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Python & Data Science

Total Duration : 5.5 Months

Description to Python & Data Science

WITH GENERATIVE AI & AGENTIC AI

Industry Ready Curriculum | Machine Learning

  • LLMs
  • RAG
  • Agentic AI
  • Multi-Agent Systems
  • LLMOps

    • Python is a powerful, versatile programming language widely used in software development, automation, data analysis, and artificial intelligence. Its simple syntax and extensive libraries make it an excellent choice for beginners and experienced developers. Python supports various applications, including web development, scripting, database management, and machine learning. With libraries such as NumPy, Pandas, and Matplotlib, developers can efficiently process data, perform calculations, and create meaningful visualizations.

    Data Science is the process of collecting, cleaning, analyzing, and interpreting large amounts of data to discover useful insights and support better decision-making. It combines programming, statistics, mathematics, and machine learning to identify patterns, predict trends, and solve real-world problems.

    We address your IT concerns
    • Lectures
      Online / Offline
    • Duration
      5.5 Months
    • Category
      Software
    • Language
      English / Hindi / Marathi
    • Certificate
      Yes

    Empeno Journey To Success !

    Introduction to HTML CSS

    The learning path to the course

    Syllabus adapted into EMPENO branding from the supplied curriculum. Training scope and schedule are subject to confirmation.

      Core Python

    • Variables & Data Types
    • Operators
    • Conditional Statements
    • Loops
    • Functions
    • Strings
    • Lists, Tuples, Sets & Dictionaries
    • OOP Concepts
    • Exception Handling
    • File Handling

      • Advanced Python

    • Decorators
    • Generators
    • Iterators
    • Lambda Functions
    • List Comprehensions
    • Modules & Packages
    • Multithreading
    • Multiprocessing
    • Async Programming
    • Logging
    • Virtual Environments
    • API Handling using Requests

      Linear Algebra

    • Scalars, Vectors & Matrices
    • Matrix Operations
    • Eigen Values & Eigen Vectors

      • Statistics

    • Mean, Median & Mode
    • Variance & Standard Deviation
    • Probability Distributions
    • Bayes Theorem
    • Hypothesis Testing

      • Calculus

    • Derivatives
    • Partial Derivatives
    • Gradient Descent

      SQL Fundamentals

    • DDL Commands
    • DML Commands
    • Constraints
    • Primary Keys
    • Foreign Keys

      • Advanced SQL

    • Joins
    • Subqueries
    • Common Table Expressions (CTE)
    • Window Functions
    • Rank & Dense Rank
    • Lead & Lag
    • Views
    • Stored Procedures

      NumPy

    • Arrays
    • Array Indexing & Slicing
    • Broadcasting
    • Vectorization
    • Mathematical Operations
    • Matrix Operations
    • Random Module

      • Pandas

    • Series
    • DataFrames
    • Data Cleaning
    • Missing Values Handling
    • GroupBy
    • Merge & Join
    • Pivot Tables
    • Aggregations
    • DateTime Operations

      • Data Visualization — Matplotlib

    • Line Charts
    • Bar Charts
    • Pie Charts
    • Histograms
    • Scatter Plots

      • Data Visualization — Seaborn

    • Heatmaps
    • Pair Plots
    • Distribution Plots
    • Correlation Analysis

      • Data Visualization — Plotly

    • Interactive Charts
    • Interactive Dashboards

      Topics

    • Univariate Analysis
    • Bivariate Analysis
    • Multivariate Analysis
    • Data Cleaning
    • Feature Engineering
    • Outlier Detection
    • Correlation Analysis
    • Data Transformation

      Supervised Learning

    • Linear Regression
    • Logistic Regression
    • Decision Trees
    • Random Forest
    • KNN
    • Support Vector Machine (SVM)
    • Naive Bayes
    • XGBoost

      • Unsupervised Learning

    • K-Means Clustering
    • Hierarchical Clustering
    • PCA

      • Model Evaluation

    • Accuracy
    • Precision
    • Recall
    • F1 Score
    • ROC Curve
    • Cross Validation

