Best Data Science Full stack Training Institute in Kottayam, Kerala

HTML5 - The Static Web Page Creation

• HTML5 Introduction
• Structure
• Elements
• Semantics
• Audio & Video
• Section & Article
• Canvas, Aside
• Drag & Drop
• Forms & Form Elements

CSS 3 - The Presentation Semantics

• CSS Properties, Selectors, Style Declaration Types
• Colors, Backgrounds, Text and Fonts
• Images, Links, Tables and List
• Borders, Padding, Margin
• Cursor, Dimension, Scrollbars, Visibility and Positioning
• Pseudo class & Elements, @Rules (import, font-face, charset)
• Filters, Media Types, Printing and Layouts
• Flexbox Layout, Alignment, and Responsive Design

Bootstrap

• Introduction to Bootstrap
• Bootstrap Grid System
• Creating Layouts with Bootstrap
• Bootstrap CSS - Understanding the CSS
• CSS Customization / Skins
• Responsive Web design with Bootstrap
• Single Page Responsive site with Bootstrap
• Bootstrap Layout Components
• Bootstrap Plug-ins: Transition, Modal, Dropdown, Scrollspy, Tab, Tooltip
• Building Websites with Bootstrap

JavaScript - The Interpreted Programming Language

• Interpreted Programming Languages
• Integrating JavaScript with HTML
• Variables in JavaScript
• Operators in JavaScript
• Expressions in JavaScript
• Arrays in JavaScript
• Handling Loops & Decision structures
• Executing Conditional statements
• Working with Functions

TypeScript - The Typed Superset of JavaScript

• Introduction to TypeScript
• Types and Variables
• Functions
• Objects, Interfaces & Classes
• Enums & Generics
• Advanced Type Concepts

AngularJS

• AngularJS Introduction
• Single Page Application (SPA)
• Directives, Filters and Data Binding
• Views, Controllers and Scope
• Modules, Routes and Factories

Python – Understanding the Preliminaries

• Installation and Environment Variables setting
• Statements and Syntax
• Variables Assignment
• Identifiers
• Basic Inputs
• Numbers
• Operators

Conditionals and Loops

• If else statement
• If elif else statement
• Switch
• While loop, For loop
• Break, Continue, Pass

Sequences: String, List and Tuple, Mapping and Set types

• Sequences
• Strings and operators
• String built-in Functions
• List and methods
• Tuples
• Mapping Type: Dictionaries
• Mapping type built-in functions
• Set Type – operators, built-in methods

Object-Oriented Programming

• Object-Oriented Programming
• Classes
• Class Attributes
• Instances
• Instances and Attributes
• Binding and Method Invocation
• Static Methods and Class Methods
• Inheritance

File handling (read/write CSV, JSON)

• Basic File Handling Concepts
• File Built-in Functions
• Handling CSV Files
• Handling JSON Files

Errors and Exceptions

• Exceptions in Python
• Detecting and Handling Exceptions
• Context Management
• Exceptions as Strings
• Raising Exceptions
• Assertions
• Standard Exceptions
• Creating Exceptions

NumPy and Pandas

• Introduction to NumPy
• NumPy arrays and operations
• Random Number Functions in NumPy
• Introduction to pandas
• DataFrames and Series in Pandas
• Reading and Writing data in Pandas
• Data manipulation: filtering, grouping, merging

Data Visualization

• Introduction to Matplotlib and Seaborn
• Line plots, bar charts, histograms, scatter plots
• Customizing plots

Python Framework - Django

• Introduction to Django
• Install Django
• MVC: Model, View and Template
• About the core files: models.py, urls.py, views.py
• Setting up database connections
• Managing Users & the Django admin tool
• Django URL Patterns and Views
• Django Forms

Relational Database Basics

• Brief History of MySQL
• Relational Databases and Popular Databases
• SQL Statements

Data Manipulation Language (DML)

• INSERT
• UPDATE
• DELETE
• SELECT

Data Definition Language (DDL)

• CREATE
• ALTER
• DROP

Sub-Queries, Joins and Unions with MySQL

• Order By, Like, And & Or, Where, Between
• Joins & Unions
• Aggregate Functions and Grouping

Constraints and Normalization

• Understanding Primary and Foreign Keys
• Understanding Database Normalization
• Views and Triggers

Introduction to Data Science

• What is Data Science?
• Data Science lifecycle
• Data Analyst vs Data Scientist
• Real-world applications

Data Wrangling and Preprocessing

• Handling Missing Values
• Removing Duplicates
• Encoding Categorical Variables
• Feature Scaling
• Outlier Detection & Treatment

Exploratory Data Analysis (EDA)

• Univariate Analysis
• Bivariate Analysis
• Multivariate Visualization
• Correlation and Covariance

Statistical Foundations

• Descriptive Statistics
• Probability Basics
• Inferential Statistics

Feature Engineering & Selection

• Feature Engineering
• Feature Selection
• Dimensionality Reduction (Intro Only)

Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning
• Supervised vs Unsupervised Learning
• Reinforcement Learning (Brief Intro)
• Real-world ML use cases

Scikit-learn & ML Workflow Basics

• Overview of Scikit-learn
• Steps in a typical ML workflow
• Importing data
• Preprocessing
• Model training and evaluation
• Basic classification/regression pipeline using Scikit-learn

Supervised Learning Algorithms

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• K-Nearest Neighbors (KNN)
• Support Vector Machines (SVM)

Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• PCA (Dimensionality Reduction)

Model Evaluation & Validation

• Train/test split
• Cross-validation
• Classification Metrics
• Accuracy, Precision, Recall, F1-score, ROC-AUC

Introduction to AI

• What is AI?
• History and evolution of AI
• AI vs ML vs DL
• AI subfields (NLP, CV, Robotics, etc.)

Search Algorithms & Problem Solving

• State space search
• BFS, DFS
• Heuristics and A* Algorithm
• Constraint satisfaction problems

Knowledge Representation & Reasoning

• Logic-based representation
• Propositional and First-order logic
• Inference rules, truth tables

Natural Language Processing

• Text preprocessing: tokenization, stemming, lemmatization
• Bag of Words, TF-IDF
• Sentiment analysis
• Named Entity Recognition (NER)

Neural Networks & Deep Learning Basics

• Perceptron and activation functions
• Feedforward networks and backpropagation
• Keras/TensorFlow intro
• Build and evaluate basic neural networks

AI Applications and Trends

• AI in healthcare, finance, robotics, etc.
• Ethical considerations in AI

• Structured Data: Organized in rows and columns (e.g., Excel, SQL databases)
• Unstructured Data: Data without a fixed format (e.g., images, videos, text)

ML is a subset of AI that allows systems to learn from data and improve without explicit programming

AI is the simulation of human intelligence in machines that can perform tasks like reasoning, learning, and problem-solving.