Data Science Training & Certification

2. Types of Applications

3. Web Application Fundamentals

4. Web Technologies: (List key technologies and their roles)

• Frontend: HTML, CSS, JavaScript, React
• Backend: Python, Java, Node.js
• Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

5. Software Development Life Cycle (SDLC)

• Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

6. Application Development Methodologies

• Agile: Core principles, Scrum, Kanban
• Waterfall

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

2. Key Computing Technologies:

• CPU (Central Processing Unit)
• GPU (Graphics Processing Unit)

3. Cloud Computing:

• What is the Cloud?
• Cloud Service Models:
• IaaS (Infrastructure as a Service)
• PaaS (Platform as a Service)
• SaaS (Software as a Service)

1. What is Artificial Intelligence (AI)?

2. How AI Works?

3. Key Concepts:

• Machine Learning (ML)
• Deep Learning (DL)

4. Generative AI:

• What is Generative AI?
• Examples: Large Language Models (LLMs), image generation models.

5. AI in Everyday Learning

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

Python Language Basics: Variables, Conditions, and Functions

Advanced Python Structures: Lists, Tuples, Dictionaries

Virtual Environments and Package Management in Python for Data Science

NumPy for Numerical Data Processing

Pandas for Data Wrangling and Analysis

Introduction to Data Cleaning Techniques with Python

Visualizing Data with Matplotlib and Seaborn

Advanced Data Visualization Techniques

Interactive Data Visualizations with Plotly and Dash

Web Scraping with Python: Beautiful Soup and Scrapy

Utilizing APIs for Data Collection

Strategies for Working with Cloud Data in Python

Object-Oriented Programming in Python

Writing Efficient and Readable Python Code

Error Handling, Debugging, and Unit Testing in Python

Introduction to Vectors, Matrices, and Operations

Eigenvalues and Eigenvectors: Concepts and Applications

Scalars, Vectors, and Tensors: Understanding through Linear Algebra

Fundamentals of Differentiation and Integration in Data Science

Understanding Gradient, Gradient Descent, and Cost Functions

Applications of Calculus in Machine Learning and AI

Foundations of Probability Theory

Descriptive Statistics and Inferential Statistics

Statistical Measures, Distributions, and Hypothesis Testing

Regression Techniques and Their Applications

Analysis of Variance (ANOVA) and Its Use Cases

Implementing Time Series Analysis in Data Science Projects

Understanding Sampling Techniques and Methodologies

Principles of Experimental Design

Conducting A/B Testing and Result Interpretation

Concepts in Supervised, Unsupervised, and Reinforcement Learning

Overfitting, Underfitting, and Model Validation Techniques

Cross-Validation and Hyperparameter Tuning

Overview of Classification Algorithms

Decision Trees, Random Forests, and Gradient Boosting Machines

Model Evaluation Metrics and Techniques

Linear Regression and Its Variants

Understanding Polynomial Regression and Regularization Techniques

Performance Evaluation in Regression Models

Clustering Techniques: K-Means, Hierarchical, and DBSCAN

Introduction to Principal Component Analysis (PCA)

Association Rule Mining: Concepts and Applications

Ensemble Methods: Bagging, Boosting, and Stacking

Feature Engineering and Selection

Introduction to Advanced Algorithms: Neural Networks and SVMs

Basics and Anatomy of Neural Networks

Activation Functions: Types and Their Impact

The Training Process: Backpropagation and Learning Rates

Getting Started with TensorFlow: Installation and Basics

Building Models with Keras: A Gentle Introduction

PyTorch for Deep Learning: Key Features and Differences

Fundamental Concepts of CNNs and Their Architecture

Implementing a CNN for Image Recognition Tasks

Advanced Techniques: Transfer Learning and Fine-tuning

Topics

Understanding RNNs: From Basics to LSTM Networks

Sequence Prediction and Text Generation with RNNs

Challenges and Solutions: Vanishing Gradients and Long-term Dependencies

Exploring Generative Adversarial Networks (GANs)

Autoencoders for Feature Learning and Generation

Reinforcement Learning Basics: Building Intelligent Agents

The Landscape of AI: Defining AI and Its Domains

Rule-based AI vs. Machine Learning-driven AI

Evolution and Key Milestones in AI

Introduction to Text Processing and Analysis

NLP Techniques: From Tokenization to Semantic Analysis

Leveraging NLP Libraries: NLTK, spaCy, and Beyond

Fundamentals of Computer Vision with AI

Implementing Object Detection and Recognition Systems

Advanced Applications: Facial Recognition and Autonomous Vehicles

Ethical AI Development: Challenges and Best Practices

Data Privacy and Security in AI Implementations

The Future of Work and Society with AI

General AI vs. Narrow AI: Understanding the Scope

AI in Robotics: Current State and Future Prospects

The Role of AI in Shaping Future Technologies

Overview and Importance of DevOps in Data Science

Implementing Continuous Integration (CI) and Continuous Deployment (CD) in ML

Containerization with Docker: Basics for Data Scientists

Introduction to MLOps Practices and Tools

Monitoring and Version Control for ML Projects

Managing the Lifecycle of ML Models in Production

Big Data Technologies for ML: Hadoop, Spark, and Beyond

Building Scalable ML Models on Big Data Platforms

Challenges and Solutions in Large-scale ML Deployment

Understanding Data Governance and Compliance

Best Practices for Ethical AI and ML

Navigating Regulatory Requirements for Data Science Projects

Introduction to Generative AI

1. What is Generative AI?

2. Key Applications:

Text (ChatGPT, Claude, LLaMA)

Images (DALL·E, MidJourney, Stable Diffusion)

Audio (Music Generation, Voice Cloning)

Code (GitHub Copilot, Cursor)

3. Evolution of GenAI:

Rule-Based → Deep Learning → Transformers

GANs vs. VAEs vs. LLMs

1. Effective Prompt Design

Instruction-Based, Few-Shot, Zero-Shot

2. Advanced Techniques:

Chain-of-Thought (CoT) Prompting

Self-Consistency & Iterative Refinement

Hands-on:

Optimizing prompts for GPT-4, Claude, LLaMA

Transformer Architecture

1. Why Transformers? (Limitations of RNNs/LSTMs)

2. Key Components:

Self-Attention & Multi-Head Attention

Encoder-Decoder (BERT vs. GPT)

3. Evolution: BERT → GPT → T5 → Mixture of Experts

4. Large Language Models (LLMs)

5. Pre-training vs. Fine-tuning

6. Popular Architectures:

GPT-4, Claude, Gemini, LLaMA 3

BERT (Encoder-based) vs. T5 (Text-to-Text)

Introduction to AI Agents

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

CrewAI + n8n: Automating Business Workflows

Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

Case Studies:

AI Customer Support Agents