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Software Signal Learning · Foundational

Python Foundations for AI and Data

Learn to read, write and debug small Python programs—then use those foundations to work confidently with structured data.

  • Beginner-friendly starting point
  • Guided practice and debugging
  • Built toward later AI and data learning

Current availability

Applications are reviewed for learners who are new to Python or rebuilding their fundamentals.

Applications not currently open

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Is this the right course for you?

This course starts before AI libraries and machine-learning frameworks. It builds the programming habits needed to approach those tools with less guesswork.

A good fit if you…

  • are new to Python or returning after a long gap;
  • can follow technical instructions but struggle to build a program independently;
  • want guided practice with errors, debugging and problem decomposition;
  • plan to move toward data analysis, automation, AI or machine learning.

Choose another path if you…

  • already write and debug Python programs comfortably;
  • need an advanced machine-learning, MLOps or production-systems course;
  • are looking only for tool demonstrations without programming practice.

Compare both Python courses if you already know the basics.

Learning path

What you will work toward

By completing the guided work and practising consistently, you should be better prepared to:

  • Read Python code and explain how values move through a program.
  • Break a small problem into inputs, decisions, repeated steps and outputs.
  • Use strings, lists, dictionaries and other core collections appropriately.
  • Write reusable functions and organise code across simple modules.
  • Load, validate and transform structured text, CSV and JSON data.
  • Investigate errors systematically with tracebacks, tests and debugging habits.

These are learning objectives, not guaranteed outcomes. Progress depends on attendance, practice, submitted work and individual starting point.

Detailed curriculum

Course syllabus

The syllabus defines the current learning scope. The teaching sequence and exercise mix may be refined before applications open, but any material commercial change will be disclosed before commitment.

  1. Module 1

    Python setup and the programming workflow

    Build a dependable way to create, run and inspect small programs.

    • Python files and the interactive environment
    • Statements, expressions and comments
    • Reading error messages without guessing
    • Simple input, output and program state

  2. Module 2

    Values, variables and expressions

    Represent information clearly and understand how Python evaluates it.

    • Numbers, text, booleans and None
    • Variables and naming
    • Operators, conversions and formatted output
    • Common type-related mistakes

  3. Module 3

    Decisions, loops and problem decomposition

    Turn a written requirement into controlled program behaviour.

    • Conditions and comparison logic
    • if, elif and else
    • for and while loops
    • Tracing and simplifying repeated logic

  4. Module 4

    Strings and core collections

    Choose and manipulate the right structure for a small dataset.

    • String operations and parsing
    • Lists and tuples
    • Dictionaries and sets
    • Iteration, membership, sorting and basic comprehensions

  5. Module 5

    Functions and reusable code

    Move from one long script to code with clear responsibilities.

    • Parameters, return values and scope
    • Function contracts and naming
    • Decomposing a problem into helpers
    • Imports, modules and standard-library discovery

  6. Module 6

    Files and structured data

    Read external data, validate it and produce useful output.

    • Paths and safe file handling
    • Reading and writing text files
    • CSV and JSON fundamentals
    • Cleaning missing, malformed and inconsistent values

  7. Module 7

    Errors, debugging and confidence checks

    Develop a repeatable process for finding faults and protecting expected behaviour.

    • Exceptions and defensive validation
    • Tracebacks and focused debugging
    • Assertions and small test cases
    • Refactoring duplicated or fragile code

  8. Module 8

    Integrated data exercise

    Combine the foundations in a small end-to-end program using a structured dataset.

    • Clarifying the question and expected output
    • Loading, validating and transforming records
    • Summarising results with reusable functions
    • Reviewing correctness, limitations and next improvements

Before you apply

Course facts and delivery status

Format
Delivery format will be confirmed before applications open
Duration and sessions
Will be published before applications open
Schedule
Tentative; dates are not yet confirmed
Minimum cohort
10 learners
Practice and support
Arrangements will be published before applications open
Recordings
Availability and access period will be published before applications open

Applying is free and does not collect payment. The cohort runs only after its published minimum and operational conditions are met.

Commercial terms

Fees, policies and certificates

Total fee
₹40 INR
Deposit
₹10 INR
Remaining fee
₹30 INR

A seat is not confirmed by applying. If an offer is issued, review its current payment deadline and the policies and disclosures before paying.

A certificate is intended to be available, but completion criteria are not yet published. Admission, a seat, placement, employment, income, certification and any particular learning outcome are not guaranteed.