Stock Market Data Analysis & Visualization w/ Python & More

Meet Mammoth Interactive's online school, its founding in 2012, and its mission to teach in-demand tech skills that help learners land jobs and build results-driven projects.

Learn online effectively with industry-leading instructors, affordable, flexible access, and hands-on, project-based learning that builds a portfolio for stock market data analysis and visualization with Python.

Begin the stock market data analysis and visualization course by learning Python basics for complete beginners, covering variables, data types, conditionals, loops, functions, and classes in Google Colab.

Discover how Python variables act as placeholders that store data, explore booleans, integers, floats, and strings, and learn dynamic typing and type conversion with practical code examples.

Learn type conversion in Python by converting between int, string, bool, and float with hands-on examples, including how original variables stay unchanged and when conversions crash.

Explore Python operators from arithmetic and assignment to comparison and logical operators, with practical examples for beginners using numbers, strings, and booleans, including exponentiation, modulus, and floor division.

Master Python lists by creating and accessing items, modifying elements, and using common operations like append, insert, pop, remove, and clear. Explore multidimensional lists with rows and columns.

Explore Python ranges, using start, end, and step to generate numbers, reverse ranges for loops, and understand end is not included, plus in and not in checks.

Master conditionals that drive control flow by using if, elif, and else. Test complex states with boolean values, nested structures, and and/or logic to decide code paths.

Master Python control flow with if statements, elif, else, and the ternary operator using a game-inspired health and movement example. Explore consecutive and nested ifs and multi-test logic with and/or.

Explore Python loops, including while and for loops, with break and continue statements. Learn how loops automate code, iterate over ranges and lists, and prevent infinite execution.

Explore Python functions as self-contained blocks of code you can call from anywhere, with parameters and return values, mastering global and local scope, and the implementation and calling of functions.

Master Python functions by adding parameters and return values, using default parameters, and enforcing bounds with start and end positions to produce safe, flexible code.

Define classes and create objects to model items with state and behavior, using fields and methods. Explore inheritance and static members to share code and access level features without instantiation.

Explore Python inheritance by subclassing a game character into a player character, using super() to initialize and override take damage and check is dead, with lives and max health.

Learn how static variables and static methods belong to the class rather than instances in Python, demonstrated with a game character and shared constants.

Fetch stock data in Python using Google Colab, pandas data reader, and Yahoo Finance; define start and end dates, loop through tickers, and build pandas data frames for analysis.

Visualize stock data features by plotting columns with matplotlib for Alphabet/goog, using date axes and legends. Create moving average plots for 5, 20, and 50 days and explore daily returns.

Calculate daily return using percent change on adjusted close, plot the daily return with legends and styles, and compare returns across multiple stocks over one year.

Create a dataframe of adjusted closing prices from Yahoo Finance, compute daily returns, and compare multiple stocks using seaborn visualizations such as joint plots and pair plots.

Compare closing prices across stocks, build a heat map and power grid, and compute correlations from returns to assess cross-stock risk.

Visualize stock risk and return by plotting standard deviation against mean returns, annotate points for each stock, and explore risk versus expected returns with a Python-based scatter plot.

Apply Monte Carlo analysis to estimate stock risk by simulating price paths using a starting price, drift, volatility, and random shocks. Visualize these runs to see price distribution.

Visualize price distribution with a Python Monte Carlo approach, using histograms to interpret variability and display final price, mean, variance, and value at risk with a 99% interval.