CertNexus Data Science Practitioner

What is Data Science? Certified Data Science Practitioner (CDSP) is a vendor-neutral, high-stakes certification designed for programmers, data professionals and analysts seeking to validate and showcase their knowledge and skills in the area of Data Science.

Why get Data Science Practitioner Certified?

Organizations have access to large and ever-increasing quantities of data. A Certified Data Science Practitioner can make sense of this information, capturing and applying it to improve organizational practices and performance.

Prove your skills Earning your Certified Data Science Practitioner sets you apart from other candidates when you seek a new job or want to advance in your existing position. It validates your ability to extract key insights from large amounts of data and use this information to drive your organization forward.

Lead Knowledgeble Data Science Professionals Data can be empowering, but only if properly collected, maintained, manipulated, analyzed, and translated into actionable information. CDSP confirms the capabilities and expertise of existing team members and those whom you are considering onboarding.

Overview:

For a business to thrive in our data-driven world, it must treat data as one of its most important assets.

Data is crucial for understanding where the business is and where it's headed. Not only can data revealinsights, it can also inform—by guiding decisions and influencing day-to-day operations.

This calls for arobust workforce of professionals who can analyze, understand, manipulate, and present data within aneffective and repeatable process framework. In other words, the business world needs data sciencepractitioners.

This course will enable you to bring value to the business by putting data science conceptsinto practice.This course includes hands on activities for each topic area.

Course Objectives:

In this course, you will implement data science techniques in order to address business issues. You will:

•Use data science principles to address business issues.

•Apply the extract, transform, and load (ETL) process to prepare datasets.

•Use multiple techniques to analyze data and extract valuable insights.

•Design a machine learning approach to address business issues.

•Train, tune, and evaluate classification models.

•Train, tune, and evaluate regression and forecasting models.

•Train, tune, and evaluate clustering models.

•Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.

Target Student:

This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.