Certified Full Stack Data Scientist

GSDC’s Full Stack Data Scientist Certification helps the candidates in learning the full cycle of identifying the business problem, analyzing the data sources and deciding if more data is needed, transform the data so that it can be put into an ML algorithm, training the models, measuring how well the model solves the business problem, and its implementation.

A Full Stack Data Scientist is responsible for, handwritten digits identification, building a recommendation engine, practical case study on Banking data, classifying the type of cancer-case study, object detection in live video feed, webcams, video files or YouTube, Sentiment Analysis for reviews.


What is Full-Stack Data Scientist

A Data Scientist who is capable of understanding and executing not just the data analytics and model development, but also the model deployment and integration with the business application

What is Full Stack Data Scientist

Structure of Full-Stack Data Scientist

GSDC’s Full-Stack Data Scientist certification is a combination of 3 certifications- Certified Business Analytics Practitioner, Certified Machine Learning Master, and Certified DevOps Engineer. In a nutshell, GSDC offers certifications for a broad range of topics that would help 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

Certified Business Analytics Practitioner deals with Product management. The syllabus curated for this certification teaches you about Business Analysis & Stakeholders Overview, Communication, Planning, Evaluation, Prioritization, BA Tools Overview & Design Documents, Stakeholder management BPMN, Requirement Elicitation & Management, and Enterprise Analysis, Agile & Scrum

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

Certified Machine Learning Master

GSDC's Certified Machine Learning Master certification offers a deep understanding of Machine Learning practices. Machine learning is a subset of Artificial Intelligence that is based on the study of computer algorithms that has the ability to improve automatically through experience. Machine learning algorithms make a model with sample data or training data for prediction and decision making without being programmed to do so.

Certified Machine Learning Master certification doesn't only clear your concept about the basics of Machine Learning but gives you a detailed picture of all the modules of supervised learning, Apriori Algorithm, Market Basket Analysis, ARIMA analysis and many more. After the completion of Machine Learning Master certification, you will be able to worth all the machine learning models and use every Machine Learning approaches.

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

Certified DevOps Engineer

DevOps Engineer Certification qualifies various aspects of the principles of continuous development and deployment, software development operations, continuous integration, automation of configuration management, and learns the various tools like Git, Docker, Jenkins, Nagios, Puppet, Ansible, and Kubernetes.

The focus is to understand DevOps right from the cultural change and leading to an understanding of various roles & responsibilities, cross-functional team structure, and the importance of automation for achieving benefits from successful DevOps implementation.

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:

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The Global Skill Development Council (GSDC) is an Independent, Vendor Neutral, International Credentialing and Certification Organization for Professionals.

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