Python Bootcamp

Discover how Python blends object-oriented and procedural programming as a high-level language, with simple syntax, modularity through modules and packages, and hands-on Jupyter notebook basics.

Celebrate this milestone in Python bootcamp as you join the top 50% of learners, stay motivated, and use its Q&A, AI assistant, and resources to complete the course.

Discover how Python variables store values, enable reuse, and require descriptive names. Learn rules: start with a letter or underscore, are case sensitive, and cannot use Python keywords.

Explore Python data types, including integers, floats, strings, lists, tuples, dictionaries, sets, booleans, bytes, byte arrays, and none type, and master arithmetic operations, type checking, and operator precedence.

Explore Python strings, the text type, created with single, double, or triple quotes, and learn slicing with start and end indices, negative indexing, and concatenation using plus.

Learn to apply built-in Python string methods on string variables, including upper, lower, islower, isupper, isdigit, startswith, endswith, count, capitalize, find, replace, and len, plus escape sequences.

Learn how Python lists, a mutable sequence data type, store any type and support indexing, slicing, and common methods such as append, extend, insert, pop, remove, sort, and clear.

Explore Python tuples as immutable sequence data types and how they differ from lists. Create empty and single-element tuples, index elements, and use count and index methods.

Explore Python dictionaries as a mapping type of key-value pairs and learn to access, update, and inspect items, keys, and values, including strings, numbers, lists, and tuples.

Learn how Python sets store non-repetitive, unordered, immutable elements and perform union, intersection, and difference, with methods like add, remove, and clear.

Explore Python conditional expressions using if, elif, and else to make decisions based on conditions. Learn how to compare ages, handle multiple branches, and understand operator precedence.

Explore Python operators including arithmetic, assignment, relational, and logical operators, and master precedence, associativity, and indentation. Build and evaluate conditional expressions with if, elif, else, nesting, and the pass statement.

Master Python loops, including for and while loops, using lists, strings, and the range function. Learn to print items, break and continue, and build nested loops for two-digit combinations.

Explore while loops in Python, contrast with for loops, and learn incrementing and decrementing, break and continue, plus practical examples like summing even numbers and reversing a string.

Master Python functions by learning how to define with def, call and reuse built-in and user-defined functions, pass arguments, return values, set default parameters, and handle variable arguments with *args.

Discover recursion in Python by building self-calling functions that accept various argument types, from lists to strings, and explore factorial and Fibonacci examples.

Explore lambda functions in Python, defined as small anonymous single-expression functions, and learn to apply them with map for lists, including squaring numbers and Celsius-to-Fahrenheit conversion.

Master Python file input and output by using open, read, write, and append modes for text and binary files, and learn the with statement for automatic closing.

Explore how machine learning enables computers to learn from data, detect patterns, and predict outcomes using supervised and unsupervised methods such as regression, classification, forecasting, and clustering.

Explore how NumPy provides multidimensional arrays and fast numerical routines for scientific computing in Python. See why an ndarray uses contiguous memory for faster operations than Python lists.

Learn to index and select data in NumPy arrays of 1D, 2D, and 3D shapes using row and column indices, slicing with colons and steps, and handling out-of-range errors.

Explore NumPy arrays, learn data types (ints, bools, unsigned ints, floats, complex floats, timedelta, datetime, strings, and Unicode strings), inspect shape and reshape, and join or stack arrays with concatenate, axis, and vstack.

Create numpy arrays of various shapes and values using zeros, ones, and full; generate identity (np.eye) and diagonal matrices; produce random integers and convert Python lists to arrays.

Master element-wise numpy array operations, including add, subtract, multiply, divide, and sqrt or cbrt; compute sum, min, max, mean, and median; and use copy and set operations (intersection, union, difference).

Explore the Pandas library in Python for fast dataframe manipulation, integrated indexing, and handling missing data across CSV, Excel, MySQL, and HDF5 formats.

Explore pandas series and data frames, learn to create series with index labels, and build data frames from dictionaries and numpy arrays, including loading CSV and Excel files.

Master core data analysis methods in pandas for python, including head, tail, info, describe, sort_values, groupby, merge, join, copy, concat, and handling missing values.

Learn to detect and treat missing values in data using pandas, with isnull, value_counts, and sum to identify nulls; apply dropna and fillna strategies for bedrooms, parking, and furnishing status.

Learn to transform and manipulate pandas data frames by adding, dropping, renaming, and indexing columns with iloc and loc, handling duplicates and examining correlations for EDA.