Introduction to Coding and Data Analysis for Scientists

Week 1: Introduction and Data Types

Today’s Lecture

Lecture 1: Introduction and Data Types
- Course Structure
- Setting up Noteable
- Introduction to Coding
- Data Types
- Practical

Course Structure

This course will run for 20 weeks
We will cover:
- Data types, conditionals and loops
- Building functions
- Numpy arrays and linear algebra in python
- Pandas dataframes
- Plotting and visualisation
Classes:
- 1 lecture per week (Thursday)
- 1 small-group tutorial per two weeks (date should be on calendar)

About us

Dr Francesco Turci (Course director, School of Physics)
- f.turci@bristol.ac.uk
Dr Thomas Maullin-Sapey (lecturer, School of Mathematics)
- thomas.maullin-sapey@bristol.ac.uk

Lectures

The weekly lecture will consist of
- ≈ 30-40 minutes presentation time covering:
- Key concepts
- Administrative points (e.g. homework submission etc)
- Code Demonstrations#
≈ 1-1.5 hours practical
Designed to provide hands on experience
Opportunity to ask for help from
- Lecturer
- PhD helpers
- Each other!

Assignments

Formative:
- Weekly (optional) assignments available on quarto
Summative:
- This module is 100% coursework
- 4 pieces of coursework
- Assignment 1 (15%)
- Assignment 2 (30%)
- Assignment 3 (15%)
- Assignment 4 (40%)
Assignments 1-3 will be exercises
Assignment 4 will be different
- Project report analysing a dataset

Online Material

The course material is available online via:
- Quarto – online searchable version
- Noteable – interactive interface we shall work in during class

Welcome page for the unit on Blackboard

Accessing Noteable

Open Blackboard
Go to Introduction to Coding and Data Analysis for Scientists 2025
Click Unit Information and Resources
Open Noteable
- Make sure Jupyter Classic (Legacy) is selected.
- Click Start
Click +GitRepo
Paste into Git Repository URL: git@github.com:TomMaullin/SCIF10002-2025.git
Press clone

Welcome page for the unit on Blackboard

Introduction to Coding

In this module, we shall be learning to code in Python
Python is a programming language

Welcome page for the unit on Blackboard

In the same way humans communicate using different languages, there are many languages we can use to communicate with a computer
Python is particularly useful for:
- Analysing data
- Making plots and visualisations
- Running simulations
- Machine learning and AI

Learning to Code

In fact, learning to code is a lot like learning a language
When learning French, you might:
- Learn many phrases
- Practice writing sentences
- Speak to people who know the language
You can’t learn a language by listening to other people describe it…
Hands-on experience and regular practice are crucial!

Getting Started

So… what actually is coding?
You can think of writing code as like writing a recipe…
- You might start by specifying some ingredients…
- Then list some instructions…
- To get a desired output
Coding is pretty similar

Welcome page for the unit on Blackboard

Getting Started

You might start by specifying some inputs…
Then list some instructions…
To get a desired output

Here we are producing some output that might be difficult to compute or evaluate by hand.

Assigning Variables

In this code, x and y are variables
These are named pieces of data which we can use for future computations
The = symbol is the assignment operator.
- Unlike in maths, this is an instruction
- E.g. x=7 means “Save the integer 7 under the variable name x”

Data Types

When writing the recipe, some foods were of the “same type”
- E.g. we had 2 eggs
In the same way, in coding we have different types of data
Today, we shall look at some of these
Understanding data types is important, as we want to use different data types for different tasks

Numeric Data Types

The most ubiquitous data types are numeric
- Integers: Whole numbers/Integers
- ℤ = {…, -3, -2, -1, 0, 1, 2, 3,… }
Floats: Decimal numbers
- ℝ = Real numbers
- We can convert between float’s and integers using the float and int functions.
What do you think might happen if we convert y to an integer?

Warning: A computer cannot actually describe arbitrarily small and large numbers – it approximates instead

Strings

A string is a sequence of characters. Strings can contain:
- Letters
- Numbers (treated as characters)
- Punctuation
- Spaces
- Combinations of the above
- Nothing at all

In practice, there are many things we may want to do with text in Python
- Split it into sentences
- Search through it
- Replace words

After todays practical you will be able to perform some of these operations!

