Certified Data Science Practitioner (CDSP) (Exam DSP-110)

  • Course level: Intermediate


Certified Data Science Practitioner (CDSP) (Exam DSP-110)

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.

Benefits of the course

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.


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)

Targeted Audience

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.
However, there is not necessarily a single organizational role that this course targets. A prospective
student 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
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in applied math and statistics who wants to take their skills to the next level; or any number of other datadriven situations.
Ultimately, the target student 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.
This course is also designed to assist students in preparing for the CertNexus® Certified Data Science
Practitioner (CDSP) (Exam DSP-110) certification.

Topics for this course

20 Lessons40h

Lesson 1: Addressing Business Issues with Data Science?

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

Lesson 4: Designing a Machine Learning Approach

Lesson 5: Developing Classification Models

Lesson 6: Developing Regression Models

Lesson 7: Developing Clustering Models

Lesson 8: Finalizing a Data Science Project