Data Science: Python for Data Analysis Full Bootcamp

Store values in Python using variables as memory containers, assign values with the equals operator, and print results to reveal data types like int, float, bool, str, complex, and lists.

Describe how to use comments in python to describe code and improve readability, including single-line and multi-line comments, their placement, and how the interpreter ignores them.

Explore how Python uses indentation to define blocks of code, avoid indentation errors, and structure if statements and loops with automatic four-space indentation.

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.

Learn how to read user input with Python's input function and display results with print, including prompts, type conversion to int or float, and simple examples with variables.

Discover how to create and work with lists in Python, mutable and ordered data, square brackets or list() function, index with negative indices, and operations like append, insert, extend, slicing.

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 how to use Python dictionaries as mutable key-value collections, create them with braces or dict(), access values by keys, and update or delete items while inspecting their dict type.

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 how to use conditional statements in Python to make decisions with logical and comparison operators, including if, elif, and else, and to indent blocks correctly.

Explore the while loop in Python and compare it with the for loop, showing how to repeat code with a test expression, increment values, and print results until loop ends.

Master Python for loops to iterate over sequences such as strings, lists, and tuples, using range and print to process data efficiently.

Explore built-in Python functions and how to call them from the Python standard library. Use built-in examples like absolute value, bool, complex, dict, dir, input, print, sum, and zip.

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 to use the break statement in Python to exit a for loop under a specific condition, using a range from zero to four and print statements.

Learn how to use the continue statement in Python to skip the value 3 in a for loop over range(0,7) and proceed to the next iteration.

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.

Develop a password checking app using input, if statements, and print to display results, loop with range for multiple attempts, break when the password matches, and show completion.

Explore Python's object oriented programming by defining a class and creating multiple instances. Learn how to use attributes, methods, and the self parameter, and implement init to initialize object state.

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 concise comprehensions in Python by building lists and sets with for loops, applying conditions and ranges, and performing simple arithmetic.

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 the Python statistics module to compute mean, median, mode, and standard deviation for numerical data. Import the module, call functions on lists, and interpret results.

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.

Explore in-memory file handling in Python by using the io module to create a file-like object, write and read text, seek, and append data for data analysis.

Learn how to import the os module and interact with the operating system to read, list, and manage files and directories, including obtaining the current working directory.

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.

Learn to use python's zipfile module to create and write two text files, compress them into a zip archive, and extract them with extractall.

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.

Create and manipulate one-dimensional numpy arrays using the array and ones functions. Perform element-wise operations like addition and multiplication to explore array math.

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 how to create multidimensional arrays and matrices with numpy, using nested lists and np.array, and generate ones, zeros, or random data in shapes like 3 by 4.

Learn to perform array operations using the Nampara library, including creating 2x2 matrices, adding matrices with plus, using dot for products, and applying max and min functions to find extremes.

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.

Create a one-dimensional array of six items, then reshape it into 2x3 and 3x2 matrices using the reshape function in Python, specifying rows and columns.

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 in Python for data analysis and manipulation, work with series and data frames, import pandas, load various file formats, and use built-in functions.

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.

Learn to use the pandas head function to view the first n rows (default five) and the shape method to inspect rows and columns while reading HTML data.

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.

Learn to import matplotlib and pyplot in Python, alias as mp, and create simple plots from numpy arrays in a Jupyter notebook with an interactive plotting window.

Visualize data with Matplotlib by calling the plot function on a numeric sequence created with the arrange function, switching between an interactive window and inline plotting with Matplotlib inline.

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.

Create and display histograms in Python with Matplotlib, generating a 230-sample normal distribution around 150 with std 15, and visualize frequency distributions with the show function.

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.