Python Job Oriented Training In Bhopal

Fee: ₹24,000/- ~~₹40,000/-~~(Classroom training), ₹20,000/- ~~₹32,000/-~~(Online training) | Duration: 8 months

Class Modes: Offline and Online classes are available.

Interview Preparation & Placement Support

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Python

Data Science

AI/ML

DJANGO

Python Course Syllabus and Curriculum For 2025

Interview Preparation & Placement Support – "Learn, Practice, and Land Your Dream Job with Confidence!"

Duration: 8 months | Fee: ₹24,000/- ~~₹40,000/-~~(Classroom training), ₹20,000/- ~~₹32,000/-~~(Online training)

Class Modes: Offline and Online classes are available.

Weeks 1-4: Python Basics & Foundations

Week 1: Introduction to Python, Get Started, Syntax, Comments, and Variables
Week 2: Data Types, Numbers, Casting, Strings, and Booleans
Week 3: Operators, Lists, Tuples, Sets, and Dictionaries
Week 4: Control Flow: If...Else, While Loops, For Loops

Weeks 5-8: Intermediate Python Concepts

Week 5: Functions, Lambda Functions, Arrays
Week 6: Classes and Objects, Inheritance, Iterators, Polymorphism
Week 7: Python Scope, Modules, Dates, Math, and JSON
Week 8: Regular Expressions, PIP, Try...Except, User Input, String Formatting

Weeks 9-12: File Handling & Data Science with Python

Week 9: Python File Handling (Reading, Writing, Deleting Files)
Week 10: Introduction to NumPy, Creating Arrays, Indexing, Slicing
Week 11: NumPy Data Types, Copy vs View, Shape, Reshaping, Iterating
Week 12: NumPy Array Operations: Join, Split, Search, Sort, Filter

Weeks 13-16: Advanced NumPy & Data Visualization

Week 13: NumPy Random, Data Distributions, and Seaborn
Week 14: NumPy ufuncs (Arithmetic, Logs, Summation, Trigonometric functions)
Week 15: Introduction to Pandas, Series, DataFrames, Reading CSV/JSON
Week 16: Analyzing Data in Pandas: Cleaning Data, Removing Duplicates, Correlations

Weeks 17-20: Data Science Concepts

Week 17: Data Science Fundamentals, Data Preparation, DataFrames
Week 18: Statistics: Percentiles, Standard Deviation, Variance, Correlation
Week 19: Advanced Data Science: Linear Functions, Plotting, Slope, Intercept
Week 20: Linear Regression, Regression Coefficients, P-Value, R-Squared

Weeks 21-24: Machine Learning Basics

Week 21: Introduction to Machine Learning: Types, Terminology, Clustering
Week 22: Regression Techniques: Linear Regression, Polynomial Regression
Week 23: Classification Algorithms: Decision Trees, Logistic Regression
Week 24: Model Evaluation: Confusion Matrix, ROC Curve, Cross Validation

Weeks 25-28: Deep Dive into Machine Learning

Week 25: K-means Clustering, K-nearest Neighbors
Week 26: Bootstrap Aggregation, Grid Search, Hyperparameters
Week 27: Neural Networks & Deep Learning Introduction
Week 28: TensorFlow Basics, Operations, Models, Training

Weeks 29-32: Final Projects & Advanced Concepts

Week 29: Django: Project Setup, Models, Views, Templates
Week 30: Django CRUD Operations, Admin Panel, User Authentication
Week 31: Introduction to MySQL & MongoDB: Basic Queries
Week 32: Capstone Project: Build a Complete Python Data Science or Web Application