13 Modules
2 - 3 MonthsOverview
Course Description
The course covers everything from core Python syntax and data structures to advanced topics like object-oriented programming, file handling, and automation. Through hands-on exercises and real-world projects, learners will develop problem-solving skills and gain practical coding experience. By the end of the program, you’ll be confident in writing clean, efficient, and scalable Python code for web, data, or automation tasks.
What you'll learn
- Python basics: syntax, variables, and data types
- Control statements, loops, and functions
- Working with lists, tuples, dictionaries, and sets
- Object-Oriented Programming (OOP) concepts
- File handling and exception management
- Modules, libraries, and virtual environments
- Working with APIs and JSON data
- Introduction to data analysis using Pandas and NumPy
- Basics of web development and automation with Python
- Writing clean, optimized, and reusable code
Course Features:
- Practical coding exercises and assignments
- Mini projects and real-world applications
- Access to popular IDEs and Python tools
- Mentorship from experienced Python developers
- Hands-on sessions with live coding examples
- Code review and performance improvement sessions
- Course completion certificate
Course Content
Installation of Anaconda Prompt
Jupyter Notebook-An Overview
Shorcut Lkeys in Jupyter Notebook
Data Types in Python
Rules for Naming the Variables
List, Tuple, Set, Dictionary
"Introduction to Files and directories"
Introduction to the command prompt or terminal paths
Text files Reading from a text file Opening a file using `with`
If, else if and else condition
For and While Loop
Machine Learning Libraries
Numpy-Hands on
"Pandas-Hands on"
Learn how to explore, visualize, and extract
insights from data
Data Visualization
Matplotlib-Hands on
Seaborn hands on
You need to think statistically and to speak the language of your data
Measures of Central Tendency
Measures of Dispersion
IQR Statistics-Hands-On
Classification, Regression, Fine-tuning your model
Supervised Learning
Unsupervised Learning
Linear Regression
Metrics in Linear Regression Hands-on in Linear Regression
Logistic Regression
Metrics in Logistic Regression
Hands-on in Logistic Regression
Linear regression
Metrics for Linear regression
Introduction to Data Preprocessing
Standardizing Data
Exploratory Data Analysis
Missing Values
Outliers
Standardization
Mnormalization
Feature Scaling and Selection
Decision Tree
Bagging
"Boosting Random Forest"
Neural Network & Project using data science packages,analysis, visualization, create model, extract pure data etc
