CertNexus

Certified Data Science Practitioner (CDSP)

Prove your ability to apply end-to-end data-science processes in 2 instructor-led days. This hands-on workshop guides you through business-problem framing, ETL, exploratory analysis, machine-learning modelling and project delivery – preparing you for the CertNexus® Certified Data Science Practitioner (Exam DSP-210) credential.

Why choose this course?

Vendor-neutral, expert-level credential – covers the full data-science lifecycle from framing business issues to deploying models.
Exam-aligned, performance-based labs – each module maps directly to the DSP-210 exam blueprint, reinforcing real-world scenarios.
Hands-on use of industry-standard tools – work with Python, SQL, pandas, scikit-learn and visualization libraries in guided exercises.
Fast-track your certification – two intensive days plus capstone labs get you ready for the DSP-210 exam in record time.

This course is ideal for:

Data scientists, analysts and engineers who build and deploy ML solutions.
Business-intelligence or business-analytics professionals seeking to validate end-to-end data-science skills.
IT and operations staff responsible for integrating predictive models into production workflows.
Anyone preparing for CertNexus DSP-210 on their data-science-certification journey.

Prerequisites

Several years of experience with computing technology and a basic aptitude in programming.
Familiarity with data concepts (tables, rows, columns) and high-level programming (e.g. Python, SQL).

Course Content

Addressing Business Issues with Data Science – Initiate a data-science project; formulate a machine-learning problem to meet business objectives.
Extracting, Transforming & Loading Data – Extract data from files and databases; transform and engineer features; load cleansed data for analysis.
Analyzing Data – Examine distributions, outliers and relationships; apply visualizations to derive insights and detect anomalies.
Designing a Machine-Learning Approach – Select algorithms; test hypotheses; choose evaluation metrics for classification, regression and clustering.
Developing Classification Models – Train and tune logistic-regression and k-nearest neighbours models; evaluate with confusion matrices and ROC curves.
Developing Regression & Forecasting Models – Build, evaluate and optimize linear regression and time-series forecasting approaches.
Developing Clustering Models – Apply k-means and hierarchical clustering to segment data and uncover patterns.
Finalizing a Data-Science Project – Communicate results to stakeholders, demonstrate models in a simple web app and outline production pipelines.

Hardware Requirements

Interested?

Enquire today and one of our consultants will be in touch.