To thrive in today’s data-driven world, businesses must treat data as a vital asset. Data provides essential insights into current performance and future directions, guiding decisions and influencing daily operations. This necessitates a skilled workforce capable of analyzing, interpreting, and presenting data within a structured process. The demand for data science practitioners is growing.
This training course will empower you to add value to your organization by applying data science concepts in practical settings, featuring hands-on activities for each topic covered.
To participate successfully in this course, specific prerequisites are required. You can view the program prerequisites by accessing the following link: CDSP Prerequisites and CertNexus Exam Blueprints.
In this training course, you will implement data science techniques in order to address business issues. You will:
This training course is designed for business professionals who leverage data to address business issues. The typical participant in this training course will have several years of experience with computing technology, including some aptitude in computer programming.
However, there is not necessarily a single organizational role that this training course targets. A prospective participant might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data driven situations.
Ultimately, the target participant is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.
This training course is also designed to assist participants in preparing for the CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110) certification.
Addressing Business Issues with Data Science
Extracting, Transforming, and Loading Data
Analyzing Data
Designing a Machine Learning Approach
Developing Classification Models
Developing Regression Models
Developing Clustering Models
Finalizing a Data Science Project