Data Science with Python
The natural next step after Python — NumPy, Pandas, Machine Learning, NLP, and real-world data projects. Build an AI and data science career with your Python foundation.
₹6,000 40 Classes
Programming Foundation Course
Python Programming
Course in Howrah & Kolkata
Master Python — the world's most popular and versatile programming language. From basic syntax and data types to OOP, file handling, database connectivity, CGI, and multithreading — build a rock-solid programming foundation with 400+ hands-on practice problems across 40 expert-guided classes.
Python 3
OOP / OOPs
Functions
Database
CGI
Multithreading
40
Classes
40h
Duration
16
Modules
400+
Problems
10–15
Batch Size
Course Details
What You Get
Everything you need to become a confident Python programmer — from variables and loops to object-oriented design, database connectivity, web CGI, and multithreading — all in one structured, problem-driven course.
40 Classes · 40 Hours
A comprehensive, expert-paced programme covering every aspect of Python programming — from history and basic syntax to OOPs, file handling, exception management, database connectivity, CGI web programming, and multithreading for real-world applications.
ISO & MSME Certificate
Earn a government-recognized, ISO-certified completion certificate that adds genuine credibility to your resume and proves professionally verified Python Programming training to employers, IT companies, and data-driven organizations across India.
400+ Practice Problems
Solve over 400 real programming questions across all modules — 50+ on basics, 150+ on loops, 30+ on conditionals, and dozens more on strings, lists, functions, OOPs, and database programming — building genuine problem-solving muscle memory.
Small Batch Sizes
Only 10–15 students per batch ensures personal attention, faster code reviews, and a genuinely productive learning environment where every student masters every concept — from loops to multithreaded programs — with full understanding and confidence.
OOPs & Advanced Python
Go beyond the basics with full object-oriented programming — classes, objects, inheritance, polymorphism, overloading, overriding, and data hiding — plus CGI web programming, database connectivity with MySQL, and Python multithreading for high-performance applications.
Real-World Applications
Apply Python to real scenarios — build database-driven programs, web applications via CGI, file I/O utilities, exception-safe software, and multithreaded systems — gaining practical experience that hiring managers and clients immediately recognize as valuable.
Full Curriculum
Course Syllabus
5 comprehensive module groups — Python Basics, Data Structures, Functions & Modules, Advanced Topics, and Real Applications — covering all 16 chapters in depth.
Group 1: Python Basics & Control Flow
Start your Python journey from the very beginning. Learn the history and philosophy of Python, set up your development environment, and master the foundational building blocks — variables, data types, operators, conditional logic, and loops. Solve 230+ real programming problems to build deep, lasting fluency with core Python control flow before moving to more complex topics.
Chapters 1–4SyntaxVariablesLoopsConditionals230+ Problems
Ch 1
Introduction to Python
History and evolution of Python — from Guido van Rossum's 1989 project to the world's most popular language. Key features: readability, interpreted execution, dynamic typing, extensive standard library, and cross-platform compatibility. Setting up the Python path and working with the interactive interpreter (REPL). Understanding the Python execution model. Basic syntax — indentation rules, comments, print statements. Variables and data types — int, float, str, bool, complex, None. Arithmetic, comparison, assignment, logical, bitwise, and membership operators with real examples.
50+ Problems
Ch 2
Conditional Statements
Decision-making in Python — how programs choose different paths based on conditions. The
if statement for single-condition branching. if-else for binary decisions and the ternary operator shorthand. Nested if-elif-else chains for multi-way decisions — real-world examples like grade calculators, tax brackets, and menu-driven programs. Understanding truthiness and falsy values in Python. Combining conditions with and, or, and not. Common beginner pitfalls — assignment vs. comparison.
30+ Problems
Ch 3
Looping — for, while & Nested Loops
Iteration — the engine of programming. The
for loop with range(), iterating over strings, lists, and other sequences. The while loop for condition-driven repetition — infinite loops and how to avoid them. Nested loops — patterns, multiplication tables, matrix printing, and pyramid problems. Loop control with break, continue, and the else clause on loops. List comprehensions as a Pythonic alternative to simple for loops. Real problems: FizzBuzz, prime checker, Fibonacci series, factorial, number reversal, and pattern printing.
150+ Problems
Ch 4
Control Statements — break, continue & pass
Fine-grained loop control with Python's jump statements.
break — exiting a loop entirely on a condition (e.g., searching a list, early termination). continue — skipping the current iteration and moving to the next (e.g., filtering even/odd numbers). pass — the null statement for syntactically required but logically empty blocks, useful in stubs and placeholders. Combining control statements in real search and filter algorithms. Understanding when to use each — and how overuse of break creates unreadable spaghetti code.
