Certified Data Science Practitioner (CDSP)

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 reveal insights, it can also inform—by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands on activities for each topic area.

Course Details

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Days:

Location:

Virtual

Course Overview

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.

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.

1 – Addressing Business Issues with Data Science

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

2 – Extracting, Transforming, and Loading Data

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

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

4 – Designing a Machine Learning Approach

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

5 – Developing Classification Models

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

6 – Developing Regression Models

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

7 – Developing Clustering Models

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

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

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