• Java Syntax & Basics

• Data Types, Variables, Constants

• Operators & Control Structures

• Object-Oriented Programming (Classes, Objects)

• Inheritance, Polymorphism, Abstraction, Encapsulation

• Collections Framework (List, Set, Map)

• Exception Handling

• Java 8+ Features: Lambda, Stream API, Optional

• Multithreading

• ORM with Hibernate / JPA

• Spring Core (Dependency Injection)

• Spring Boot — Starter Projects

• Building RESTful APIs with Spring Boot

• Spring Data JPA

• Security with Spring Security

• REST API Design Principles

• SQL Database

• HTML & CSS

• JavaScript

• Front-End Framework — Angular / React(Any One)

• BackEnd FrontEnd Conncetivity

• Git and Github

• Sample Projects (Hands-On)

• Python Installation & IDE setup

• Syntax, Keywords, Variables

• Data Types (int, float, string, list, tuple, set, dict)

• Operators & Control Statements

• Functions & Lambda Functions

• File Handling

• Exception Handling

• OOP Concepts in Python

• Decorators

• Generators & Iterators

• HTML5

  • Semantic HTML

  • Forms & Tables

  • CSS3

    • Box Model

    • Flexbox & Grid

    • Responsive Design

    • Media Queries

    🔹 JavaScript

    • Basics & ES6

    • DOM Manipulation

    • Events

    • Fetch API / AJAX

    • SQL ,SQLite Database

    • Django Framework

    • Django Architecture (MVT)

    • Project & App Structure

    • URLs & Views

    • Templates & Static Files

    • Models & ORM

    • Migrations

    • Admin Panel

    • Forms & Model Forms

    • Authentication & Authorization

    • Sessions & Cookies

    • CRUD Applications

    • Django Advanced

      • Django REST Framework (DRF)

      • API Views & ViewSets

      • Serializers

      • JWT Authentication

      • Pagination & Filtering

      • Permissions

      • File & Image Upload

      • Email Integration

This Data Science with Python online course is a comprehensive program designed for students, graduates, and working professionals who want to build strong foundations and practical skills in data science, analytics, and machine learning. Conducted by Karishma Nikhil Kochar, the course covers the complete data science workflow—from understanding data and preparing it for analysis to building machine learning models and working on real-world projects. The curriculum is structured to help learners progress step by step, even if they are new to data science, while gradually introducing advanced analytical and modeling concepts.

What Students Will Learn

Introduction to Data Science

• What data science is and how it is used

• Data science lifecycle and workflows

• Types of data: structured and unstructured

• Real-world applications and use cases

• Career roles: Data Analyst, Data Scientist, ML Engineer

Python for Data Science

• Python basics: syntax, variables, and data types

• Control flow and functions

• Object-oriented programming basics

• File handling and exception handling

• NumPy for numerical computing

• Pandas for data manipulation and analysis

• Data visualization using Matplotlib and Seaborn

Mathematics for Data Science

• Statistics fundamentals

• Mean, median, and mode

• Variance and standard deviation

• Probability and data distributions

• Hypothesis testing concepts

• Linear algebra basics

• Correlation and covariance

Data Cleaning & Preprocessing

• Handling missing values

• Outlier detection techniques

• Data transformation methods

• Feature scaling: normalization and standardization

• Encoding categorical variables

• Feature engineering concepts

Exploratory Data Analysis (EDA)

• Data visualization techniques

• Univariate and multivariate analysis

• Identifying trends and patterns

• Extracting insights and reporting findings

Machine Learning

Supervised Learning

• Linear regression

• Logistic regression

• K-Nearest Neighbors (KNN)

• Decision trees

• Random forest

• Support vector machines

Unsupervised Learning

• K-means clustering

• Hierarchical clustering

• DBSCAN

• Principal Component Analysis (PCA)

Deep Learning Basics

• Neural network fundamentals

• Perceptron and artificial neural networks (ANN)

• Introduction to TensorFlow and Keras

• Conceptual understanding of CNN and RNN

SQL for Data Science

• SQL fundamentals

• Joins and subqueries

• Aggregations and window functions

• Using SQL for data analysis

Real-World Projects

• Hands-on projects to apply concepts

• End-to-end data analysis and modeling workflow

• Practical exposure to real datasets

Teaching Method

• Mode: Online live classes

• Structured, step-by-step curriculum

• Concept explanation with practical coding demonstrations

• Hands-on exercises and project-based learning

• Regular doubt-clearing sessions

• Focus on real-world data science workflows

Why This Tutor

Karishma Nikhil Kochar follows a concept-first and application-oriented teaching approach, ensuring learners understand both the theory and practical implementation of data science techniques. The course structure supports gradual skill development, making complex topics approachable for learners from diverse academic backgrounds.