30+ Problems
Group 2: Strings, Lists, Tuples & Dictionaries
Python's built-in data structures are its superpower. Master string manipulation with 50+ built-in methods. Work with mutable lists, immutable tuples, and key-value dictionaries — the foundational data types used in every real Python program from scripts to web apps to data science. Build practical programs using each structure and understand when to choose which container.
Chapters 5–8StringsListsTuplesDictionaries60+ Problems
Ch 5
String Manipulation
Strings as sequences — indexing, negative indexing, and the concept of immutability. String slicing — extracting substrings with start, stop, and step parameters. Basic string operations — concatenation, repetition, membership testing with
in. Essential string methods: upper(), lower(), strip(), split(), join(), replace(), find(), count(), startswith(), endswith(), format(), and f-strings. String formatting with % operator and .format(). Real applications: palindrome checker, word counter, Caesar cipher, string compression.
50+ Problems
Ch 6
Lists
Lists — Python's most versatile and widely-used data structure. Creating and initializing lists — literals, list comprehensions, and
list(). Accessing elements: indexing, negative indexing, and slicing. List mutability — in-place modification vs. creating new lists. Operations: concatenation, repetition, and membership testing. Core methods: append(), insert(), remove(), pop(), sort(), reverse(), index(), count(), copy(), extend(). Working with 2D lists (matrices). Sorting with key functions. Real programs: student marks manager, inventory system.
15+ Problems
Ch 7
Tuples
Tuples — immutable sequences for fixed collections of data. Creating tuples: single-element tuples (the comma gotcha), empty tuples, and tuple unpacking. Accessing and slicing tuples — same syntax as lists, different semantics. Why immutability matters — tuple use cases vs. lists: dictionary keys, function returns, data integrity. Tuple operations: concatenation, repetition, membership, and iteration. Built-in functions:
len(), max(), min(), sum(), sorted(). Named tuples for readable data records. Swapping variables with tuple unpacking.
15+ Problems
Ch 8
Dictionaries
Dictionaries — Python's hash-map implementation for fast key-value storage. Creating dictionaries: literals,
dict() constructor, and dictionary comprehensions. Accessing values — dict[key] vs. dict.get(key, default). Modifying, adding, and deleting key-value pairs. Key properties — uniqueness, immutability of keys. Essential methods: keys(), values(), items(), update(), pop(), setdefault(), copy(). Iterating over dictionaries. Nested dictionaries. Real applications: phone book, word frequency counter, student grade tracker, inventory manager.
15+ Problems
Group 3: Functions, Modules, I/O & Exception Handling
Write reusable, modular, and robust Python code. Master function definitions, argument types, and anonymous lambda functions. Organize code with Python modules and packages. Handle user input, file reading/writing, and build exception-safe programs using Python's powerful try-except-finally system. These skills separate beginner code from professional, production-ready Python.
Chapters 9–12FunctionsModulesFile I/OException Handling60+ Problems
Ch 9
Functions
Defining and calling functions — the
def keyword, return values, and the importance of the DRY principle. Types of functions: built-in functions, user-defined functions, and anonymous functions. Function arguments: positional, keyword, default values, *args (variable positional), and **kwargs (variable keyword). Recursion — base cases, recursive cases, and the call stack. Anonymous functions with lambda and their use with map(), filter(), and sorted(). Global vs. local variables and the global keyword. Closures and first-class functions introduction.
15+ Problems
Ch 10
Modules & Packages
Code organization with Python modules — creating, importing, and using your own modules. The
import statement, from...import, and aliasing with as. Python's built-in module ecosystem — the standard library. The math module: sqrt(), ceil(), floor(), pi, e, log(), pow(). The random module: random(), randint(), choice(), shuffle(). Understanding packages and the __init__.py file. Module composition and the __name__ == '__main__' guard. Installing third-party packages with pip.
15+ Problems
Ch 11
Input / Output & File Handling
User interaction with
input() — reading and type-converting keyboard data. Formatted output with print(), f-strings, and .format(). File handling — the complete lifecycle: open, read, write, close. File modes: 'r', 'w', 'a', 'rb', 'wb'. Reading files: read(), readline(), readlines(). Writing and appending to files. The with statement for automatic resource management. Working with CSV files using the csv module. File system operations with os module: listing directories, checking existence, renaming, deleting.
15+ Problems
Ch 12
Exception Handling
Understanding errors and exceptions — syntax errors vs. runtime exceptions vs. logical errors. Python's exception hierarchy —
BaseException, Exception, and common built-in exceptions: ValueError, TypeError, ZeroDivisionError, FileNotFoundError, IndexError, KeyError. The try-except block — catching specific exceptions and multiple exceptions. The else clause on try blocks. The finally clause for guaranteed cleanup code. Raising exceptions with raise. Creating user-defined custom exceptions by subclassing Exception. Writing robust, production-safe Python code.
