Certified Data Science Practitioner (CDSP)

Corporate Courses

Certified Data Science Practitioner (CDSP)

Course Outline

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.

Location

Online

Prerequisites

To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science. You can obtain this level of knowledge by taking the CertNexus DSBIZ™ (Exam DSZ-110) course.

You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended. You can obtain this level of skills and knowledge by taking the Logical Operations course Using Data Science Tools in Python® . In addition to programming, you should also have experience working with databases, including querying languages like SQL. Several Logical Operations courses can help you attain this experience:
• Database Design: A Modern Approach
• SQL Querying: Fundamentals (Second Edition)
• SQL Querying: Advanced (Second Edition)

Course Content

Lesson 1: Addressing Business Issues with Data Science

Topic A: Initiate a Data Science Project
Topic B: Formulate a Data Science Problem

Lesson 2: Extracting, Transforming, and Loading Data

Topic A: Extract Data
Topic B: Transform Data
Topic C: Load Data

Lesson 3: Analyzing Data

Topic A: Examine Data
Topic B: Explore the Underlying Distribution of Data
Topic C: Use Visualizations to Analyze Data
Topic D: Preprocess Data

Lesson 4: Designing a Machine Learning Approach

Topic A: Identify Machine Learning Concepts
Topic B: Test a Hypothesis

Lesson 5: Developing Classification Models

Topic A: Train and Tune Classification Models
Topic B: Evaluate Classification Models

Lesson 6: Developing Regression Models

Topic A: Train and Tune Regression Models
Topic B: Evaluate Regression Models

Lesson 7: Developing Clustering Models

Topic A: Train and Tune Clustering Models
Topic B: Evaluate Clustering Models

Lesson 8: Finalizing a Data Science Project

Topic A: Communicate Results to Stakeholders
Topic B: Demonstrate Models in a Web App
Topic C: Implement and Test Production Pipelines

Appendix A: Mapping Course Content to CertNexus® Certified Data Science Practitioner (CDSP) (Exam DSP-110)

Corporate Courses