Course Overview
- ›Download and Installation of Python
- ›Install Visual Studio Code (VSCode)
- ›Essential Extensions Installation in VSCode
- ›Python input & output functions, Import command, operators in python (operators associativity)
- ›Configure Python Interpreter in VSCode
- ›Setup Integrated Terminal in VSCode
- ›Create and Run a Python Script in VSCode
- ›Install Additional Packages or Libraries (Optional)
- ›Introduction to Python Data Types
- ›Data Types - Numeric, Strings
- ›Data Collections - List, Tuple, Dictionary, Set
- ›Operators in Python
- ›Arithmetic Operators
- ›Comparison Operators
- ›Logical Operators
- ›Assignment Operators
- ›Identity Operators
- ›Membership Operators
- ›Bitwise Operators
- ›Escape Keywords in Python
- ›Overview of Escape Sequences
- ›Common Escape Sequences
- ›Usage of Escape Sequences in Strings
- ›Introduction to Strings
- ›Declaration and Initialization
- ›String Concatenation, Indexing and Slicing
- ›String Formatting
- ›Commonly Used String Methods -Length, find, count, replace + more
- ›Creating and Manipulating Lists
- ›List Comprehensions for Efficient List Operations
- ›Common Use Cases and Best Practices for Lists
- ›Understanding Immutable Nature of Tuples
- ›Creating and Accessing Tuple Elements
- ›Tuples vs. Lists: When to Use Which
- ›Working with Key-Value Pairs in Dictionaries
- ›Dictionary Methods for Data Retrieval and Manipulation
- ›Applications of Dictionaries in Python Programming
- ›Exploring Unique and Unordered Nature of Sets
- ›Set Operations such as Union, Intersection, and Difference
- ›Using Sets for Efficient Data Processing and Deduplication
- ›Conditional Statements(If Statements)
- ›Loops in python(while loops, for loops)
- ›Loop control statement(Break , continue, pass)
- ›Using For Loops to Iterate Over Sequences
- ›Using While Loops with Iteration Control
- ›Conditional Statements Inside Loops
- ›Loops Inside Conditional Statements
- ›Syntax of Function Definition
- ›Parameters and Arguments
- ›Return Statement
- ›Calling Functions
- ›Function Arguments- Default, Data Type, Multilple
- ›Scope of Variables(local, global)
- ›Anonymous Functions (Lambda Functions)
- ›Built-in Functions Overview
- ›Importing of Python internal Modules
- ›Understanding Pre-Built Modules
- ›Overview of Python Standard Library
- ›Commonly Used Pre-Built Modules
- ›math
- ›random
- ›datetime
- ›os
- ›sys
- ›json
- ›csv
- ›re
- ›Creating User-Defined Modules
- ›Importing User-Defined Modules
- ›Importing Entire Module
- ›Importing Specific Functions or Classes
- ›Importing with Alias Names
- ›Using Modules in Python Scripts
- ›Defining Classes (class syntax, attributes and methods)
- ›Creating Objects (Instances) of a Class
- ›Constructor
- ›Inheritance
- ›Polymorphism
- ›Abstraction
- ›Class Methods and Static Methods
- ›Special Methods (Magic Methods) init(), str(), repr(), etc.
- ›Error Handling and Exceptions
- ›Handling exceptions in Python
- ›Custom exceptions
- ›Exception chaining and cleanup actions
- ›Fundamentals of Data Science
- ›Data Collection
- ›Data Cleaning and Preprocessing
- ›Statistical Analysis
- ›Data Visualization
- ›Installation and Setup
- ›Basic Concepts and Operations
- ›NumPy Arrays: ndarray
- ›Array Creation : np.zeros(), np.ones()
- ›Array Indexing and Slicing
- ›Array Operations
- ›Mathematical Functions
- ›Introduction to Series and DataFrame
- ›Basic operations on DataFrame
- ›Reading and writing data
- ›Pandas Operations and Methods
- ›Data Manipulation with Pandas
- ›Data Aggregation and Grouping
- ›Reading CSV Files
- ›Importing Excel Sheets
- ›Accessing Json Files
- ›Basic Plotting
- ›Adding labels, titles, and legends
- ›Creating Multiple Subplots
- ›Advanced Plot Types
- ›Visualization with Widgets
- ›Scatter plot
- ›Bar Graph
- ›Box Plot
- ›Histogram
- ›Accessing Data using Pandas
- ›Handling missing values
- ›Handling Duplicate Values
- ›Feature Scaling
- ›Data Transformation
- ›Overview of Machine Learning
- ›Install Anaconda/Jupyter Notebook
- ›Install Essential Libraries
- ›Data Preparation
- ›Handling and loading data
- ›Feature Extraction
- ›Rendering Categorical Variables
- ›Linear Regression
- ›Decision Tree Regression
- ›Random Forest Regression
- ›Logistic Regression
- ›K-Nearest Neighbor(KNN)
- ›Decision Tree
- ›Random Forest Classification
- ›K-Means clustering
- ›Stock Price Prediction
- ›Heart Disease Classification
- ›Credit card fraud detection
- ›Recommendation System
What we'll cover in this course:
- Installation of Python and VScode Environment Setup
- Python Syntax - DataTypes, Operators, Escape Keywords
- Strings - Strings & Predefined String Methods
- Collections - List, Tuples, Dictionary, Sets
- Conditional Statements & Loops
- Functions & Built-in Functions
- Pre-Built Modules and User Defined Modules
- Object Oriented Programming(OOPS)
- Data Science with Python
- Statistical Analysis with NumPy
- Data Wrangling with Pandas
- Data Visualization with Matplotlib
- Data Preprocessing Technologies
- Machine Learning Setup and Environment
- Handling Categorical Data
- Regression
- Classification
- Clustering
- Projects
Technologies & Tools
VS Code
Core Python
Numpy
