Certified Data Science and AI Specialist

  • 44 weeks
  • 264 Hours

Mastering the Art of Data Science and AI with Industry-Recognized Certification

Course Overview

The Certified Data Science and AI Specialist Course is a comprehensive and immersive program designed to equip participants with essential data science and artificial intelligence knowledge and skills. This hands-on course covers Python fundamentals, statistical analysis, data manipulation, machine learning techniques, advanced AI concepts, data visualization, and ChatGPT implementation. Participants will gain expertise in Python programming, data cleaning, statistical analysis, machine learning algorithms, and AI ethics. Learners will apply their knowledge to solve practical data science challenges through real-world projects and case studies. The course fosters a collaborative learning environment and culminates in the development of end-to-end data science workflows, empowering graduates to excel in datadriven decision-making and drive innovation in the fields of data science and AI


Practical and hands-on approach with real-world projects enhances problem-solving skills, builds confidence, and aligns graduates with industry demands, fostering a successful Data Science career. Working on real-world data science projects and case studies helps students develop strong problem-solving skills. They learn how to approach and tackle complex data challenges that they may encounter in their future careers.

What you'll learn

  • Python
  • SQL
  • Power BI
  • Machine Learning
  • Data Visualization
  • Artificial Intelligence
  • ChatGPT and Prompt Engineering

Data Science and AI

  • Introduction to Python and its ecosystem
  • Variables, data types, and operators
  • Control flow statements (if, for, while)
  • Functions and modules
  • File handling
  • Exception handling
  • Object-oriented programming in Python
  • Introduction to Statistics for Data Science
  • Descriptive Statistics and Data Visualization
  • Probability and Probability Distributions
  • Sampling and Estimation
  • Hypothesis Testing for Data Science
  • Correlation and Regression Analysis
  • Bayesian Statistics for Data Science
  • Introduction to data manipulation libraries (NumPy, Pandas)
  • Data cleaning and preprocessing
  • Exploratory Data Analysis (EDA)
  • Statistical analysis using Python
  • Data wrangling and feature engineering
  • Data aggregation and grouping
  • Introduction to SQL for data analysis
  • Introduction to Machine Learning
  • Supervised learning algorithms (linear regression, logistic regression, decision trees, random forests, support vector machines)
  • Unsupervised learning algorithms (clustering, dimensionality reduction)
  • Model evaluation and validation techniques
  • Feature selection and extraction
  • Deep learning fundamentals and neural networks
  • Convolutional Neural Networks (CNNs) for image classification
  • Recurrent Neural Networks (RNNs) for sequence data
  • Transfer learning and fine-tuning
  • Natural Language Processing (NLP) techniques
  • Reinforcement Learning basics
  • Introduction to Artificial Intelligence
  • Ethics in Data Science and AI
  • Bias and fairness in AI algorithms
  • Explainable AI and interpretability
  • AI in business and society
  • Introduction to data visualization libraries (Matplotlib, Seaborn, Plotly)
  • Data visualization principles and best practices
  • Creating interactive visualizations
  • Dashboard creation using tools like Tableau, Power BI, or Plotly Dash
  • Effective communication of data insights
  • Introduction to Natural Language Processing (NLP)
  • Understanding ChatGPT and its applications
  • Implementing ChatGPT using Python libraries (e.g., Hugging Face)
  • Training and fine-tuning ChatGPT models
  • Prompt engineering techniques for improving model responses
  • Handling biases and ethical considerations in ChatGPT applications
  • Working on real-world projects and case studies
  • Applying the concepts and techniques learned throughout the course
  • Collaborative projects to simulate team-based data science work
  • Hands-on experience with end-to-end data science workflows
  • Advanced Level of Power BI
  • Enrichment of Data Visualization

Admission Process

Please call to admission counselor for course fees, registration fees, EMI fecilities,registration form and other formalities. Contact to admission counselor

Who can join?

Any graduate with knowledge of besic computing.


1.Personal computer/laptop with webcam and microphone
2.Stable internet connections

Payment details

Bank Details:
Account Number: 19700200000420
IFSC Code: BARB0SALTLA (5th letter is numeric zero)
UPI Payment: 9432257052@okbizaxis

This Course Include:
  • Live Instructor-Led Course
  • Project and Case Studies
  • Certificate of completion
  • Learn from Experts
  • Placement Assistance
  • Assistance for Global Certification