Booleans

A Boolean is variable that can be either True or False
Booleans represent logical statements.
For instance, we might think of:
- cat_is_black as representing the sentence “The cat is black”
- cat_has_four_legs as representing the sentence “The cat has four legs”
We can use logical operators to combine Boolean statements
- cat_is_black and cat_has_four_legs represents the sentence “The cat is black and has four legs”

Booleans

Welcome page for the unit on Blackboard

Collections

We’ve now seen some simple examples of datatypes:
- Numeric, strings, and booleans.
Data types don’t always have to be this simple!
- Sometimes we need data types that can hold more complex information.
We’ll see more examples throughout the course but for now we’ll provide just one
A collection is any in-built data type that can group multiple objects together.
The most common collection is a list: an ordered group of items.
- Lists let you store, organize, and work with many values at once.

Practical

We have students from a range of courses and backgrounds in this class
- Chemistry
- Physics
- Data Science
Some people in the room will have less experience than others
For the first few weeks, we shall try to account for the differences in ability where possible

Practical

We now move over to Python
Please open week_01_home.ipynb
For the rest of today, you must work through a Python notebook
You have a choice of one of three options

--- title: Introduction to Coding and Data Analysis for Scientists jupyter: python3 --- ## Week 1: Introduction and Data Types ## Today's Lecture - Lecture 1: Introduction and Data Types - Course Structure - Setting up Noteable - Introduction to Coding - Data Types - Practical ## Course Structure - This course will run for 20 weeks - We will cover: - Data types, conditionals and loops - Building functions - Numpy arrays and linear algebra in python - Pandas dataframes - Plotting and visualisation - Classes: - 1 lecture per week (Thursday) - 1 small-group tutorial per two weeks (date should be on calendar) # About us - Dr Francesco Turci (Course director, School of Physics) - f.turci@bristol.ac.uk - Dr Thomas Maullin-Sapey (lecturer, School of Mathematics) - thomas.maullin-sapey@bristol.ac.uk ## Lectures - The weekly lecture will consist of - ≈ 30-40 minutes presentation time covering: - Key concepts - Administrative points (e.g. homework submission etc) - Code Demonstrations# - ≈ 1-1.5 hours practical - Designed to provide hands on experience - Opportunity to ask for help from - Lecturer - PhD helpers - Each other! ## Assignments - Formative: - Weekly (optional) assignments available on quarto - Summative: - This module is 100% coursework - 4 pieces of coursework - Assignment 1 (15%) - Assignment 2 (30%) - Assignment 3 (15%) - Assignment 4 (40%) - Assignments 1-3 will be exercises - Assignment 4 will be different - Project report analysing a dataset ## Online Material - The course material is available online via: - Quarto – online searchable version - Noteable – interactive interface we shall work in during class <img src="./images/noteable.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:80%"/> ## Accessing Noteable - Open Blackboard - Go to `Introduction to Coding and Data Analysis for Scientists 2025` - Click `Unit Information and Resources` - Open Noteable - Make sure `Jupyter Classic (Legacy)` is selected. - Click Start - Click `+GitRepo` - Paste into Git Repository URL: `git@github.com:TomMaullin/SCIF10002-2025.git` - Press clone <img src="./images/noteable2.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:20%"/> ## Introduction to Coding - In this module, we shall be learning to code in Python - Python is a programming language <img src="./images/python.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:20%"/> - In the same way humans communicate using different languages, there are many languages we can use to communicate with a computer - Python is particularly useful for: - Analysing data - Making plots and visualisations - Running simulations - Machine learning and AI ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} def greet(name): """Return a friendly greeting.""" return f"Hello, {name}!" greet("Bob") ``` ## Learning to Code - In fact, learning to code is a lot like learning a language - When learning French, you might: - Learn many phrases - Practice writing sentences - Speak to people who know the language - You can't learn a language by listening to other people describe it… - Hands-on experience and regular practice are **crucial!** ## Getting Started - So… what actually is coding? - You can think of writing code as like writing a recipe… - You might start by specifying some ingredients… - Then list some instructions… - To get a desired output - Coding is pretty similar <img src="./images/recipe.