15+ Problems
Group 4: Object-Oriented Programming (OOPs)
Object-Oriented Programming is the paradigm that powers every major Python framework — Django, Flask, Pandas, TensorFlow. Master classes and objects, the four pillars of OOP — encapsulation, inheritance, polymorphism, and abstraction — and advanced features like method overloading and data hiding. Write code that is reusable, scalable, and designed the way professional Python developers build real applications.
Chapter 13ClassesInheritancePolymorphismEncapsulation10+ Problems
13.1
Classes & Objects
The object-oriented paradigm — thinking in terms of real-world entities. Defining classes with the
class keyword. Instance variables and the __init__ constructor. self parameter — the implicit reference to the current instance. Instance methods, class methods (@classmethod), and static methods (@staticmethod). Creating and using object instances. The __str__ and __repr__ magic methods for string representations. Object identity vs. equality. Real class designs: BankAccount, Student, Car, ShoppingCart.
13.2
Attributes & Encapsulation
Instance attributes vs. class attributes — understanding the difference and when to use each. Public, protected (
_name), and private (__name) attributes — Python's name mangling mechanism. The @property decorator for controlled attribute access — getters and setters without breaking the interface. Class-level constants. Encapsulation — bundling data and methods, hiding internal state, and exposing a clean API. __dict__ and dir() for object introspection. Dynamic attribute access with getattr(), setattr(), hasattr().
13.3
Inheritance
Reusing and extending code through inheritance — the "is-a" relationship. Single inheritance: creating child classes that inherit methods and attributes from parent classes. The
super() function for calling parent constructors and methods. Method Resolution Order (MRO) — how Python finds methods in class hierarchies. Multiple inheritance in Python and its diamond problem solution via C3 linearization. Multi-level inheritance chains. isinstance() and issubclass() for type checking. Abstract base classes with the abc module. Real examples: Animal → Dog → GuideDog, Shape → Circle → FilledCircle.
10+ Problems
13.4
Overloading, Overriding & Data Hiding
Method overriding — redefining a parent method in a child class for specialized behavior. Operator overloading using Python's dunder (magic) methods:
__add__, __sub__, __mul__, __eq__, __lt__, __len__, __contains__. Building custom classes that behave like built-in Python types. Duck typing and polymorphism — writing code that works with any object that has the right methods. Data hiding with name mangling (__private) and the philosophy of "we're all consenting adults." Practical polymorphism: a shape area calculator that works with any shape class.
Group 5: CGI, Database & Multithreading
The most advanced and career-relevant group — where Python meets the real world. Learn CGI-based web programming with Python for dynamic web content, connect Python to MySQL and other databases for persistent data storage, and harness multithreading for concurrent, high-performance programs. These are the skills that appear in job descriptions for Python developers, backend engineers, and automation specialists.
Chapters 14–16CGIMySQLMultithreading30+ Problems
Ch 14
CGI — Common Gateway Interface
What is CGI — the protocol connecting web servers to Python programs for dynamic content generation. CGI architecture — browser → web server → CGI script → response flow. Setting up a Python CGI environment with Apache or Python's built-in HTTP server. CGI environment variables —
REQUEST_METHOD, QUERY_STRING, CONTENT_TYPE, HTTP_HOST. GET vs. POST methods — reading form data with the cgi module and FieldStorage. Handling cookies — setting, reading, and managing session state. File upload via CGI — reading binary data from HTTP POST. Building a simple dynamic web form application.
10+ Problems
Ch 15
Database Connectivity with Python
Introduction to database programming — why persistent storage matters. Connecting Python to MySQL using
mysql-connector-python or PyMySQL. The DB-API 2.0 interface — connection, cursor, execute, fetch, commit, close. Executing SQL queries from Python: CREATE TABLE, INSERT, SELECT, UPDATE, DELETE. Parameterized queries — preventing SQL injection attacks. Fetching results: fetchone(), fetchall(), fetchmany(). Transaction management — commit() and rollback(). Error handling for database operations. Building a CRUD application: student records manager with Python + MySQL.
10+ Problems
Ch 16
Multithreading
Concurrency vs. parallelism — and why it matters for Python programs. Threads vs. processes — understanding Python's GIL (Global Interpreter Lock) and its implications. The
threading module — creating threads with Thread class. Starting threads with start() and join(). Thread synchronization — race conditions, deadlocks, and using Lock, RLock, and Semaphore to protect shared resources. Thread-safe programming patterns. The threading.local() for thread-local storage. Multithreaded Priority Queue with queue.Queue — the producer-consumer pattern for building efficient, concurrent data pipelines in Python.
