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Full Stack Data Scientist Training & Certification

Trusted by 1000s of global organizations, NovelVista is the leading Accredited Training Organization (ATO) to conduct Full Stack Data Scientist Certification Course.

  • Industry Expert Trainers
  • Online learning session
  • Accredited Trainer
  • Exam fee included
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Certified Full Stack Data Scientist Course Overview

Full-Stack Data Scientist course is aimed towards helping you to learn the full cycle of identifying the business problem, analyzing the data sources and decoding 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 models solve the business problem, and its implementation. As a Full-Stack Data Scientist, you’ll be 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 the live video feed, webcams, video files on youtube, sentimental analysis for review. The Full-Stack Data Scientist training and certification course teaches you all of it.
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What You Will Get?

Study Material

Mock Exams

Exam Registration Assistance

60+ Hours of Live Training

Case Studies Soft Copy

Official Courseware from GSDC

Learning Outcome

After the completion of the course, the participants would be able to:

How to implement Data Science techniques..
Data Science best practices.
Data Science tools and techniques.
How to extract insights from complex data.
How to build & architect complex apps.
Real-Time Case Studies.

Course Curriculum

Course Curriculum+

Full-Stack Data Scientist course curriculum is mainly focused on sharing in-depth knowledge about:

  • Building telecom churn management case study
  • Handwritten digits identification
  • Building a recommendation engine
  • Practical case study on banking data
  • Classifying cancer- case study
  • Sentiment Analyzer for reviews
  • Object detection in the live video feed, webcams, video files or YouTube

Exam Syllabus+

1. Product management (BA)


  1. Business Analysis & Stakeholders Overview
  2. Communication, Planning, Evaluation, Prioritization
  3. BA Tools Overview & Design Documents
  4. Stakeholder management BPMN, Requirement Elicitation & Management
  5. Enterprise Analysis, Agile & Scrum


2. Data engineering and Database Management


  1. SQL & NoSQL Databases
  2. MySQL deep dive
  3. Stream processing
  4. Batch processing
  5. Data pipelines
  6. Big data technologies
  7. Spark and Hadoop
  8. Airflow


3. Python Programming for Data Science


  1. Python for data science
  2. Detailed Python curriculum - required for data science
  3. Data Analytics Libraries
  4. Data Visualization - matplotlib, seaborn, plotly
  5. Exploratory Data Analysis


4. Practical Approach for Data Science with AL/ML


  1. Overview of Data Science
  2. Business problem understanding
  3. Statistics for Data Science [need to be detailed]
  4. Machine Learning overview and Techniques
  5. Supervised Machine Learning
  6. Algorithms - Linear Regression, Logistic Regression, Decision, Random forest, SVM
  7. Unsupervised Machine Learning - clustering algorithms
  8. AI and Data Science
  9. ANN, RNN, CNN Overview
  10. Deep learning overview


5. Machine Learning Model Deployment using DevOps


  1. Infrastructure
  2. Automation
  3. Monitoring, Reliability
  4. Microservice architecture
  5. Docker
  6. Kubernetes
  7. Ansible
  8. Tools for model deployment
  9. Continuous integration and deployment - jenkins
  10. AWS Overview & Important AWS services
  11. Machine Learning Model Deployment on AWS