Offered by CertNexus®, Certified Data Science Practitioner™ (CDSP) certificate is an industry-validated certification which helps professionals differentiate themselves from other job candidates by demonstrating their ability to put data science concepts into practice. The Certified Data Science Practitioner™ (CDSP) training program offered by Multimatics is designed to help participants gain the ability to use data science principles to address business issues, use multiple techniques to prepare and analyze data, evaluate datasets to extract valuable insights, and design a machine learning approach. The training material is prepared based on the latest edition of COBIT® 2019 Framework, accompanied by discussions and exercises to work on the questions.
Multimatics is an Authorized Training Partner for the Certified Data Science Practitioner™ (CDSP) training and certification program accredited by the CertNexus®.
By the end of the program, participants will be able to:
This program is designed for professionals across different industries seeking to demonstrate the ability to gain insights and build predictive models from data.
This program is 5 days of intensive training class.
The program provided by Multimatics will be delivered through interactive presentation by professional instructor(s), group debriefs, individual and team exercises, behavior modelling and roleplays, one-to-one and group discussion, case studies, and projects.
There is no specific requirement to join this program, although the following knowledge, skills, and abilities are recommended:
Participants will take CDSP Exam which consists of 100 multiple choice questions. They will be given 2 hours to finish the exam. Participants who successfully passed the exam will be given an official Certified Data Science Practitioner™ (CDSP) certification from CertNexus®.
Identify the project scope
Understand stakeholder challenges
Classify a question into a known data science problem
Gather relevant datasets
Clean datasets
Merge datasets
Apply problem-specific transformations to datasets
Load data
Examine data
Preprocess data
Carry out feature engineering
Prepare datasets for modeling
Build training models
Evaluate models
Test hypotheses
Test pipelines
Report findings