Python Data Course: Python for Data Analysis & Visualization

Learn how Python variables store data, use the assignment operator, and name variables, then apply arithmetic, comparison, and logical operators with numbers, floats, and strings.

Explore Python string functions for data analysis, including length, indexing, slicing, counting, finding, joining, replacing, stripping, and transforming text with lower, upper, capitalize, swapcase, and title.

Learn how to create and modify dictionaries in Python, access and update keys and values, and use sets for unique elements, while understanding mutable and immutable data structures.

Master python control flow with if statements and loops, using for and while, range, break, and continue, plus enumerate, zip, and unzip for color lists.

Explore python error handling in data analysis workflows by using try and except blocks to manage errors, print messages, handle name errors, and control program flow when something goes wrong.

Explore Python functions, including definitions, parameters, and returns, and distinguish local and global variables while applying examples like rectangle area and even checks.

Learn how to create and import Python modules, define and use functions such as even, and read from and write to files with open, process lines, and close.

Learn to define simple classes in Python, differentiate class attributes from instance attributes, and implement methods using self for object interactions.

Explore object oriented programming by defining classes, initializing each instance with name and age, and calculating rectangle areas and their average across instances.

Learn to implement Python dunder methods by using a constructor to create a user class, print objects with a custom representation, and define __len__ to measure an object's length.

Learn how inheritance lets you create a base class and extend it with subclasses, override methods, and reuse attributes such as name and age in Python data analysis and visualization.

Encapsulate an object's internal parts to provide a simple, restricted interface. See how getters and setters control access and reveal data safely.

Learn how multiple inheritance works in Python by combining attributes from two parent classes, as citrus fruit inherits from fruit and its name, season, and color.

Explore how a subclass overrides a method from its superclass and how to call the superclass method, demonstrating method overriding in Python classes.

Explore advanced object-oriented programming with class methods and static methods, learn how decorators differentiate instance, class, and static access, and see practical fruit class examples.

Explore how to use built-in decorators in Python to control attribute access in a fruit class example, using @property and setter to preserve compatibility.

Explore how to build a color-choices game in project 1, including user and computer color selections, score tracking, win conditions, and replay options.

Explore project 1 notes for building a choices game with players and computer inputs, random outcomes, scoring, and replay options, guided by step-by-step Python logic and project walkthrough.

This project 1 solution teaches a python color-choices game with a board, using random for computer moves, importing modules, and tracking player and computer scores.

Build and analyze a hangman game in Python, constructing word lists and drawing stage shapes while showcasing gameplay flow, coding steps, and resources to guide development.

Import random and Hangmen Gissing module and Hangmen Life Module to select a random word, set six tries, print the word for testing, and track guesses in a result list.

Begin a hangman game project in a Python notebook by importing random, selecting a random word from the guest list, and initializing underscores with game_over false and six tries.

Complete the hangman game project by integrating the random module to reveal guessed letters in a result list of underscores, iterating with loops until game over, and display hangman shapes.

Walk through building a Python game project, creating a game board class, item management, decorators, and a four-function game flow with players, a game manager, and a launcher.

Explore the project on X.Org game classes and building a game board. Learn to create items, check board full, and manage players with a launcher.

Develop a Python project solution by building a game with a board and nine positions. Include players, win conditions, input handling, and restart or draw options.

Collect data from multiple sources, clean and organize it, correct timestamps and formats, handle missing values, and perform exploratory data analysis to draw actionable conclusions for decision makers.

NumPy forms the scientific foundation for modules like pandas and cyclone, turning data into numbers to prepare data for machine learning modeling, with fast performance and broadcasting that avoids Python loops.

Explore numpy arrays, learn to create 1d, 2d, and 3d arrays, inspect their shape and size, and reshape them for conversion to pandas data frames.

Learn to create NumPy nd arrays with zeros, ones, and arange, generate random arrays with seed control, and inspect shape and size for reliable, repeatable data analysis.

Learn numpy.unique and array slicing with a 3x4x5x6 array, practice indexing to access specific rows and elements, and extract the last element of every matrix.

Explore numpy calculations and operators to perform array arithmetic, handle elementwise operations, and manage shape compatibility with addition, subtraction, multiplication, division, floor division, power, modulo, exponential, and log.

Explore NumPy aggregations on arrays, computing sum, mean, min, max, and the standard deviation and variance, including the square root of the variance.

Learn how to reshape and transpose arrays in numpy to align shapes for multiplication, avoiding broadcasting errors and enabling seamless data operations.

Explore numpy comparison operators—greater than, less than, equal, and not equal—on arrays with the same shape, plus dot product and how to fix shape errors.

Learn to load images with numpy in Python data analysis, convert images to arrays, inspect size, shape, and RGB values, and perform basic image processing in a notebook.

Install Jupyter Lab and Pandas to set up a data analysis environment, launch a Python 3 notebook on the lab server, and run your first code cell.

Download the PostgreSQL installer for Windows, run the graphical installer, accept defaults for installation and data directories, set and store the server password, and finish without launching Stack Builder.

Create a new database through the server interface, rename it Airlines, select template zero, save, then inspect schemas to find public and tables, noting there are no tables yet.

Practice restoring the airlines database via the right-click restore option in the database UI, selecting the SQL file, executing the restore, and verifying the table contents after completion.

Execute a targeted SQL query on the PostgreSQL airlines database, export results to CSV, clean city names, and load the data with Python pandas for quick inspection.

Learn to load and query a PostgreSQL database from Python using psycopg2, connect to the database, run a select query, and fetch subsets with fetchmany and fetchall.

Learn to query an airlines database using Python and pandas, execute SQL queries, fetch results, and create data frames for aircraft, tickets, and boarding data with a natural join.

Learn to work with a pandas DataFrame by selecting columns, viewing rows with tail, using iloc, and exploring rank and info, plus the docs via a web browser.

Visualize data with pandas by loading a CSV into a dataframe. Compute mean sales and render bar charts showing August, then July and June.

Use pandas to load a csv, inspect dtypes, and apply describe to reveal descriptive statistics for numeric columns, focusing on sales to report mean, std, min, max, quartiles, and median.

Identify and avoid sampling error by using larger samples from the population, reading the data, and visualizing monthly ticket sales to show July leads, then August and June.

Learn to scrape data from websites using Python with requests and BeautifulSoup. Extract text and images from HTML and clean results for data analysis while respecting robots.txt and privacy.

Install lxml and pandas to scrape a webpage's html table, read it into a data frame, access columns like popularity and unique visitors per month, and describe statistics.

Learn to visualize scraped data using Python data frame and charts, creating a pie chart of websites with their unique visitors per month and explode effects to show popularity.

Learn to save scraped data to a database with Python, pandas, and SQLAlchemy by creating a database, cleaning columns, loading data into a dataframe, and writing a table.