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:20%"/> ## Getting Started - You might start by specifying some **inputs**… - Then list some **instructions**… - To get a desired **output** ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Start with two ingredients (inputs) x = 7 y = 4 # Step 1: Add them together step1 = x + y # Step 2: Multiply the result by 2 step2 = step1 * 2 # Step 3: Subtract y from that z = step2 - y # Final output print("The final result is:", z) ``` Here we are producing some output that might be difficult to compute or evaluate by hand. ## Assigning Variables ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} x = 7 y = 4 ``` - In this code, `x` and `y` are variables - These are named pieces of data which we can use for future computations - The `=` symbol is the assignment operator. - Unlike in maths, this is an instruction - E.g. `x=7` means "Save the integer 7 under the variable name x" ## Data Types ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} x = 7 # an integer y = "hello" # a string of text z = True # a boolean (true/false) ``` - When writing the recipe, some foods were of the “same type” - E.g. we had 2 eggs - In the same way, in coding we have different types of data - Today, we shall look at some of these - Understanding data types is important, as we want to use different data types for different tasks ## Numeric Data Types ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # Start with an integer and a float x = 7 # integer y = 3.5 # float (decimal number) # Convert integer to float x_as_float = float(x) # Convert float to integer y_as_int = int(y) ``` - The most ubiquitous data types are numeric - Integers: Whole numbers/Integers - ℤ = {…, -3, -2, -1, 0, 1, 2, 3,… } - Floats: Decimal numbers - ℝ = Real numbers - We can convert between float's and integers using the `float` and `int` functions. - What do you think might happen if we convert `y` to an integer? **Warning:** A computer cannot actually describe arbitrarily small and large numbers – it approximates instead ## Strings - A string is a sequence of characters. Strings can contain: - Letters - Numbers (treated as characters) - Punctuation - Spaces - Combinations of the above - Nothing at all ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" # Letters letters = "Hello" # Numbers (treated as characters) numbers = "12345" # Punctuation punctuation = "!?.,;;" # Spaces spaces = " " # three spaces # Combinations of the above combo = "Hi123?! " # Nothing at all (an empty string) empty = "" ``` - In practice, there are many things we may want to do with text in Python - Split it into sentences - Search through it - Replace words After todays practical you will be able to perform some of these operations! ## Booleans - A Boolean is variable that can be either `True` or `False` - Booleans represent logical statements. - For instance, we might think of: - `cat_is_black` as representing the sentence "The cat is black" - `cat_has_four_legs` as representing the sentence "The cat has four legs" - We can use logical operators to combine Boolean statements - `cat_is_black and cat_has_four_legs` represents the sentence "The cat is black and has four legs" ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} cat_is_black = True cat_has_four_legs = False print(cat_is_black and cat_has_four_legs) ``` ## Booleans <img src="./images/truth_table.png" alt="Welcome page for the unit on Blackboard" style="display:block;margin-left:auto;margin-right:auto;width:60%"/> ## Collections - We've now seen some simple examples of datatypes: - Numeric, strings, and booleans. - Data types don’t always have to be this simple! - Sometimes we need data types that can hold more complex information. - We'll see more examples throughout the course but for now we'll provide just one - A collection is any in-built data type that can group multiple objects together. - The most common collection is a list: an ordered group of items. - Lists let you store, organize, and work with many values at once. ```{pyodide} #| caption: "▶ Ctrl/Cmd+Enter | ⇥ Ctrl/Cmd+] | ⇤ Ctrl/Cmd+[" #| slideshow: {slide_type: fragment} # A list of numbers scores = [10, 15, 20] # A List of strings shopping = ["milk", "bread", "eggs", "cheese"] # A mixed list (different data types together) mixed = [42, "hello"] ``` ## Practical - We have students from a range of courses and backgrounds in this class - Chemistry - Physics - Data Science - Some people in the room will have less experience than others - For the first few weeks, we shall try to account for the differences in ability where possible ## Practical - We now move over to Python - Please open [week_01_home.ipynb](./week_01_home.qmd) - For the rest of today, you must work through a Python notebook - You have a choice of one of three options - [Option 1: Beginner - Basic Data Types](week_01_intro_to_data_types_beginner.qmd) - [Option 2: Intermediate - Collections](week_01_intro_to_data_types_intermediate.qmd) - [Option 3: Advanced - Copying and References](week_01_intro_to_data_types_advanced.qmd)