Write Python Programs
- Use variables, operators, conditions and loops
- Work with core collections
- Read errors and debug common problems
Live Online Python Course for Beginners
Stop Saving Python Tutorials. Start Writing Python.
Start from the fundamentals, build coding confidence and learn how Python is used for automation, data handling and exploratory analysis.
This practical course covers programming logic, functions, object-oriented concepts, file handling, NumPy, Pandas, visualisation and portfolio-ready assignments.
- Beginner-friendly learning path
- Live coding, guided practice and recordings
- Python fundamentals, functions, files and OOP
- NumPy, Pandas, Matplotlib and Seaborn
- Assignments and practical data projects
- No previous coding experience required
Build practical Python confidence
Progress from core logic and reusable code to files, OOP and data analysis.
6–8 weeksPlanned duration
₹15,000Current listed fee
Live onlineInteractive mentor-led sessions
Complete beginnerNo previous coding required
Course guidance
Check Whether Python Is the Right Starting Point
Share your current background, learning goal and preferred batch. A course advisor will explain the curriculum, current fee, schedule and the right progression path after Python.
Direct answer
What Is the Python Programming Course?
A live online Python programming course for beginners and data learners, moving from core logic into functions, files, OOP, NumPy, Pandas, visualisation and exploratory analysis.
Who can join?
Freshers, students, non-technical graduates, working professionals and career switchers. Previous coding experience is not required.
What will you build?
Coding exercises, file-processing assignments, data-analysis notebooks, visualisations and a final exploratory data analysis project.
Problem to solution
You Do Not Need Another Python Playlist. You Need a Learning Order and Practice.
Watching syntax demonstrations can make Python look familiar without making you capable of writing code independently. This course is designed to close that gap.
Coding feels confusingLearn one concept at a time through live demonstrations and guided practice.
Syntax disappears after videosUse every topic in assignments, mini tasks and connected projects.
Python's purpose is unclearSee how it supports automation, analytics, data science, AI and engineering.
Your resume has no coding proofBuild notebooks and documented projects that demonstrate practical ability.
Errors stop your progressLearn debugging, exception handling and problem-solving patterns.
Learning outcomes
What You Will Be Able to Do After the Course
Write, organise, debug and explain Python code independently.
Build Reusable Code
- Create functions with parameters and return values
- Understand scope, modules and packages
- Use object-oriented foundations
Process Files & Text
- Read and write text, CSV and JSON
- Handle exceptions safely
- Search and clean text with regex
Analyse Data
- Use NumPy arrays for numerical operations
- Clean and summarise data with Pandas
- Create charts with Matplotlib and Seaborn
Document Projects
- Build connected coding assignments
- Explain approach and output clearly
- Create a portfolio-ready EDA notebook
Continue Your Path
- Prepare for analytics and data programs
- Apply Python to repetitive work
- Choose the right next learning route
Eligibility
Who Should Join This Course?
Basic computer knowledge, consistent practice and willingness to solve problems are enough. Previous professional or coding experience is not mandatory.
Absolute Beginners
Start with setup, syntax, variables and logic without assumed coding knowledge.
Students & Graduates
Build a programming base before analytics, data science or software learning.
Non-Technical Learners
Develop confidence through practical data and business examples.
Working Professionals
Reduce repetitive work, process files and build reusable workflows.
Data & AI Aspirants
Create the Python base required for analytics, data science, ML and AI.
Career Switchers
Replace random self-learning with guidance, assignments and progression.
Learning experience
How the Learning Journey Works
Understand the Logic
Learn the purpose of each concept before memorising syntax.
Code Live
Follow demonstrations, ask questions and complete guided exercises.
Practise with Assignments
Turn each topic into a short program, file task or data exercise.
Build Connected Projects
Combine concepts into realistic workflows.
Debug and Explain
Understand errors, correct them and explain why your approach works.
Build the Final Project
Use Python, Pandas and visualisation to analyse a realistic dataset.
Continue Your Progression
Move into analytics, machine learning, data science, AI or engineering.
10-module curriculum
Complete Python Programming Course Curriculum
Progress from programming fundamentals into reusable code, files, OOP, numerical computing and data analysis.
Phase 1 · Foundations & LogicEnvironment, syntax, data types, operators, conditions, loops and collections.
Phase 2 · Reusable ProgrammingFunctions, modules, files, exceptions, regex and object-oriented foundations.
Phase 3 · Numerical & Tabular DataNumPy and Pandas for efficient data handling.
Phase 4 · Visualisation & EDACreate charts, identify patterns and document findings.
01Python Setup and Programming FoundationsSet up the environment and build correct habits from your first program.
