CDSP Instructor Digital Course Bundle w/lab
WEB PRICE:
$735.00
Member price:
$735.00
Qty
Please select required options above
WEB PRICE:
$735.00
Member price:
$735.00
Qty
Please select required options above
Description
Includes digital courseware, labs, and exam voucher for CertNexus Certified Data Science Practitioner (Exam DSP-110).
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.
The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring data sets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.
Course Objectives:
In this course, you will implement data science techniques in order to address business issues. You will:
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.
The exam will certify that the successful candidate has the knowledge, skills, and abilities required to answer questions by collecting, wrangling, and exploring data sets, applying statistical models and artificial-intelligence algorithms, to extract and communicate knowledge and insights.
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.