CertNexus

Data Science Foundation

(DSP-110.AK1)/ISBN:978-1-64459-424-7

This course includes
Lessons
TestPrep
Hands-On Labs
AI Tutor (Add-on)
Data Science Foundation

Unlock the power of data and pave your way to success with the Data Science Foundation course. Prepare for the exam while engaging in interactive lessons, quizzes, test preps, and hands-on labs that will equip you with the skills to analyze, manipulate, and present data effectively. Become a sought-after data science practitioner and bring invaluable insights to your organization's decision-making processes.

Here's what you will get

Step into the world of data science and become a sought-after professional with the Certified Data Science Practitioner (CDSP) exam. In today's data-driven landscape, businesses rely on skilled individuals who can effectively analyze, manipulate, and present data. This exam will test your abilities to extract valuable insights, make informed decisions, and contribute to the success of any organization.

Lessons

9+ Lessons | 154+ Exercises | 64+ Quizzes | 247+ Flashcards | 247+ Glossary of terms

TestPrep

25+ Pre Assessment Questions | 2+ Full Length Tests | 25+ Post Assessment Questions | 50+ Practice Test Questions

Hands-On Labs

37+ LiveLab | 37+ Video tutorials | 01:50+ Hours

Need guidance and support? Click here to check our Instructor Led Course.

Here's what you will learn

Download Course Outline

Lessons 1: About This Course

  • Course Description
  • Course Objectives

Lessons 2: Addressing Business Issues with Data Science

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

Lessons 3: Extracting, Transforming, and Loading Data

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

Lessons 4: 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
  • Summary

Lessons 5: Designing a Machine Learning Approach

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

Lessons 6: Developing Classification Models

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

Lessons 7: Developing Regression Models

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

Lessons 8: Developing Clustering Models

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

Lessons 9: 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
  • Summary

Hands-on LAB Activities

Extracting, Transforming, and Loading Data

  • Reading Data from a CSV File
  • Extracting Data with Database Queries
  • Consolidating Data from Multiple Sources
  • Handling Irregular and Unusable Data
  • Correcting Data Formats
  • De-duplicating Data
  • Handling Textual Data
  • Loading Data into a Database
  • Loading Data into a DataFrame
  • Exporting Data to a CSV File

Analyzing Data

  • Examining Data
  • Exploring the Underlying Distribution of Data
  • Analyzing Data Using Histograms
  • Analyzing Data Using Box Plots and Violin Plots
  • Analyzing Data Using Scatter Plots and Line Plots
  • Analyzing Data Using Bar Charts
  • Analyzing Data Using HeatMaps
  • Handling Missing Values
  • Applying Transformation Functions to a Dataset
  • Encoding Data
  • Discretizing Variable
  • Splitting and Removing Features
  • Performing Dimensionality Reduction

Developing Classification Models

  • Training a Logistic Regression Model
  • Training a k-NN Model
  • Training an SVM Classification Model
  • Training a Naïve Bayes Model
  • Training Classification Decision Trees and Ensemble Models

Developing Regression Models

  • Training a Linear Regression Model
  • Training Regression Trees and Ensemble Models
  • Tuning Regression Models
  • Evaluating Regression Models

Developing Clustering Models

  • Training a k-Means Clustering Model
  • Training a Hierarchical Clustering Model
  • Tuning Clustering Models
  • Evaluating Clustering Models

Finalizing a Data Science Project

  • Building an ML Pipeline

Exam FAQs

$350

Pearson VUE

Multiple Choice / Single Response

The exam contains 100 (of which 75 count towards the final score) questions.

120 minutes

70%

A re-test fee of $50 will apply for each re-take. You may take the test up to three (3) times. You must pass the exam within six (6) months of the date of your first test