Programme Outline

Students will learn concepts via pre-prepared Jupyter Notebooks which serve as interactive textbooks, where they compose their Python code alongside conceptual notes. Concepts covered are documented in sections of the Jupyter Notebook alongside sections which allow the execution of Python code. Students will be introduced to variables and basic data types in Python, conditions and conditional statements, and loops.

Students will appreciate functions as tools which allow programmers to enhance the readability and reusability of their code. In the process, they will develop their computational thinking skills of decomposition and abstraction.

A comprehensive Multiple Choice Questionnaire will be conducted for students to gauge their progress status.

The highlight of the BootCamps is learning how to code a virtual DeepRacer car. All students will be provided with an AWS starter account which they can use to access the DeepRacer console, a graphical interface for training their reinforcement learning models.

A DeepRacer league champion engaged by AWS will share tips and practical strategies for programming the DeepRacer.

After successfully training and evaluating models in simulations, students will get a chance to run their code on a physical DeepRacer. The race is run as a time-tap where each team will get several runs on teh track thus being able to tweak their code after each run. We will take the best time from each team for selection of the finalists!.

Conducted in a similar fashion to the Singapore Grand Prix, the 8 fastest teams from the league will get additional sessions to better their times and vie for the top spot. The entire DeepRacer League and Finals will be live streamed.

In this module, students will continue to expand develop their Python skill set with Lists and Dictionaries. Students will learn to understand these data structures as bundles of simpler data used to represent more complex entities through data composition. In addition, this module will cover Object Oriented Programming paradigm, a powerful problem-solving approach that allows programmer to think about the behaviour of their program in terms of a collection of objects and how they interact, and enables the solving of complex problems.

Data Science is a multidisciplinary field comprising of Statistics, Machine Learning, Data Visualization, Database Programming and Computing. Through the introduction fo CRSIP-DM methodology, students will be exposed to field of computer science that deals with programming machines that finds patterns in data, an important part of solving business problems.

In this module, students will learn data wrangling, in which they will load data from external sources into their Python environment for processing and analysis in a Python environment.

This is a hands-on module where students will practice how to conduct the process of storytelling to explain the patterns found. After being introduced to the AGILE framework, students will be split into groups and work with a Teaching Assistance on how to craft the user requirements for a business problem.

Learn to create interactive user interfaces and frontend with Python and bring your app ideas to life.

Students will have the opportunity to understand Application programming Interfaces (APIs) as programming tools that allows programmers to add third-party developed functionality and data to their applications.

During this module, students will be introduced to the AWS Console, AWS S3 service as a flexible file storage on cloud, IAM service for securing cloud applications, DynamoDB service for persistent structured data storage, Elastic Beanstalk for deploying application servers, Most importantly, they will use AWS services and host data-driven applications using Dash.

In this module, students will gain an understanding on how to evaluate and deploy a data science project through examining a case study. They will learn how Netflix increased the accuracy of movie recommendations to its users by more than 10% in 2008 by using its user review data. At this time, students will also be introduced to a selection of challenge statements provided by the Tampines Town Council that they will address for their capstone project.

Students will be introduced to AWS Sagemaker as an environment to run Machine Learning in the cloud. They will be exposed to Linear Regression, Logistic Regression and Regularisation. Overall, they will have a strong understanding of Machine Learning as computing the search of an appropriate explanatory function for a given dataset.

Students will begin on their capstone data science project using CRISP-DM framework and AWS cloud services. Throughout the project, students will be guided by Teaching Assistants in the planning and execution of their work.

Learn how neural networks work and why they are the most powerful and effective tools in the field of artificial intelligence today. Build applications for machine vision and natural language processing use cases with Convolutional and Recurrent Neural Networks.

Students will explore the AWS DeepLens for onsite real-time image capture. They will also be deploying machine vision models directly on the DeepLens and learning how to configure the DeepLens console. In addition, students will use numerous APIs including the Transcribe API which will turn speech to text.

Students will continue to work towards the completion of their capstone project. Along the way, students will be able to attend webinars hosted by AWS that showcases Data Scientists recounting how Data Science is relevant and crucial in today's world.