Data Science: Python for Data Analysis Full Bootcamp

Master the python string data type, including creation, immutability, and quoting with single or double quotes and escaping. Explore indexing, slicing, concatenation, and functions like upper, lower, len, and strip.

Explore Python numbers as integers, floats, and complex, and learn conversions with int(), float(), and complex(). Master the order of operations, including division, integer division, modulo, exponentiation, parentheses, and f-strings.

Structure data using tuples in Python by creating immutable collections with parentheses or tuple(), access items by zero-based or negative indices, and use len, in, and del for basic operations.

Learn to structure data with sets in Python, noting they are unordered, immutable, and contain no duplicates. Create sets with curly braces or set() function, print and check type.

Learn how to use Python's comparison operators to compare numbers and strings, producing boolean values true or false, and inspect results with the type function.

Master Python's logical operators and, or, not to combine conditions and yield boolean results, with practical examples and truth-value outcomes.

learn to create and call user defined functions in python with def, parameters, return values, and indentation; explore scope and the global keyword through an addition example.

Learn how lambda functions in Python create anonymous one-expression helpers with multiple arguments, how to assign them to variables, and how they return function objects for higher-order use.

Learn how the for loop and break statement control Python iteration, and how a for-else pattern can print a message after completing the loop, illustrated by iterating over a string.

Explore inheritance in Python by building a base car class, and a derived Currawong class, showing how child classes inherit methods, properties, and initialization with super.

Explore built-in Python modules from the standard library, using import and from import syntax, and generate random numbers with the random module, including random integers and the choice function.

Explore Python's math module, import it or specific functions like sine, cosine, and log, and use constants e and pi and perform radians and degrees conversions.

Explore common Python errors such as syntax errors, index errors, module not found, type errors, name errors, and zero division errors, and learn how these exceptions guide debugging.

Learn how to use Python's try and except blocks to handle errors, such as zero division, with multiple except clauses and messages, and observe how execution continues after an exception.

Move files between directories using python's shutil.move after importing the module, and verify results with os.listdir. The lesson demonstrates creating files and folders and moving them back and forth.

Install center trash module with pip in Jupiter notebook, import it, and use its send_to_trash function to move a file to trash bin, restore it from trash if needed.

Explore Nampara, the numerical Python library, to create arrays and matrices in Python. Use it in Jupyter with MPLX alias, and leverage zeros, ones, and random for math operations.

Master Python indexing for arrays in NumPy: learn zero-based indexing, access the first item with 0, the second with 1, and the last with -1.

Explore aggregation with numpy: use max, min, sum, mean, and standard deviation on a one-dimensional array, calling built-in functions with simple syntax to compute values.

Learn to access elements in a NumPy multidimensional array using row and column indices with zero-based numbering, including single-item access and slicing to extract ranges.

Explore NumPy level 9 by creating multidimensional arrays with ones, zeros, and random values, and learn to shape and view three-dimensional arrays in Python data analysis.

Explore pandas data structures by building a one-dimensional series with a custom index and constructing a two-dimensional data frame from lists, then read data files like CSV and Excel.

Learn to read CSV files with the pandas library using read_csv and preview data with head; explore reading Excel files and other formats.

Use info to inspect column types and structure, and describe for descriptive statistics on datasets. Apply amax and amin to find maxima and minima, as shown with cars and price.

Import the pandas library and read excel data with read_excel to extract information from a single or multiple sheets using sheet_name, then print results for data analysis in Python.

Create interactive line plots using matplotlib by adding a title, x label, and y label in the interactive window, then plot X and Y lists with the built-in plot function.

Explore Matplotlib data visualization level 4 by plotting line graphs with colored markers and saving figures to files, then create bar plots from range data for new films and awards.

Create a Matplotlib bar plot using the bar method, set the title and y-label 'number of hours', label the x-axis with full film names at each bar center, and show.

learn to create pie charts in python using matplotlib's pie function, including preparing data, labels, and displaying the chart, with a simple example comparing values 37, 27, 27, and 17.