- What Python is and where it is used
- Installing Python
- Jupyter Notebook and IDE awareness
- Keywords, identifiers and variables
- Literals, comments and indentation
- Script mode and interactive execution
- Basic debugging habits
02Data Types, Operators and Input-OutputWork confidently with values, expressions and interaction.
- Numbers, strings and booleans
- Type conversion
- Arithmetic and comparison operators
- Logical, identity and membership operators
- Indexing and slicing
- Mutability and immutability
- Input, output, expressions and formatting
03Conditions, Loops and Logic BuildingTurn requirements into clear decisions and repeatable logic.
- if, elif and else
- for and while loops
- range
- break, continue and pass
- Nested control structures
- Logic-building exercises
04Python CollectionsStore, transform and combine structured values.
- Lists, tuples and dictionaries
- Sets and frozen sets
- Indexing, slicing and common methods
- Aliasing
- Shallow copy and deep copy
- List comprehensions
- enumerate and zip
05Functions, Modules and PackagesOrganise logic into reusable code.
- Defining functions
- Parameters and return values
- Scope
- Default arguments
- Reusable utilities
- Lambda functions
- Higher-order thinking
- Importing modules
- Packages and environment awareness
06Files, Exceptions and Regular ExpressionsProcess real files and handle problems safely.
- Reading and writing text files
- Working with CSV data
- Working with JSON data
- Exception handling
- Custom error flows
- Regular expressions for search
- Validation and text cleaning
07Object-Oriented ProgrammingModel reusable behaviour with classes and objects.
- Classes and objects
- Attributes and methods
- Constructors
- Instance behaviour
- Inheritance
- Encapsulation
- Reusable design
- OOP use in analytics and engineering projects
08NumPy for Numerical ComputingReplace repetitive loops with efficient numerical operations.
- Array creation
- Indexing and slicing
- Reshaping
- Vectorised operations
- Broadcasting
- Aggregations
- Statistical operations
- Matrix foundations
- Replacing repetitive loops with NumPy
09Pandas for Data HandlingClean, transform and summarise structured data.
- Series and DataFrame structures
- Filtering and sorting
- Transforming data
- Missing values
- Duplicates and data cleaning
- GroupBy
- Merge and join
- Pivot and aggregation
10Data Visualisation and Exploratory AnalysisFind patterns and explain them in a documented notebook.
- Matplotlib chart foundations
- Seaborn distributions
- Comparisons and relationships
- Correlation analysis
- Outlier analysis
- Pattern identification
- EDA observations and conclusions
- Notebook documentation
Tools and environment
Tools Covered in the Course
Each tool is introduced inside a coding, file-processing or data-analysis workflow.
PythonJupyter NotebookVS Code or PyCharmNumPyPandasMatplotlibSeabornCSVJSONGitGitHub Foundations
Projects and portfolio
Projects You Will Build
Personal Expense & Budget Analyser
Read transaction data, classify expenses, calculate monthly summaries and create reusable reports.
Sales Data Cleaning & EDA
Clean a messy sales dataset, handle missing values, analyse revenue patterns and visualise findings.
File-Processing Automation
Combine CSV files, validate records, remove duplicates and generate a clean output file.
Customer Behaviour Notebook
Use Pandas and visualisation to explore customer segments, purchase patterns and business questions.
What You Can Add to Your Portfolio
- Logic-building Python programs
- Reusable function or utility module
- File-processing automation workflow
- NumPy numerical-analysis notebook
- Pandas data-cleaning project
- Documented exploratory data analysis notebook
Why NeoNex MindX
Practice Beyond Syntax
Build confidence through a beginner-first order, live correction, practical assignments and clear progression.
Beginner-First Learning
Start with logic and fundamentals before moving into data libraries.
Live Coding & Correction
Ask questions, identify mistakes and correct code during guided sessions.
Connected Practice
Use each concept in assignments and realistic projects.
Data-Oriented Progression
Move from core programming into NumPy, Pandas, visualisation and EDA.
LMS & Recordings
Revisit class recordings, notes and supporting learning resources.
Assignments & Review
Receive feedback on logic, output quality and explanation.
Completion Certificate
Issued after meeting attendance, assessment and project requirements.
Progression Guidance
Choose analytics, ML, data science, AI/ML Engineering or Data Engineering.
Trainers and mentors
Learn with the NeoNex MindX Trainer Panel
Build technical depth, business understanding, project confidence and communication skills with specialists across the learning journey.
Nikhil
Developer & QA
Sanjana
DevOps
Lakshmi
Data Analytics, Business Analytics, Data Science & AI
Academic and technical perspective across AI, machine learning and structured learning.
