Data Science Training

Empowering data science professionals to tackle strategic business challenges.

This course propels data scientists towards higher performance by embedding software engineering excellence into AI workflows.

Participants will learn to architect scalable AI solutions that deliver added value, streamline development to production transitions, and foster effective team collaboration. Focused on clean code and strategic planning, it prepares teams to deploy robust, scalable AI strategies, ensuring impactful, efficient outcomes in any business context.

Who we work with

Partners in progress for upskilling large enterprises.

Our programme is tailored for Learning and Development officers in organisations with 300+ employees, transforming data scientists to adopt a more engineering-focused approach.

This upskilling journey empowers teams to think and work more dynamically, bridging the gap between traditional data science and innovative software engineering solutions.

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Case Study

Hypergolic's tailored training in advanced data science techniques significantly enhanced a government agency's productivity, leading to improved project outcomes and the establishment of an ongoing partnership for continued skills development.

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COURSE OVERVIEW

The course prepares data scientists to deploy scalable AI solutions, enhancing team performance and delivering outsized business value.

01

Foundations

At the core of the course are the foundational principles that inspire better team motivation through improved technical practices. It delves into how a product mindset can significantly enhance the effectiveness of a data science team and introduces the concept of Clean Architecture. This section is pivotal in demonstrating how adopting Clean Architecture can lead to significant productivity gains across the AI lifecycle, enabling teams to work more efficiently and with greater focus on outcomes that matter.

02

Tactical Programming

The Tactical Programming segment of the course is dedicated to addressing common challenges faced by data scientists, including identifying and mitigating technical debt, understanding the differences between software testing and AI testing, and establishing refactoring cycles to improve project health. This part of the course also covers the identification and resolution of anti-patterns ("code smells") and emphasises the importance of maintaining code readability and conducting effective code reviews. It introduces participants to relevant design patterns for data-driven projects, ensuring that they are well-equipped to handle the complexities of AI programming.

03

Technical & Business Programming

Moving beyond immediate programming concerns, the Strategic Programming section focuses on higher-level planning and architectural considerations. It introduces concepts such as Domain Data Models, Bounded Contexts, and Layers of Abstraction, which are crucial for structuring and planning the delivery of AI products. This section teaches how to effectively utilise Clean Architecture and tactical programming throughout various stages of AI product development, from exploratory data analysis to full-scale business integration, ensuring that products are built on solid, scalable foundations.

04

Planning and Operation

This segment of the course is where the strategic concepts taught earlier are applied to real-world project and product delivery scenarios. It emphasises the importance of leveraging technical capabilities for more effective problem-solving, early productionisation, and the creation of fast feedback loops. Participants will learn about transparent estimation and reporting techniques, along with strategies for granular resource allocation, ensuring that projects are managed efficiently and can adapt rapidly to changing business needs.

05

Hands-on Programming Exercises

The final section, encompassing about 60% of the course hours, focuses intensely on the practical application of coding skills for ML applications. Participants will optimise and refine their programming approaches to boost agility and maintainability of machine learning algorithms. Emphasising real-world applicability, the training entails hands-on programming encompassing advanced data structures, software architectures, and the effective use of critical ML libraries and frameworks, ensuring that data scientists can implement robust, scalable solutions.

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