1.4Support TechnologySession 1 · Foundations of AI & Data Science

Algorithms and programming

30classes90htotalTH2 · PR4 · PNW2weighting per class

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30 classes

TH = theory · PR = practical · PNW = personal work. These are ministry weighting codes (not hours) used to split each class's minutes. Class length = course hours ÷ class count; the three time-boxes below always sum to that length.

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First 2 classes free with a referral 29 more with enrollment180 min per classTheory 45mPractical 90mPersonal Work 45m

What Is Programming — Algorithms as Recipes a Computer (and an Agent) Follows

FreeTH2PR4PNW2

A Data/Business Analyst who thinks in algorithms can automate the boring parts of their job — and an AI agent is just an algorithm that picks its own next step.

Theory45m

What an algorithm is: a precise step-by-step recipe
Programming languages vs human language (and vs LLM prompts)
How code becomes action (edit → run → output)

Practical90m

Write an 'algorithm' as plain steps to make a sandwich, then spot ambiguities
Open a Python editor (e.g. Replit/VS Code) and run a provided 'hello' program

Personal Work45m

Write a 5-step algorithm for a daily task you do
Micro-task: submit your algorithm as a short doc

Your First Python Program — print, input and Running Code

LockedTH2PR4PNW2

Locked class preview

An AI Integration Manager's first win is often a tiny script — confidence starts the moment you run your own code

Enroll to unlock every class in this course — the full agenda, the AI tutor, and the quiz.

Algorithms and programming

Class-by-class breakdown

What Is Programming — Algorithms as Recipes a Computer (and an Agent) Follows

Your First Python Program — print, input and Running Code

Variables and Data Types — Storing Information

Numbers and Strings — Text and Math in Code

User Input and Output — Making Programs Interactive

Operators and Expressions — Doing Calculations

Conditionals — Making Decisions with if / elif / else

Boolean Logic in Code — and, or, not

Loops — Repeating with while

Loops — Iterating with for and range

Nested Loops and Early Exit — Patterns and break / continue

Lists — Storing Many Values

List Operations — Indexing, Slicing and Methods

Tuples and Immutability — When Not to Change

Dictionaries — Key-Value Lookup (and How Tool Schemas Describe Arguments)

Strings as Sequences — Slicing and Methods

Functions — Packaging Reusable Steps

Parameters and Return Values — Functions That Communicate

Scope and Variables — Local vs Global

Built-in Functions and Modules — Using What Exists

Reading and Writing Files — Persisting Data (and Saving LLM Output)

Handling Errors — try / except for Robust Programs (and Flaky AI APIs)

Algorithms — Searching a List (Linear Search)

Algorithms — Sorting (Bubble and Selection Sort)

Algorithm Efficiency — The Idea of Doing Less Work

Recursion Basics — Functions That Call Themselves (and Agent Loops)

Putting It Together — A Small Text-Based Project

Debugging — Finding and Fixing Bugs

Good Code Style — Naming, Comments and Readability

Mini-Project — Build and Present a Small Program (AI-Assisted)

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