LinkedIn profileNeeraj
Data Analytics, Business Analytics, Data Science & AI
5+ years across analytics, data science and AI/ML, with a practical focus on Python, analytics and technical workflows.
LinkedIn profileYashas
Data Analytics, Business Analytics, Data Science & AI
Raveendran
Data Analytics & Data Engineering
Venkata Nagarjuna
Developer & QA
Megha
Communication Skills, Soft Skills & Profile Building
Business and operations perspective for decisions, stakeholder communication and practical application.
LinkedIn profileTrainer allocation may vary by topic and batch. LinkedIn buttons currently use the main LinkedIn URL and can be replaced with individual profile links later.
Career direction
Where Python Skills Can Take You Next
A short Python course builds an important foundation, not an entire career by itself. Use it to strengthen your current role and prepare for deeper programs.
Python InternData Analyst TraineeAutomation AssociateJunior Reporting AnalystData Science LearnerAI/ML Program Aspirant
Continue into the right path
Choose Data Analytics for SQL, Excel, BI tools and analyst-role preparation; Machine Learning for focused modelling; Data Science for broader statistics and projects; AI/ML Engineering for deep learning and MLOps; or Data Engineering for pipelines and cloud data systems.
Course comparison
Python Short Course vs Full Career Program
| Comparison Point | Python Programming Course | Full Career Program |
|---|---|---|
| Primary Goal | Learn Python programming and data handling | Build a complete multi-tool career path |
| Coverage | Python, NumPy, Pandas, visualisation and exploratory analysis | Python plus SQL, statistics, BI tools, projects and role preparation |
| Best Suited For | Learners who want one foundational skill | Learners targeting an analytics, data science or AI career |
| Choose This When | You want focused Python training before a larger program | You need end-to-end readiness across several tools |
Commerce Graduate
Begin without coding and progress into Data Analytics after a sales-data notebook.
Operations Executive
Use file handling and Pandas to reduce repetitive CSV consolidation.
MBA Student
Build Python confidence before Business Analytics or Data Analytics.
Engineering Student
Strengthen logic, OOP and data libraries before Machine Learning.
These learner profiles are illustrative scenarios, not verified learner testimonials.
Fee, batches and enrolment
Course Fee and Live Batch Options
Python Programming Course
₹15,000Current listed fee · planned duration 6–8 weeks · live online training.
Get the SyllabusWeekday MorningMonday to Friday7:00 AM–9:30 AM
Weekday EveningMonday to Friday8:00 PM–10:30 PM
Weekend BatchSaturday and Sunday9:00 AM–1:30 PM
Batch availability and start dates may vary. Confirm the current schedule before enrolment.
Answers before you enrol
Frequently Asked Questions
Is this Python course suitable for complete beginners?
Yes. The course begins with Python setup, syntax, variables, data types and logic. Previous coding experience is not required.
How long is the Python course?
The planned duration is 6–8 weeks, depending on the batch schedule, practice pace and project completion.
Does the course cover Python for data analysis?
Yes. The curriculum includes NumPy, Pandas, Matplotlib, Seaborn and exploratory data analysis.
Will I learn object-oriented programming?
Yes. You will learn classes, objects, methods, constructors, inheritance and reusable program design at a practical foundation level.
Are practical projects included?
Yes. You will work on file-processing, data-cleaning, analysis and visualisation projects using realistic datasets.
Do I need mathematics for this course?
No advanced mathematics is required. Basic arithmetic and logical thinking are enough to begin.
Will I receive recordings and learning materials?
Yes. LMS access includes class recordings, notes and supporting learning resources.
Can working professionals attend?
Yes. Weekday morning, weekday evening and weekend batch options are available, subject to current seat availability.
Will this course make me job-ready as a data analyst?
This focused course builds Python skill. A complete Data Analytics program is recommended when you also need SQL, Excel, Power BI, Tableau, statistics, projects and broader career preparation.
Will I receive a certificate?
A NeoNex MindX course completion certificate is provided after meeting the applicable attendance, assessment and project requirements.
What is the Python course fee?
The current listed fee is ₹15,000. Speak with the course advisor for the next batch date and payment details.
How do I enrol?
Submit the enquiry form, call 84318 03839 or request the syllabus. A course advisor will explain the curriculum, batch options and enrolment process.
Ready to start?
Stop Saving Python Tutorials. Start Writing Python.
Get the complete syllabus, understand the right batch for your schedule and begin with a structured path from coding fundamentals to practical data projects.
10 connected modulesPython, files, OOP and data projectsLive classes, LMS and recordingsProject review and progression guidance
Call 84318 03839