Benefits / Outcomes

• Strong understanding of the complete data science lifecycle

• Practical skills in Python, data analysis, and visualization

• Ability to clean, analyze, and interpret real-world datasets

• Foundational knowledge of machine learning and deep learning

• Improved confidence to work on data-driven projects and roles

Introduction to Data Analysis

• What is Data Analysis?

• Data Analyst vs Data Scientist vs BI Analyst

• Data Analysis Lifecycle

• Types of Data

• Real-world Use Cases

• Statistics & Math for Data Analysis

  • Descriptive Statistics

    • Mean, Median, Mode

    • Variance & Standard Deviation

  • Probability Basics

  • Data Distributions

  • Correlation & Covariance

  • Sampling Techniques

  • Hypothesis Testing (t-test, chi-square – basics)

  • Excel for Data Analysis

    • Excel Interface & Shortcuts

    • Data Cleaning

    • Formulas & Functions

      • VLOOKUP / XLOOKUP

      • IF, COUNT, SUM, AVERAGE

    • Pivot Tables & Charts

    • Conditional Formatting

    • Data Validation

    • Dashboard Creation

    • SQL for Data Analysis

      • SQL Basics

      • SELECT, WHERE, ORDER BY

      • Aggregate Functions

      • GROUP BY & HAVING

      • Joins (INNER, LEFT, RIGHT)

      • Subqueries

      • Window Functions

      • SQL Optimization Basics

      • Python for Data Analysis

        • Python Basics

        • NumPy (Arrays & Operations)

        • Pandas (DataFrames, Series)

        • Data Cleaning & Transformation

        • Data Aggregation

        • Data Visualization

        • Visualization Libraries

          • Matplotlib

          • Seaborn

          ---

          🧹 6. Data Cleaning & Preprocessing

          • Handling Missing Values

          • Outlier Detection

          • Data Formatting

          • Feature Engineering

          • Data Quality Checks

          • Exploratory Data Analysis (EDA)

            • Univariate Analysis

            • Bivariate & Multivariate Analysis

            • Identifying Patterns & Trends

            • Correlation Analysis

            • Business Insights Generation

            • Data Visualization & BI Tools

              • Data Visualization Principles

              • Chart Selection

              • Power BI / Tableau

                • Data Import

                • Data Modeling

                • DAX Basics

                • Interactive Dashboards

                • Business & Domain Knowledge

                  • KPI & Metrics

                  • Business Problem Solving

                  • Storytelling with Data

                  • Stakeholder Communication

                  • Writing Data Reports

                  • Real-World Projects

Introduction to Cloud Computing & AWS

• What is Cloud Computing?

• Cloud Service Models (IaaS, PaaS, SaaS)

• Cloud Deployment Models

• Benefits of AWS

• AWS Global Infrastructure

  • Regions

  • Availability Zones

  • Edge Locations

  • AWS Pricing & Support Plans

  • AWS Identity & Access Management (IAM)

    • IAM Users, Groups & Roles

    • Policies (Managed vs Inline)

    • Least Privilege Principle

    • MFA (Multi-Factor Authentication)

    • IAM Best Practices

    • 3. Compute Services

      • EC2 (Elastic Compute Cloud)

        • Instance Types

        • AMIs

        • Key Pairs & Security Groups

        • Elastic IP

      • Auto Scaling

      • Elastic Load Balancer

        • Application Load Balancer

        • Network Load Balancer

      • Lambda (Serverless Basics)

      • Storage Services

        • S3 (Simple Storage Service)

          • Buckets & Objects

          • Storage Classes

          • Versioning & Lifecycle Policies

          • Encryption

        • EBS (Elastic Block Store)

        • RDS

          • MySQL, PostgreSQL, MariaDB

          • Backup & Multi-AZ

          • Networking & Content Delivery

            • VPC (Virtual Private Cloud)

              • CIDR

              • Subnets (Public / Private)

              • Route Tables

              • Internet Gateway & NAT Gateway

            • Security Groups vs NACL

            • VPC Peering

            • VPN & Direct Connect (Overview)

            • Route 53

            • CloudFront

            • 7. Security & Compliance

              • Shared Responsibility Model

              • AWS KMS

              • Encryption at Rest & in Transit

              • AWS WAF & Shield

              • Secrets Manager

              • Compliance Programs

              • 8. Monitoring, Logging & Management

                • CloudWatch

                • CloudTrail

                  Cost Management & Optimization

                  • AWS Pricing Models

                  • EC2 Purchasing Options

                    • On-Demand

                    • Reserved

                    • Spot Instances

                  • Cost Explorer

                  • Budgets

                  • Cost Optimization Best Practices

                  • SNS

                  • EventBridge

                  • AWS SAA-C03 Exam Pattern

                  • Sample Architecture Questions

                  • Scenario-Based Questions

                  • Practice Tests