DS Scientific Programming Lab
  • Scientific Programming for DS
  • General Info
    • Timetable and lecture rooms
    • Equipment
    • Acknowledgements
  • Module 1, Practical 1
    • Setting up the environment
      • Linux on windows
      • A dual boot system
    • Our toolbox
    • Installing Python3 in Linux
    • Installing Python3 in Windows/Mac
      • OPTION 1:
      • OPTION 2 (easier):
    • The console
    • Visual Studio Code
    • The debugger
    • A quick Jupyter primer (just for your information, skip if not interested)
      • Installation
    • Let’s try together
    • Exercises
  • Module 1, Practical 2
    • Modules
    • Objects
    • Variables
    • Numeric types
      • Integers
      • Booleans
      • Real numbers
    • Strings
      • Methods for the str object
    • Exercises
  • Module 1, Practical 3
    • Lists
    • Operators for lists
    • Methods of the class list
    • From strings to lists (split method)
    • from list to strings (join method)
    • Exercises (LIST)
    • Tuples
    • Introduction to sets
    • Exercises
    • Exercises (difficult)
  • Module 1, Practical 4
    • Execution flow
    • Conditionals
      • The basic if - else statement
      • The if - elif - else statement
    • Loops
      • For loop
      • Looping over a range
      • Nested for loops
      • While loops
    • Exercises
    • Exercises (difficult)
  • Module 1, Practical 5
    • More on loops
      • Ternary operator
      • Break and continue
        • Continue statement
        • Break statement
      • List comprehension
    • Dictionaries
      • Functions working on dictionaries
      • Dictionary methods
    • Exercises
    • Exercises difficult
  • Module 1, Practical 6
    • Functions
    • Namespace and variable scope
    • Argument passing
      • Positional arguments
      • Passing arguments by keyword
      • Specifying default values
    • File input and output
      • Opening a file
      • Closing a file
      • Reading from a file
      • Writing to a file
      • String formatting with format
    • Exercises
    • Long exercise: Word Frequency Analysis
  • Module 1, Practical 7
    • Gentle reminder on functions
    • Getting input from the command line
    • Argparse
    • Libraries installation for the next practicals
    • Exercises
    • 1st ML model
      • Machine Learning
      • What is k-Nearest Neighbors (k-NN)?
      • Main Idea Behind k-NN
      • For instance:
      • todo
  • Module 1, Practical 8
    • Libraries installation
    • Pandas
    • Series
      • How to define and access a Series
      • Operator broadcasting
      • Filtering
      • Missing data
      • Computing stats
    • Plotting data
    • Pandas DataFrames
      • Define a DataFrame
      • Loading data from external files
      • Extract values by row and column
      • Broadcasting, filtering and computing stats
      • Merging DataFrames
      • Grouping
    • Exercises
    • Exercises (2) DS
  • Module 1, Practical 9
    • Numpy
    • Numpy ndarray
    • Random arrays
    • Numpy to and from pandas
    • Reshaping
    • Iterating over arrays and Indexing
    • Broadcasting and arithmetic functions
    • Filtering
    • Linear algebra
    • Matplotlib
    • Exercises
  • Module 1, Practical 10
    • Part 1
  • Module 1, Exams
    • Exams and Simulation
DS Scientific Programming Lab
  • Scientific Programming for DS
  • General Info
    • Timetable and lecture rooms
    • Equipment
    • Acknowledgements
  • Module 1, Practical 1
    • Setting up the environment
      • Linux on windows
      • A dual boot system
    • Our toolbox
    • Installing Python3 in Linux
    • Installing Python3 in Windows/Mac
      • OPTION 1:
      • OPTION 2 (easier):
    • The console
    • Visual Studio Code
    • The debugger
    • A quick Jupyter primer (just for your information, skip if not interested)
      • Installation
    • Let’s try together
    • Exercises
  • Module 1, Practical 2
    • Modules
    • Objects
    • Variables
    • Numeric types
      • Integers
      • Booleans
      • Real numbers
    • Strings
      • Methods for the str object
    • Exercises
  • Module 1, Practical 3
    • Lists
    • Operators for lists
    • Methods of the class list
    • From strings to lists (split method)
    • from list to strings (join method)
    • Exercises (LIST)
    • Tuples
    • Introduction to sets
    • Exercises
    • Exercises (difficult)
  • Module 1, Practical 4
    • Execution flow
    • Conditionals
      • The basic if - else statement
      • The if - elif - else statement
    • Loops
      • For loop
      • Looping over a range
      • Nested for loops
      • While loops
    • Exercises
    • Exercises (difficult)
  • Module 1, Practical 5
    • More on loops
      • Ternary operator
      • Break and continue
        • Continue statement
        • Break statement
      • List comprehension
    • Dictionaries
      • Functions working on dictionaries
      • Dictionary methods
    • Exercises
    • Exercises difficult
  • Module 1, Practical 6
    • Functions
    • Namespace and variable scope
    • Argument passing
      • Positional arguments
      • Passing arguments by keyword
      • Specifying default values
    • File input and output
      • Opening a file
      • Closing a file
      • Reading from a file
      • Writing to a file
      • String formatting with format
    • Exercises
    • Long exercise: Word Frequency Analysis
  • Module 1, Practical 7
    • Gentle reminder on functions
    • Getting input from the command line
    • Argparse
    • Libraries installation for the next practicals
    • Exercises
    • 1st ML model
      • Machine Learning
      • What is k-Nearest Neighbors (k-NN)?
      • Main Idea Behind k-NN
      • For instance:
      • todo
  • Module 1, Practical 8
    • Libraries installation
    • Pandas
    • Series
      • How to define and access a Series
      • Operator broadcasting
      • Filtering
      • Missing data
      • Computing stats
    • Plotting data
    • Pandas DataFrames
      • Define a DataFrame
      • Loading data from external files
      • Extract values by row and column
      • Broadcasting, filtering and computing stats
      • Merging DataFrames
      • Grouping
    • Exercises
    • Exercises (2) DS
  • Module 1, Practical 9
    • Numpy
    • Numpy ndarray
    • Random arrays
    • Numpy to and from pandas
    • Reshaping
    • Iterating over arrays and Indexing
    • Broadcasting and arithmetic functions
    • Filtering
    • Linear algebra
    • Matplotlib
    • Exercises
  • Module 1, Practical 10
    • Part 1
  • Module 1, Exams
    • Exams and Simulation
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