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CDSP Certification Training: Master Data Science

Upskilling in data science is the way forward, and the CDSP course gets you there with hands-on learning.

(CDSP-210.AK1) / ISBN : 978-1-64459-727-9
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About This Course

Our Certified Data Science Practitioner (CDSP) course is perfectly aligned with the DSP-210 exam objectives…and gets you hands-on!

Learn how to initiate data science projects, democratize data, and frame problems for analytical solutions. Design machine learning approaches, train classification, regression, and clustering models, and fine-tune them for accuracy. 

Finally, deliver impact by passing the CDSP certification.     

Skills You’ll Get

  • Data Wrangling & ETL: Extract, clean, transform, and load data from multiple sources for analysis.
  • Exploratory Data Analysis (EDA): Analyze datasets, visualize trends, and identify patterns using statistical methods.
  • Machine Learning Model Development: Build, train, and optimize classification, regression, and clustering models.
  • Deep Learning Fundamentals: Understand transformer-based models and apply them to real-world problems.
  • Data Storytelling & Deployment: Communicate insights effectively and deploy models in production environments.
  • End-to-End Data Science Workflow: Manage the full project lifecycle from problem formulation to solution implementation. 
Download Course Outline

1

Introduction

  • Course Description
  • How To Use This Course
  • Course-Specific Technical Requirements
2

Addressing Business Issues with Data Science

  • TOPIC A: Initiate a Data Science Project
  • TOPIC B: Democratize Data
  • TOPIC C: 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: Identify Transformer-Based Deep Learning Concepts
  • TOPIC C: 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

  • Loading Data into a Database
  • Handling Textual Data
  • Handling Irregular and Unusable Data
  • Consolidating Data from Multiple Sources
  • Extracting Data with Database Queries
  • Reading Data from a CSV File
  • Training a k-means Clustering Model
  • Training a Linear Regression Model
  • Training Classification Decision Trees and Ensemble Models
  • Exporting Data to a CSV File
  • Deduplicating Data
  • Correcting Data Formats
  • Loading Data into a DataFrame
2

Analyzing Data

  • Examining Data
  • Exploring the Underlying Distribution of Data
  • Analyzing Data Using Maps
  • Analyzing Data Using Bar Charts
  • Analyzing Data Using Scatter Plots and Line Plots
  • Analyzing Data Using Box Plots and Violin Plots
  • Analyzing Data Using Histograms
  • Handling Missing Values
  • Performing Dimensionality Reduction
  • Discretizing Variables
  • Encoding Data
  • Applying Transformation Functions to a Dataset
  • Splitting and Removing Features
3

Developing Classification Models

  • Tuning Classification Models
  • Training an SVM Classification Model
  • Training a k-NN Model
  • Training a Logistic Regression Model
  • Training a Naïve Bayes Model
  • Evaluating Classification Models
4

Developing Regression Models

  • Tuning Regression Models
  • Training Regression Trees and Ensemble Models
  • Evaluating Regression Models
5

Developing Clustering Models

  • 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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The Certified Data Science Practitioner (CDSP) is a professional certification that validates expertise in data science, covering skills like data collection, wrangling, statistical modeling, machine learning, and communicating insights.

Key details:

  • Purpose: Designed for data professionals, analysts, and programmers to demonstrate real-world problem-solving abilities.
  • Curriculum: Includes ETL processes, exploratory data analysis, model training (classification, regression, clustering), and project deployment
  • Eligibility: Open to beginners (e.g., undergraduates) and professionals with programming experience (Python/R/SQL recommended).

Yes, but with caveats:

  • Intensive Programs: Our CDSP course provides foundational skills to prepare you for the DSP-210 exam and entry-level roles.
  • Prerequisites: Prior programming (Python) or analytics (Excel/SQL) experience accelerates learning.
  • Scope: Focus on practical skills (ETL, visualization, ML models) rather than deep theoretical knowledge. 

No, 30 is not too old because data science roles are growing at 35% annually (BLS 2022–32), with employers valuing diverse experience. Also, project management, problem-solving, and domain knowledge (e.g., healthcare, finance) enhance data science applications.

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