Master Python programming by solving scientific projects

Master Python math operators across ints and floats, including addition, subtraction, multiplication, division, powers, and order of operations. Explore string overloading, concatenation, repetition, and floating point versus real arithmetic.

Learn python printing and inputting by mixing numbers and strings, converting with str and float, and computing the length of side C via the Pythagorean theorem.

Learn how to create and manipulate Python lists, including integers, strings, and nested lists; use in for membership, + for concatenation, * for repetition, and methods like append and sort.

Master Python tuples and lists: define tuples with parentheses, note their immutability, and use indexing and count; convert a tuple to a list to modify elements.

Master booleans, true and false, and Python comparisons, including equality tests with double equals. Convert numbers and strings to booleans with bool(), and practice a Pythagorean triplets exercise.

Learn how to use Python dictionaries to store key-value pairs with curly brackets, access values by keys, and inspect keys, values, and items while understanding membership and case sensitivity.

Learn to index Python data structures by accessing elements in lists, ranges, and strings using zero-based and negative indexing. Explore multi-level indexing, slicing, and preparing for NumPy arrays.

Explore Python slicing to access multiple list elements, learn start, stop, and step, including negative indices and reversing with slicing, and apply an exercise extracting a hidden message.

Explore numpy in Python by importing the module, using the np alias, and applying array operations such as linspace, min, and in-place sorting.

Use pandas data frames to store data and inspect with head. Build a dictionary-based data frame with integers 0 to 10, squares, and log values, and handle log of zero.

Learn to get help on functions in Python using docstrings, help, and online searches. See examples with sum, NumPy linspace, and inspect for learning function behavior.

Create your own functions in Python from scratch, including def and return, explore lambda, and compare a factorial function with math.factorial.

Discover how local variables inside a function differ from global ones in Python. See how scope and name binding decide which variable value is used, with practical examples.

Explore object oriented programming by using classes and objects, including constructors and methods, to manage data in an experiment example, and apply it to a board game tracking exercise.

Master Python for loops using range and indexing, contrast soft coding with hard coding, apply loops with if statements to classify numbers as even or odd, including a Fibonacci exercise.

Use the continue statement to skip loop iterations in for and while loops, such as omitting even numbers or vowels, and explore solutions with lower() or uppercase handling.

Master while loops in Python, covering boolean conditions, toggles, and break logic, contrast with for loops, and note when to use while loops in machine learning and optimization tasks.

Master python programming by solving scientific projects. Learn variable initialization with lists and numpy, using zeros or full, and compare list append, numpy append, and numpy initialization to boost speed.

Develop robust Python practices by enforcing numeric inputs with isinstance checks, raising clear errors, converting numeric strings to float when possible, and using try-except judiciously.

learn how to use zip to pair elements from two lists in a for loop, stop at the shortest input, and build a dictionary from names and values.

Explore numpy broadcasting to add a vector to each matrix row, compare with for loops, and reshape, tile, and dot to broadcast across columns.

Learn to lay out multiple plots in one figure using subplot and subplots with various geometries like 1x2, 2x1, and 3x3, and to populate them via loops and axis objects.

Master Python data visualization by building nicer plots with NumPy and Matplotlib, adding legends, axis labels, dynamic x/y limits, square aspect ratios, grid, and custom RGB colors for multiple lines.

Learn to create advanced visualizations with Seaborn, integrate with pandas, and generate joint plots, density estimates, custom color palettes, and regression plots for data analysis.

Export python graphics with matplotlib and pyplot in pixel and vector formats, using numpy to generate data and saving high- and low-resolution PNG, PDF, and SVG outputs.

Translate the Sierpinski pseudocode from the pink triangle exercise into Python, building the fractal design from scratch without peeking at prior code.

Download course materials to access project folders, including helper and solution notebooks, and learn to run Python projects locally with Anaconda or on Google Colab by uploading to Google Drive.

Master Python programming by solving scientific projects using five strategies, from real-time code along to from-scratch coding, with a pause-and-code workflow and learning tips.