About Certified Data Science Practitioner (CDSP)
This course gives professionals hands-on, practical skills to turn raw data into meaningful business insight through the complete data science lifecycle. It covers everything from initiating a data science project through to extracting and transforming data, building predictive models, and deploying them into real-world applications.
You'll start by learning how to address business challenges with data science and manage the full ETL process, then move into analysing and visualising data to uncover meaningful trends. From there, the course covers designing a machine learning approach and building classification models, followed by developing regression and clustering models, and finishing with how to communicate results and deploy models into production.
By the end, you'll present an end-to-end data science solution, built and evaluated by you, along with preparation for the CertNexus® CDSP (DSP-110) certification exam.
Expected Outcomes
The course is structured around the complete data science lifecycle — from problem definition through to deployment — so each skill builds toward a working, end-to-end solution. By the end, you'll be able to:
- Apply data science principles to identify and address real business challenges
- Execute ETL processes to prepare and structure datasets
- Analyse and visualise data to extract meaningful insights and trends
- Design and implement machine learning strategies for predictive modelling
- Train, test, and evaluate classification, regression, and clustering models
- Integrate and optimise models for production-level applications
- Communicate analytical results effectively to technical and non-technical audiences
- Monitor and maintain model performance to ensure continued business impact
Training Method
The course begins with addressing business issues through data science and managing the ETL process to prepare and structure data. It then moves into analysing and visualising data to uncover trends, before covering how to design a machine learning approach and build classification models.
From there, you'll develop regression and clustering models, learning how to train, tune, and evaluate each type. The course closes with finalising a data science project — communicating results to stakeholders, demonstrating models in a web app, and implementing production pipelines. Throughout, you'll work through instructor-led presentations, hands-on exercises using real-world datasets, machine learning labs, and project-based learning, culminating in the presentation of your own end-to-end data science solution.
