Certified Data Scientist

Create robust predictive models with statistics and Python programming. Build confidence and credibility to tackle complex machine learning problems on the job.

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Learn more about our HRD Corp Signature Programmes and how we can help your organisation reach its transformative goals.

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About this course

Demand for data scientists has increased 663% in five years, and the call for machine learning skills is up 809%.



500+ Students


  • Analysts have the opportunity to derive insights and build predictive models for business needs ranging from customer segmentation to personalised product recommendations. In this training, participants build on Level 1 Data Skills to practice advanced analytics and explore machine learning in Python.



  • This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics. Upon enrolling, you’ll complete a short onboarding task and, based on your results, may be advised to take an introductory Python workshop.



  • Analysts or engineers who want to break into data science. Managers who need to work with technical teams and want to more effectively communicate and empathise with them.



  • Level 1 Data Skills: Wrangle, explore, model, and communicate the results of multiple analyses with Python and its many packages
  • Level 2 Data Skills: Work on advanced analytics data sets and explore the capabilities of machine learning.




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

Premkumar Chandrashegaran

Data Science Instructor

Premkumar Chandrashegaran

Data Science Instructor

Avinnaash Suresh

Data Science Instructor

Avinnaash Suresh

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

Explore the essentials of Python for data science and applied math through a series of self-paced online preparatory lessons.

• Define basic Python programming concepts and
data types, including variables, lists, dictionaries,
loops, and functions.
• Create functions that accept multiple arguments
and return multiple values.
• Understand the purpose of iterators in real-world
data science workflows.
• Describe the use and purpose of DataFrames
and how they can be used to manipulate data
with Pandas.
• Plot visualizations with Matplotlib and Seaborn.
• Get acquainted with descriptive and inferential
statistics and how to calculate them.
• Calculate combinations and permutations.
• Familiarize yourself with developer tools for data
science, including GitHub basics and working with
the command line.
• Calculate linear algebra and regression equations.

Discover the fundamentals of evidential science by executing basic functions in Python.

What Is Data Science?

  • Define the workflow, tools, and approaches data scientists use to analyze data.
  • Apply the Data Science Workflow to solve a task.

Your Development Environment

  • Navigate through directories using the command line.
  • Use Git and GitHub to share repositories.

Python Foundations

  • Conduct arithmetic and string operations in Python.
  • Assign variables.
  • Implement loops and conditional statements.
  • Use Python to clean and edit data sets.

Project: Complete coding challenges that often appear in data science job interviews, further developing your Python programming skills.

Practice exploratory data analysis for cleaning and aggregating data, and understand the basic statistical testing values of your data.

Exploratory Data Analysis in Panda

  • Use DataFrames and Series to read data.
  • Rename, remove, combine, select, and JOIN data.
  • Identify and handle null and missing values.

Data Visualization in Python

  • Define key principles of data visualization.
  • Create line plots, bar plots, histograms, and box plots using Seaborn and Matplotlib.

Statistics in Python

  • Use NumPy and Pandas libraries to analyze data sets using basic summary statistics.
  • Create data visualizations to discern characteristics and trends in a data set.
  • Identify a normal distribution within a data set using summary statistics and visualization.

Experiments and Hypothesis Testing

  • Determine causality and sampling bias.
  • Test a hypothesis using a sample case study.
  • Validate your findings using statistical analysis (e.g., p values, confidence intervals).


Project: Apply your growing Python and analytical skills to conduct a basic exploratory data analysis and answer questions about a real-world data set.

Branch from traditional statistics into machine learning and explore supervised learning techniques including classification and regression.

Linear Regression

  • Define data modeling and linear regression.
  • Differentiate between categorical and continuous variables.
  • Build a linear regression model for prediction using the scikit-learn library.

Train/Test Split

  • Describe errors of bias and variance.
  • Define overfitting and underfitting.
  • Explore k-folds, LOOCV, and three-split methods.

KNN and Classification

  • Build a k-nearest neighbors model using the scikit-learn library.
  • Evaluate and tune the model using metrics such as classification accuracy/error.

Logistic Regression

  • Build a logistic regression classification model using the scikit learn library.
  • Describe the sigmoid function, odds, and odds ratios and how they relate to logistic regression.
  • Evaluate a model using metrics such as classification accuracy/error, confusion matrix, ROC/AOC curves, and loss functions.


Project: Build and validate linear regression and KNN models based on a provided data set.

Learn and implement core machine learning models to evaluate complex problems.

Working With API Data

  • Access public APIs and get information back.
  • Read and write data in JSON.
  • Use the requests library.

