Certified Data Science Practitioner (CDSP)

What are the Goals?

In this Certified Data Science Practitioner training 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.

Who is this Training Course for?

This Certified Data Science Practitioner 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.

Daily Agenda

Day One: Addressing Business Issues with Data Science

• Initiate a data science project
• Formulate a data science problem

Extracting, Transforming, and Loading Data

• Extract data
• Transform data
• Load data

Day Two: Analyzing Data

• Examine data
• Explore the underlying distribution of data
• Use visualizations to analyze data
• Preprocess data

Day Three: Designing a Machine Learning Approach

• Identify machine learning concepts
• Test a hypothesis

Developing Classification Models

• Train and tune classification models
• Evaluate classification models

Day Four: Developing Regression Models

• Train and tune regression models
• Evaluate regression models

Developing Clustering Models

• Train and tune clustering models
• Evaluate clustering models

Day Five: Finalizing a Data Science Project

• Communicate results to stakeholders
• Demonstrate models in a web app
• Implement and test production pipelines