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Data Science Foundation

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

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.

Skills You’ll 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.

Download Course Outline

1

About This Course

  • Course Description
  • Course Objectives
2

Addressing Business Issues with Data Science

  • Topic A: Initiate a Data Science Project
  • Topic B: Formulate a Data Science Problem
  • Summary
3

Extracting, Transforming, and Loading Data

  • Topic A: Extract Data
  • Topic B: Transform Data
  • Topic C: Load Data
  • Summary
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
5

Designing a Machine Learning Approach

  • Topic A: Identify Machine Learning Concepts
  • Topic B: Test a Hypothesis
  • Summary
6

Developing Classification Models

  • Topic A: Train and Tune Classification Models
  • Topic B: Evaluate Classification Models
  • Summary
7

Developing Regression Models

  • Topic A: Train and Tune Regression Models
  • Topic B: Evaluate Regression Models
  • Summary
8

Developing Clustering Models

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

1

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
2

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
3

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
4

Developing Regression Models

  • Training a Linear Regression Model
  • Training Regression Trees and Ensemble Models
  • Tuning Regression Models
  • Evaluating Regression Models
5

Developing Clustering Models

  • Training a k-Means Clustering Model
  • Training a Hierarchical Clustering Model
  • Tuning Clustering Models
  • Evaluating Clustering Models
6

Finalizing a Data Science Project

  • Building an ML Pipeline

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$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

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