      TensorFlow

    • Artificial Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • LSTM

      • PyTorch

    • Tensors
    • Neural Networks
    • Training Models
    • Model Evaluation

      Natural Language Processing (NLP)

    • Text Cleaning
    • Tokenization
    • Stemming
    • Lemmatization
    • TF-IDF
    • Word Embeddings
    • Word2Vec
    • Transformers
    • BERT
    • Hugging Face Transformers

      • Generative AI — Fundamentals

    • Introduction to LLMs
    • Tokens & Embeddings
    • Attention Mechanism
    • Transformer Architecture
    • Context Windows

      • Popular Models

    • GPT Models
    • Gemini Models
    • Claude Models
    • Llama Models
    • DeepSeek Models

      Prompt Engineering

    • Zero Shot Prompting
    • Few Shot Prompting
    • Chain of Thought
    • ReAct Prompting
    • Structured Output

      • Vector Databases

    • Embeddings
    • OpenAI Embeddings
    • Sentence Transformers
    • ChromaDB
    • FAISS
    • Pinecone
    • Weaviate
    • Similarity Search

      • Retrieval Augmented Generation (RAG)

    • RAG Fundamentals
    • Chunking Strategies
    • Embeddings
    • Vector Search
    • Retriever
    • Re-ranking

      • Advanced RAG

    • Hybrid Search
    • Graph RAG
    • Self RAG
    • Corrective RAG
    • Multi Modal RAG

      LangChain

    • Prompt Templates
    • Chains
    • Memory
    • Tools
    • Agents
    • Output Parsers
    • RAG Pipelines

      • LlamaIndex

    • Data Connectors
    • Indexing
    • Query Engine
    • Data Ingestion
    • RAG Integration

      • Agentic AI — Fundamentals

    • AI Agents
    • Planning
    • Reasoning
    • Reflection
    • Memory
    • Tool Calling

      • Agent Frameworks

    • LangGraph
    • CrewAI
    • AutoGen
    • OpenAI Agents SDK
    • Google ADK

      Agent Patterns

    • Planner Agents
    • Research Agents
    • Coding Agents
    • Supervisor Agents
    • Multi-Agent Collaboration

      • Multi-Agent Systems — CrewAI

    • Agents
    • Tasks
    • Crews
    • Sequential Process
    • Hierarchical Process

      • Multi-Agent Systems — LangGraph

    • State Graph
    • Memory Graph
    • Human-in-the-Loop
    • Agent Orchestration

      • MCP (Model Context Protocol)

    • MCP Fundamentals
    • MCP Architecture
    • MCP Servers
    • MCP Clients
    • Tools
    • Resources
    • Custom MCP Servers
    • Enterprise MCP Integration

      AI Deployment — FastAPI

    • REST APIs
    • Async APIs
    • API Documentation

      • AI Deployment — Docker

    • Containers
    • Docker Images
    • Docker Compose

      • Cloud Deployment

    • AWS EC2
    • AWS S3
    • AWS ECR

      • Kubernetes Basics

    • Pods
    • Services
    • Deployments

      • MLOps

    • MLflow
    • DVC
    • Experiment Tracking
    • Model Versioning

      • LLMOps

    • Prompt Versioning
    • Model Monitoring
    • Guardrails
    • Observability

      • Tools

    • LangSmith
    • Phoenix
    • Weights & Biases

      Topics

    • Hallucination Detection
    • Prompt Injection Prevention
    • AI Guardrails
    • Bias Detection
    • Ethical AI
    • Responsible AI
    • AI Governance
    • Secure AI Applications

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    Introduction to HTML CSS

    The learning path to the course

    While having some prior programming experience is beneficial, it is not always necessary. Many courses are designed for beginners and will cover fundamental concepts in Java and web development. However, familiarity with basic programming concepts can help you grasp the material more easily.

    Completing a Java Full Stack Development course opens up various career paths, including roles such as Full Stack Developer, Java Developer, Software Engineer, Web Developer, and DevOps Engineer. The skills gained are applicable in many industries, making you a valuable asset in the job market.

    What's the Difference?

    Introduction to HTML CSS

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