explore fundamental Python programming
concepts, including variables, lists, loops,
dictionaries, and data sets.
• Leverage programming tools like GitHub and
the command line interface to manage data
science projects.
• Practice solving coding challenges similar
to the questions used in task-based data
science interviews.
• Write and run Python functions using
multiple arguments.
• Discover how key math concepts like statistical
significance and probability distribution are
applied throughout data science.

Get acquainted with essential data science tools and techniques, working in a programming environment to gather, organize, and share projects and data with Git and UNIX.

• Demonstrate familiarity with introductory
programming concepts using Python and NumPy
to navigate data sources and collections.
• Utilize UNIX commands to navigate file systems
and modify files.
• Learn to track changes and iterations using Git
version control from your terminal.
• Define and apply descriptive statistical
fundamentals to sample data sets.
• Practice plotting and visualizing data using Python
libraries like Matplotlib and Seaborn.

Project: Apply NumPy and Python programming skills
to answer questions based on a clean data set.

Perform exploratory data analysis. Generate visual and statistical analyses, using Python and its associated libraries and tools to approach problems in fields like finance, marketing, and public policy.

• Design an experimental study with a well-thought-
out problem statement and data framework
• Use Pandas to read, clean, parse, and plot data,
extracting and rearranging data through indexing,
grouping, and JOINing.
• Review statistical testing concepts (p values,
confidence intervals, lambda functions,
correlation/causation) with SciPy and StatsModels.
• Learn to scrape website data using popular
scraping tools.
• Explore bootstrapping, Resampling and building
inferences about your data.

Project: Leverage Pandas to apply advanced NumPy
and Python skills cleaning, analyzing, and testing data
from multiple messy data sets.

Explore effective study design and model evaluation and optimization, implementing linear and logistic regression, and classification models. Collect and connect external data to add nuance to your models using web scraping and APIs.

• Use scikit-learn and StatsModels to run linear
and logistic regression models and learn to
evaluate model fit.
• Begin to look at classification models by
implementing the k-nearest neighbors (kNN)
algorithm.
• Articulate the bias-variance trade-off as you
practice evaluating classical statistical models.
• Use feature selection to deepen your knowledge of
study design and model evaluation.
• Learn to apply optimization and regularization for
fitting and tuning models.
• Dive into the math and theory behind how gradient
descent helps to optimize loss functions for
machine learning models.

Project: Explore, clean, and model data based on
a provided data set, outlining your strategy and
explaining your results.

Build machine learning models. Explore the differences between supervised and unsupervised learning via clustering, natural language processing, and neural networks.

• Define clustering and its advantages
and disadvantages as compared to
classification models.
• Build and evaluate ensemble models using decision
trees, random forests, bagging, and boosting.
• Get acquainted with natural language processing
(NLP) through sentiment analysis of scraped
website data.
• Learn how Naive Bayes can simplify the process of
analyzing data for supervised learning algorithms.
• Explore the history and use of Hadoop, as well as
the advantages and disadvantages of using parallel
or distributed systems to store, access, and analyze
big data.
• Understand how Hive interacts with Hadoop
and discover Spark’s advantages through big data
case studies.
• Analyze and model time series data using the
ARIMA model.

Project: Students will scrape and model their own data
using multiple methods, outlining their approach and
evaluating any risks or limitations.

Dive deeper into recommender systems, neural networks, and computer vision models, implementing what you’ve learned to productize models.

• Compare and contrast different types of neural
networks and demonstrate how they are fit with
back propagation.
• Build and apply basic recommender systems in
order to predict on sample user data.
• Work with career coaches to create and polish
your professional portfolio.
• Practice with data science case studies to prepare
for job interviews.

Project: Choose a data set to explore and model,
providing detailed notebook of your technical
approach and a public presentation on your findings.

Pricing & Payment Plans

Installments

from as low as

RM -/month

excluding admin fees and 6% SST

*Discounts available for first-time self-paying individuals.

Full Tuition

RM 25,000

excluding admin fees and 6% SST

*Discounts available for first-time self-paying individuals.

Employer Sponsorship

Our courses are HRD Corp-claimable, so you can receive reimbursement of your tuition fees from your employer.

Need a more flexible payment option?

Instalment plans available for self-paying individuals.

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Frequently Asked Questions

It’s ranked among the top fields in LinkedIn’s Emerging Jobs Report for three years running and, according to the latest numbers, data scientist roles are experiencing 37% annual growth. Across industries from information technology and software to financial services and higher education, organisations are moving quickly to build robust in-house teams of data scientists and advanced analysts, and there’s not enough talent to go around.

For those with quantitative backgrounds or existing analytical skills, building out this specialised skill set can spell ample opportunity to secure a high-paying job in data science, advanced analytics, business intelligence, and more.

DSI students come from all walks of life but share one common mission: They are passionate about launching a career in data science or advanced analytics. We see career-changers from diverse professional backgrounds, including engineers and recent STEM graduates, mid-career marketing and financial analysts, and business strategists, as well as more those from more far-flung fields like sales and the law.

This is an intermediate-level course with some prerequisites. We recommend that students arrive with a strong mathematical foundation and familiarity with Python and programming fundamentals. Some students have engaged in self-learning or have some technical background, such as a degree in mathematics or computer science or work experience in research or analysis. 

Upon enroling, you’ll complete a series online preparatory lessons, diving into the essentials of the Python programming language and applied math before the course begins.

Our Admissions team can discuss your background and learning goals to advise if this Immersive is a good fit for you.

Yes! Upon passing this course, you will receive a signed certificate of completion. Thousands of GA alumni use their course certificate to demonstrate skills to potential employers — including our 19K+ hiring partners — along with their LinkedIn networks. GA’s data programmes are well-regarded by many top employers, who contribute to our curriculum and partner with us to train their own teams.

Here are just some of the benefits full-time students can expect at GA:

  • Expert instruction in the skills you need to successfully transition into a data science career.

  • Self-paced pre-work to explore data science fundamentals and prepare to hit the ground running on day one of class.

  • Robust coursework, including expert-vetted lesson decks, project toolkits, and more. Refresh and refine your knowledge throughout your professional journey as needed.

  • A professional-grade portfolio to showcase your ability to solve real-world data problems to potential employers and collaborators.

  • Individual feedback and guidance from instructors and TAs during office hours. Stay motivated and make the most of your experience with the help of AGA’s dedicated team.

  • Exclusive access to alumni discounts, networking events, and career workshops.

  • A GA course certificate to showcase your new skill set on LinkedIn.

  • Connections with a professional network of instructors and peers that lasts well beyond the course. The global GA community can help you navigate and succeed in the field.

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