10+ Problems
What You'll Learn
Learning Outcomes
Graduate with the Python skills needed for developer, analyst, and automation roles — with 400+ solved problems proving your programming capability to any employer.
Write Real Python Programs
Use Python's complete syntax — variables, operators, control flow, functions, and data structures — to write clean, readable, and efficient programs that solve real problems from day one on the job.
Master Data Structures
Confidently use Python's built-in data structures — strings, lists, tuples, and dictionaries — to store, process, and transform data the way professional Python developers do every day in production code.
Design Object-Oriented Software
Apply OOP principles — encapsulation, inheritance, polymorphism, and abstraction — to design modular, reusable software systems the same way Python frameworks like Django and Flask are architectured.
Handle Exceptions & Errors
Write robust, crash-resistant programs using Python's exception handling system — try, except, else, finally, and custom exceptions — producing professional code that handles real-world failures gracefully.
Connect Python to Databases
Build database-driven Python applications — connecting to MySQL, executing CRUD operations, handling transactions, and using parameterized queries to write secure, SQL-injection-proof database code.
Build Multithreaded Applications
Write concurrent Python programs using the threading module — creating threads, synchronizing shared resources with locks, and building producer-consumer systems for high-performance, real-world automation.
Who Should Join?
This Course Is For You
Whether you're a complete beginner, a student, or someone looking to switch careers — this Python course builds the solid programming foundation every professional developer, data scientist, and AI engineer needs.
🎓
Students & Freshers
BCA, B.Tech, B.Sc, and MCA students who want to add Python programming skills — the most in-demand language for jobs in software development, data science, AI, automation, and every technology field.
💼
Career Changers
Professionals from non-IT backgrounds — accounting, management, teaching, or operations — who want to learn Python as their first programming language and enter the technology sector with confidence.
📊
Data Science Aspirants
Anyone planning to pursue our Data Science, Machine Learning, or AI courses — Python is the essential prerequisite and this course gives you the perfect, purpose-built programming foundation before stepping into data science.
🐍
Self-Taught Developers
Self-taught programmers who've learned bits and pieces online and want a structured, comprehensive Python course that fills every gap — from OOPs to databases to multithreading — taught by an expert instructor.
FAQ
Frequently Asked Questions
What is the fee for the Python Programming course at PBA Institute?
The batch class fee is ₹5,000 for the complete 40-class Python Programming course (40 hours). One-to-One personalized sessions are available at a higher rate with dedicated instructor attention and fully flexible scheduling. Both options include study materials, software installation support, and an ISO-certified completion certificate.
Do I need any prior programming experience to join the Python course?
Absolutely not. The Python Programming course is designed for complete beginners — it starts from Python's history and basic syntax, and progresses step-by-step to advanced topics like OOPs, database connectivity, CGI, and multithreading. No prior coding experience is needed. All you need is curiosity and the willingness to practice.
How many practice problems will I solve in this course?
You will solve over 400 real programming problems across all 16 chapters — 50+ on Introduction, 30+ on Conditionals, 150+ on Loops, 30+ on Control Statements, 50+ on Strings, and 15+ each on Lists, Tuples, Dictionaries, Functions, Modules, I/O, and Exception Handling. Each chapter ends with solved practice problems so you build genuine coding confidence.
Should I take Python Programming before joining the Data Science course?
Yes, we strongly recommend this sequence. Our Python Programming course gives you the solid foundation — syntax, functions, OOPs, file handling — that makes the Data Science course significantly more effective and enjoyable. If you already know Python basics (variables, functions, loops), you may be able to join Data Science directly — contact us for a free assessment.
Can I attend the Python course online from outside Howrah?
Yes. PBA Institute offers fully live online Python classes with the same instructor — screen sharing, live coding demonstrations, real-time problem solving, and doubt resolution in every class. Students from across West Bengal and India attend online and receive the same ISO-certified certificate upon completion of the course.
Is Python a good career choice in 2025 and beyond?
Python is consistently ranked the #1 most popular programming language globally by the TIOBE Index and Stack Overflow surveys. It powers data science, machine learning, web development (Django, Flask), automation, cybersecurity, and AI. In India, Python developer salaries range from ₹3–20+ LPA. Learning Python in 2025 is one of the highest-ROI career investments you can make.
Start Your Python Journey Today
Ready to Master Python?
Join PBA Institute's Python Programming course in Howrah. Learn from basics to OOPs, database, CGI, and multithreading — solve 400+ real problems, earn an ISO certificate, and unlock a world of tech careers across India.
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