Data Science

Learn Data Science, Machine Learning, Generative AI, and intelligent systems using real-world datasets, modern AI tools, and practical implementation.

Data Science
AI

What is Data Science?

Data Science is the process of collecting, analyzing, processing, and visualizing data to generate meaningful insights. It combines programming, statistics, machine learning, and artificial intelligence to solve real-world business problems. Data Science is widely used in healthcare, finance, automation, recommendation systems, and intelligent applications.

Data Science

Module 1 : Python + Data Foundations

Learn Python programming concepts required for Data Science, AI systems, APIs, and intelligent applications.

  • Python basics for data work
  • NumPy fundamentals
  • Pandas for data manipulation
  • CSV, Excel, JSON handling
  • Jupyter Notebook
  • Virtual environments (venv, pip)

Module 2 : Data Analysis & Visualization

Most beginners can build models but can’t explain data. That’s useless in real companies.

  • Exploratory Data Analysis (EDA)
  • Data cleaning strategies
  • Missing values & outliers
  • Visualization using Matplotlib & Seaborn
  • Business metrics understanding

Module 3 : Statistics & Math for Data Science

Learn essential statistics and math concepts required for data analysis, machine learning, and AI systems.

  • Mean, median, variance
  • Probability basics
  • Distributions
  • Hypothesis testing
  • Correlation vs causation
  • Linear algebra basics

Module 4 : Machine Learning Fundamentals

Focus on practical ML, not theory overload.

  • Supervised vs unsupervised learning
  • Regression
  • Classification
  • Train/test split
  • Overfitting & underfitting
  • Model evaluation metrics

Module 5 :Advanced ML + Feature Engineering

This is where real-world ML starts.

  • Feature engineering
  • Pipelines
  • Hyperparameter tuning
  • Random Forest/XGBoost
  • Cross-validation

Module 6 : Deep Learning + NLP

Skip unnecessary theory and build things.

  • Neural networks basics
  • TensorFlow/PyTorch intro
  • NLP fundamentals
  • Text preprocessing
  • Embeddings basics

Module 7 : Generative AI for Data Scientists

Data Science without GenAI is already becoming outdated.

  • LLM basics
  • Prompt engineering
  • Embeddings & vector databases
  • RAG systems
  • AI-assisted analytics

Module 8 : Production + Final Project

Most people stop at notebooks. Companies need deployment.

    • FastAPI
    • Model deployment
    • Docker basics
    • MLflow basics
    • Monitoring & logging
    • Final Projects (Pick One)
    • Customer churn prediction API
    • AI-powered analytics assistant
    • Fraud detection dashboard
    • Demand forecasting system

Data Science Demo Form

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