Certified Full Stack Data Scientist

GSDC's Full Stack Data Scientist Certification covers the complete data science lifecycle. Candidates learn to identify business problems, analyze data sources, transform data for ML algorithms, train models, and measure their effectiveness.The program includes practical case studies on topics like handwritten digit identification, recommendation engines, banking data analysis, cancer classification, object detection in live video feeds, and sentiment analysis for reviews.

This certification prepares candidates for the data scientist certification exam, showcasing their expertise in data science.GSDC's Full Stack Data Scientist Certification sets a high standard for expertise in the field and equips professionals with the necessary knowledge and skills to make significant contributions to the data science industry.


What is Full-Stack Data Scientist

A data scientist with the ability to comprehend and carry out not only data analytics and model development but also a model deployment and integration with the business application.

What is Full Stack Data Scientist

Structure of Full-Stack Data Scientist

Three certifications—Certified Business Analytics Practitioner, Certified Machine Learning Master, and Certified DevOps Engineer—combine to form GSDC's Full-Stack Data Scientist designation. To put it briefly, GSDC provides certifications for a wide range of subjects that would enable you to become a Full-Stack Data Scientist.

Full Stack Data Scientist Structure

Firstly, you’d need to complete your Business Analytics Practitioner Certification.

Certified Business Analytics Practitioner

GSDC's Practitioner Certified in Business Analysis qualification provides a deep understanding of standard practices and techniques. Earn this qualification to drive success in a changing business environment. Benefit from years of best practices and expertise from industry professionals.

Then, you will have to go for Certified Machine Learning Master Certification.

Certified Machine Learning Master

Certified Machine Learning Master certification provides a deep understanding of machine learning practices. This subset of Artificial Intelligence focuses on computer algorithms that improve automatically through experience. Gain expertise in supervised learning, Apriori algorithms, Market Basket Analysis, ARIMA analysis, and more. After completing this certification, you'll confidently utilize various machine learning models and approaches. Upgrade your skills with our machine learning professional certification.

At last, you’ll finish off with your DevOps Engineer Certification.

Certified DevOps Engineer

Becoming a Certified DevOps Engineer encompasses the principles of continuous development and deployment, software development operations, and configuration management automation.

Gain expertise in tools like Git, Docker, Jenkins, Nagios, Puppet, Ansible, and Kubernetes. Understand the cultural shift, roles & responsibilities, cross-functional team structure, and automation's significance for successful DevOps implementation. Elevate your skills as a certified DevOps engineer.

How Can You Become A Certified Full Stack Data Scientist?

After the completion of these 3 certifications, you'll be announced as a certified Full Stack Data Scientist and will be rewarded with a certificate and badge as well. Not only that, but you'll also be accepted and known at the International level to become a GSDC accredited professional.

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Exam Syllabus

1. Product management (BA)

  • Business Analysis & Stakeholders Overview
  • Communication, Planning, Evaluation, Prioritization
  • BA Tools Overview & Design Documents
  • Stakeholder management BPMN, Requirement Elicitation & Management
  • Enterprise Analysis, Agile & Scrum
2. Python Programming for Data Science
  • Python for data science
  • Detailed Python curriculum - required for data science
  • Data Analytics Libraries
  • Data Visualization - matplotlib, seaborn, plotly
  • Exploratory Data Analysis
3. Data Science Basics
  • Basics Stats for Data Science
  • ML Overview and types of ML
  • EDA Titanic Dataset
  • Random Forest
  • Conditional Probability and Naive Bayes Classifier
  • K Means Clustering
  • Association Rules and Recommendation Engine
  • Bias and Variance
  • Computer Vision OpenCV
  • Data Science - Project Methodologies
  • Decision tree
  • Digit Classification

4. Practical Approach for Data Science with AI/ML

  • Overview of Data Science
  • Business problem understanding
  • Statistics for Data Science [need to be detailed]
  • Machine Learning Overview and Techniques
  • Supervised Machine Learning
  • Algorithms - Linear Regression, Logistic Regression, Decision, Random forest, SVM
  • Unsupervised Machine Learning - clustering algorithms
  • AI and Data Science
  • ANN Overview
  • Deep learning overview

5. Machine Learning Model Deployment using DevOps

  • Infrastructure
  • Automation
  • Monitoring, Reliability
  • Microservice architecture
  • Docker
  • Kubernetes
  • Ansible
  • Tools for model deployment
  • Continuous integration and deployment - Jenkins



The Global Skill Development Council (GSDC) is an independent, vendor-neutral, international credentialing and certification organization for the emerging technologies:

  • Advisory board members and SMEs are from around the world, drawn from different specializations.
  • Supported by the world's most esteemed thought leaders from Yale, MIT, Stanford, Wharton, and Harvard.
  • Hub of Trending Technologies and framework certifications.
  • Content curated by Industry's best Subject matter experts.
  • Webinars and Conferences.
  • Training Partners Across The Globe.