Natural Language Processing

  • Demonstrate how to tokenize natural language text.
  • Categorize and tag unstructured text data.
  • Perform text classification model using scikit-learn, CountVectorizer, TfidfVectorizer, and TextBlog.

Time Series Data

  • Create rolling means and plot time series data.
  • Examine autocorrelation on time series data.

Flex Sessions

  • Explore an additional data science topic based on class interest. Options include: clustering, decision trees, robust regression, and deploying models with Flask.

Pricing & Payment Plans


from as low as

RM -/month

Full Tuition

RM 7,500

excluding admin fees and 6% SST

Frequently Asked Questions

Companies of all stripes use data science to take on today’s biggest challenges, tackling everything from public policy and robotics to dating and eCommerce. As a result, 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.

According to Burning Glass, “Data science and analytics skills are now widely in demand in decision-making roles, including managers across a range of industries. In fact, our data shows that more than 1.7 million job postings asked for data science skills in 2018.” Learning this future-proof skill set can help you enter the next stage of your career, whether that’s advancing in your current profession or exploring an exciting and lucrative field.

This course is designed for data professionals who want to perform complex analysis to power predictions and add marketable skills to their resume. You’ll find a diverse range of students in the classroom: 

Data analysts, marketing analysts, BI analysts, or consultants who work with big data and need to upgrade their skills. Software engineers who want to apply their programming skills toward a new career. Other professionals with a quantitative background eyeing a transition to tech.

Ultimately, this programme attracts a community of eager learners who have an interest in manipulating large data sets and forecasting to impact strategy and bottom lines.

Our instructors represent the best and brightest data professionals from top companies like Atlassian, Capital One, and Deloitte. They combine in-depth experience as practitioners with a passion for nurturing the next generation of talent.

We work with a large pool of experienced instructors around the world.

Here are just some of the benefits you can expect as a GA student:

  • 60 hours of expert instruction designed to build a well-rounded foundational data science skill set.

  • Up to 25 hours of self-paced pre-work to brush up on programming fundamentals and statistics.

  • Robust data science coursework, including expert-vetted lesson decks, lab materials, and more. Refresh and refine your knowledge throughout your professional journey as needed.

  • A portfolio-ready capstone project built with support from your instructor.

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

  • A data science 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 data science field.

This is a fast-paced course with some prerequisites. Students should be comfortable with programming fundamentals, core Python syntax, and basic statistics.

Upon enrolling, you’ll complete a short onboarding task and, based on your results, may be advised to take an introductory Python workshop. You’ll also complete up to 25 hours of online preparatory lessons that will ensure you have the foundations to dive into rigorous coursework.

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

Yes! Upon passing this course, you will receive a signed certificate. Thousands of GA alumni use their data science certificate to demonstrate skills to employers and their LinkedIn networks. GA’s Data Science course is well-regarded by many top employers, who contribute to our curriculum and use our data courses to train their own teams.

Yes! All of our part-time courses are designed for busy professionals with full-time work commitments. 

You will be expected to spend time working on homework and projects outside of class hours each week, but the workload is designed to be manageable with a full-time job.

If you need to miss a session or two, we offer resources to help you catch up. We recommend you discuss any planned absences with your instructor.

For your capstone project, you’ll apply machine learning techniques to solve a real-world problem. You’ll develop a model, technical documentation, and stakeholder presentation, and graduate with a polished, portfolio-ready data science project to showcase your skills. We encourage you to tackle a problem that’s related to your work or a passion project you’ve been meaning to carve out time for.

Throughout the course, you’ll also complete a number of smaller projects designed to reinforce what you’ve learned in each unit.

This Data Science course assumes some prerequisite knowledge and is designed for professionals who already work with data and want to perform more complex analysis involving computation.

If you’re searching for a more entry-level course, Data Analytics teaches beginners how to perform rigorous analysis with Excel, SQL, and Tableau.

For those committed to a career change, the full-time Data Science Immersive programme provides the most direct pathway to data science and other advanced analytics roles.

It’s up to you! Our Remote courses offer a learning experience that mirrors GA’s on-campus offerings but allow you to learn from the comfort of home. If you have a busy travel schedule, or just want to save yourself the commute, a Remote course could be a good option for you. You’ll still get access to the expert instruction, learning resources, and support network that GA is known for.

If you prefer to learn alongside your peers and can make it to campus twice a week, our in-person courses allow you to take advantage of our beautiful classrooms and workspaces.

Our Admissions team can advise you on the best format for your personal circumstances and learning style.

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