Graduate Training Programmes

Ensure new graduates have the baseline Data Science skills they need from the start

We’ll help you design structured training programmes that help graduates quickly transition from academia to industry positions in Data Analytics and Data Science.

Match training with job requirements using industry-relevant projects
Provide continuous assessment to track the progression of new hires
Monitor performance and engagement with real-time metrics
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Our Approach to Graduate Training

Training

Our proven teaching method consists of blended in-person and online learning, with clear progression paths for different graduate backgrounds.

Assessment

Participants use K.A.T.E.® to receive immediate feedback on their work. To ensure your employees get the support they need to maintain their momentum.

Analytics

K.A.T.E.® features an analytics and monitoring dashboard to track employee progression, performance and the scores on their project-based assessments.

Ready to optimise your graduate training? Get in touch!

Data Analyst and Data Science Training Pathways

Duration: Customisable or a recommended duration of 8 weeks for Data Analysts, 12 weeks for Data Scientists.

FAQs

How to maximise time-to-productivity for new graduates?

Traditionally, onboarding takes time and work from your team, but we believe onboarding can be radically improved with tailored training and project-based assessments that simulate their real working environment.

With graduate training pathways, you will address skill gaps in a short amount of time and ensure employees build confidence in the relevant Data Science tools, technicals and practices for their role.

What’s the problem with generic training?

Effective onboarding starts with personalised learning materials, that learners can directly apply to their work settings.

This requires a nontraditional approach, moving away from inflexible powerpoint lectures to engineer experiences that allow graduates to practice and apply their skills on relevant industry projects as they learn.

Sourcing relevant, cutting-edge content is another core requirement.

Techniques such as model evaluation, interpretability, performance optimisation and code maintenance need to be covered rigorously to get graduates off to a good start. However, many traditional curriculums do not keep up with the latest advancements and practises, leaving graduates uninspired and unable to contribute to production until your team leads pass on their knowledge directly.

How to cater for different skill sets and career paths?

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Training Graduate Data Analysts?

Your graduates need to understand the fundamentals of the Data Science lifecycle and techniques including Exploratory Data Analysis, Unsupervised Learning, Supervised Learning and associated methodologies to maintain and tune models. Data Analysts must be comfortable working and storing various forms of data utilising Big Data Systems and technologies (e.g. Spark and Parquet).

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Building Graduate Data Scientists?

In addition to the essential Data Analyst skills, a Data Science graduate is expected to understand and apply modern Machine Learning and Data Engineering techniques. These techniques include; Time Series Analysis, Ensembles Models, Natural Language Processing, Deep Learning, Recommender Systems and Interpretability of Models (LIME, SHAP).