Certified Full Stack Data Scientist

Full Stack Data Science Certification is based on Data, Technology, and Business (DTB) principles test

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About Certified Full Stack Data Scientist

Objectives Of Full Stack Data Science

Apply Python, SQL, and big data technologies effectively

Master statistics, mathematics, and hypothesis-driven analysis

Build, train, and optimize machine learning models

Apply deep learning techniques for vision and NLP

Design scalable pipelines and ensure data governance

Deploy, monitor, and retrain models with MLOps practices

Learn from case studies and real-world industry scenarios

Use expert-crafted templates for faster project implementation

Benefits Of Full Stack Data Science

Bridge business needs with data-driven solutions

Master Python, SQL, and modern data workflows

Apply advanced statistics for real-world decision-making

Build scalable ETL pipelines and manage big data

Design accurate ML models with practical applications

Gain expertise in deep learning and NLP use cases

Deploy and monitor models with MLOps best practices

Showcase end-to-end ownership of the data lifecycle

Leverage industry case studies and expert-built templates

Strengthen career prospects across AI and analytics roles

Exam Syllabus of Full Stack Data Scientist Certification

30+ Hours of Learning

2 Practice Exams

Capstone Project

AI interview Practice Platform

Exam Syllabus Of Certified Full Stack Data Scientist

Business Analysis & Stakeholder Management
Requirement Elicitation & Documentation
Project Methodologies (Agile, Scrum, Kanban)
Problem Framing & Solution Scoping
Communication & Presentation Skills
Value Proposition & Business Impact
Risk Management & Prioritization

Core Python Fundamentals (Data Types, Control Flow)
Object-Oriented Programming (OOP)
Data Manipulation with Pandas & NumPy
Data Visualization with Matplotlib & Seaborn
Error Handling & Debugging
Working with APIs & Web Scraping
Code Versioning with Git & GitHub

Probability Theory & Distributions
Descriptive & Inferential Statistics
Hypothesis Testing & A/B Testing
Linear Algebra for Machine Learning
Calculus Fundamentals for Optimization
Dimensionality Reduction (PCA, t-SNE)
Statistical Modeling & Regression Analysis

Database Fundamentals (Relational vs. NoSQL)
Advanced SQL Queries & Window Functions
Data Warehousing Concepts
Building ETL/ELT Pipelines
Data Governance & Quality
Big Data Ecosystem (Hadoop, Spark)
Cloud Data Services (e.g., AWS S3, Google BigQuery)

Supervised Learning (Regression & Classification)
Unsupervised Learning (Clustering & Association)
Model Evaluation Metrics (Accuracy, Precision, Recall)
Cross-Validation & Hyperparameter Tuning
Bias-Variance Tradeoff
Feature Selection & Engineering
Ensemble Methods (Random Forest, Gradient Boosting)

Introduction to Neural Networks & Perceptrons
Activation Functions & Backpropagation
Convolutional Neural Networks (CNNs) for Computer Vision
Recurrent Neural Networks (RNNs) for Sequential Data
Natural Language Processing (NLP)
Transfer Learning
Deep Learning Frameworks (TensorFlow, PyTorch)

DevOps Principles for ML
Containerization with Docker
Orchestration with Kubernetes
CI/CD Pipelines for ML Models
Model Serving & API Development
Monitoring & Logging
Model Retraining & Versioning

Complete all learning materials provided in the course.
Finish case study assignment on key Full Stack Data Scientist concepts.
Submit your completed assignment for review and approval.
Pass the final MCQ exam to earn your certification.

Self-Paced Online

Expert Led Videos - 10 hrs of learning

Get 1 Certification - Just $200

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3 SME Connect (1-on-1)

Access to GSDC AI Studio

Weekly Group SME Connect Session

Certification Exam + 1 Free Retake & Practice

Capstone Project + AI Interview & Tools

GSDC Membership worth $109 free

Course Price: USD $190.00

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Target Audience For Data Scientist Certification

Data Analysts

Business Analysts

Machine Learning Engineers

AI Engineers

BI (Business Intelligence) Developers

Software Engineers working in AI/ML

Product Managers in Data-Driven Domains

Research Scientists in AI and Analytics

Pre-Requisites For Data Scientist Certification

Prior knowledge of programming, statistics, or data-related concepts is recommended, but not mandatory, to pursue this certification.

Exam Details Of Certified Full Stack Data Scientist

Exam Questions

40

Exam Format

Multiple choice

Language

English

Passing Score

65%

Duration

90 min

Open Book

No

Certification Validity

5 Years

Complimentary Retake

Yes

Sample Certification

About Data Scientist Certification

The GSDC Certified Full-Stack Data Scientist (CFDS) credential validates end-to-end expertise in every part of the data science lifecycle, business problem framing, data engineering, advanced analytics, machine learning, deep learning, and model deployment.

The credential is globally recognized and designed for professionals who want to demonstrate not only technical skills but also business aptitude and product-oriented thinking. The CFDS provides an individual with knowledge of Python programming, statistics, machine learning, big data, and MLOps so that he or she can implement scalable solutions that have real-world impact.

A professional Certified Full-Stack Data Scientist is recognized as an all-rounder, proficient with the entire pipeline: from business needs into requirements, bugs into working validated models, and from production to maintenance. The certification focuses on applications, case studies, and industry-ready implementations, making it quite useful in analytics, AI, and digital transformation.