CDSP-210.AK1
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.
- Practice in 39 Hands-On Labs — nothing to install
- 9 Interactive Lessons and 33 topics mapped to the official exam objectives
- 292 Practice Test Questions and 2 Full Length Tests
Intermediate Self-paced · 1 year access
39 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
01 / Skills you'll get
What you will be able to do
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.
- 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.
Course Highlights
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9 Structured Lessons Comprehensive coverage of core course objectives
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39 Hands-On LiveLabs Interactive guided scenarios with instant evaluation
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292 Practice Questions Assessment tests with detailed answer rationales
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1 Year Full Access Self-paced learning accessible anytime on all devices
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
9 Interactive Lessons · 33 topics01 Introduction 3 topics +
- Course Description
- How To Use This Course
- Course-Specific Technical Requirements
02 Addressing Business Issues with Data Science 4 topics +
- TOPIC A: Initiate a Data Science Project
- TOPIC B: Democratize Data
- TOPIC C: Formulate a Data Science Problem
- Summary
03 Extracting, Transforming, and Loading Data 4 topics · 13 LiveLab +
- TOPIC A: Extract Data
- TOPIC B: Transform Data
- TOPIC C: Load Data
- Summary
13 LiveLab in this lesson — see the labs panel →
04 Analyzing Data 5 topics · 13 LiveLab +
- TOPIC A: Examine Data
- TOPIC B: Explore the Underlying Distribution of Data
- TOPIC C: Use Visualizations to Analyze Data
- TOPIC D: Preprocess Data
- Summary
13 LiveLab in this lesson — see the labs panel →
05 Designing a Machine Learning Approach 4 topics +
- TOPIC A: Identify Machine Learning Concepts
- TOPIC B: Identify Transformer-Based Deep Learning Concepts
- TOPIC C: Test a Hypothesis
- Summary
06 Developing Classification Models 3 topics · 6 LiveLab +
- TOPIC A: Train and Tune Classification Models
- TOPIC B: Evaluate Classification Models
- Summary
6 LiveLab in this lesson — see the labs panel →
07 Developing Regression Models 3 topics · 3 LiveLab +
- TOPIC A: Train and Tune Regression Models
- TOPIC B: Evaluate Regression Models
- Summary
3 LiveLab in this lesson — see the labs panel →
08 Developing Clustering Models 3 topics · 3 LiveLab +
- TOPIC A: Train and Tune Clustering Models
- TOPIC B: Evaluate Clustering Models
- Summary
3 LiveLab in this lesson — see the labs panel →
09 Finalizing a Data Science Project 4 topics · 1 LiveLab +
- TOPIC A: Communicate Results to Stakeholders
- TOPIC B: Demonstrate Models in a Web App
- TOPIC C: Implement and Test Production Pipelines
- Summary
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
39 LiveLabs- 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
- 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
- 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
- Tuning Regression Models
- Training Regression Trees and Ensemble Models
- Evaluating Regression Models
- Training a Hierarchical Clustering Model
- Tuning Clustering Models
- Evaluating Clustering Models
- Building an ML Pipeline
03 / FAQs
Questions before you start
What is a CDSP certification?+
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).
Is 3 months enough for data science?+
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.
Is 30 Too Old to Learn Data Science?+
Keep going
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Prepare for CDSP Certification
Become a Certified Data Scientist who bridges the gap between data and business objectives.
- 1 year of full access
- 39 LiveLab included
- Certificate of completion
No credit card required