Data Science and Advanced Programming

What a program is, and what happens when you run one. Files and folders. The editor, and the three parts of it you need. Your first error, and how to read it.

Workshop

Open the editor, make a file, type a one-line program, run it. Then four small programs and one error on purpose. No terminal, no commands to type — you use the play button.

Bring

A charged laptop, and your Nuvolos enrolment done before you arrive.

What values are, what their types allow, and why the computer's arithmetic sometimes surprises us: str, int, float, bool and None; names and assignment; the arithmetic operators and their order; floats and money; converting between types; reading an error.

Workshop

Short read-and-predict questions in Wooclap, then small programs to write on the same ledger. Optional extra practice for home.

Go further (optional)

• Think Python, chapters 1–2 — values, types, arithmetic, names and assignment; free online. Chapter 2 is the next page.
• CS50P, week 0 and week 3 — Harvard video lectures: variables, int, float and str (week 0); errors like ValueError (week 3).
• Python Tutor: a name is not a formula — step through the ledger lines and watch value stay at 1905.0.

Tuesday 29 September, 10:15–14:00, Internef 263 — the make-up for 21 September.

Many values under one name, work that repeats, and programs that choose: lists, indexing and slicing; for and while loops; if, elif and else; dictionaries.

Workshop

Short read-and-predict questions in Wooclap, then small programs to write on the same ledger. Optional extra practice for home.

Go further (optional)

• Think Python, chapters 9–10 (and 7) — lists, then dictionaries; chapter 7 covers loops. Section 9.9 is "two names, one list".
• CS50P, weeks 1–2 — Harvard video lectures: conditionals (week 1); loops, lists and dicts (week 2).
• Python Tutor: two names, one list — watch both names point at the same list, and .copy() make a second one.

Calling functions and writing your own: def, return and None, returning several values as a tuple. Signatures and arguments: positional, keyword, defaults. Scope, and why a function can change your list but not your number (mutable and immutable). Methods, and which ones change their object. Modules and import: the standard library, random and seeds. Classes and objects: numpy arrays and DataFrames are objects too, and you write a small class with __init__ and self.

Workshop

Read-and-predict in Wooclap: print is not return, two variables called total, a function that changes your list, the weight that lies. Then functions to write for the ledger: position values and a total, a fee with defaults, years to reach a target, a new method for Position.

Go further (optional)

• Think Python, chapters 3 and 6 — defining and calling functions, parameters, local names (chapter 3); return values (chapter 6).
• CS50P, week 0 and week 4 — Harvard video lectures: writing your own functions (week 0, second half); libraries, modules and import (week 4).
• Python Tutor: a function changes your list — watch the list outside change, and the number outside stay at 100.0.

Nested data, one-line loops, and the two libraries all data science in Python is built on: data inside data, comprehensions, lambda, numpy arrays, and pandas tables.

Workshop

Short read-and-predict questions in Wooclap, then small programs to write on the same ledger. Optional extra practice for home.

The working directory; reading and writing files; CSV; a complete load → compute → write script; try/except at reading level; environments as a concept; the AI harness, driven.

Workshop

Read ledger.csv, write report.txt — the whole pipeline.

No new material. Sessions 1–6 revisited through the kind of questions the midterm asks: what does this print, which line is the bug, is this output plausible. A mock midterm, taken in class and discussed.

Written, multiple choice, in class, proctored — no documents or devices. Covers sessions 1–6: what does this print, which line is the bug, is this output plausible. Corrected for guessing (see Assessment).

Data-science program announced at semester start.

Data-science program announced at semester start.

Data-science program announced at semester start.

Data-science program announced at semester start.

Data-science program announced at semester start.

Data-science program announced at semester start.