Python Data Analysis Bootcamp with Pandas and NLTK

Introduce Mammoth Interactive, founded in 2012 by John Baraa, an online school that offers in-demand tech skills and helps students land their dream jobs.

Learn online effectively through project-based learning, finishing what you start, and building a portfolio with hands-on projects guided by active industry leaders, with flexible pacing and practical, in-demand skills.

Master python basics through browser-based coding in Google Colab. Explore variables, data types, conditionals, loops, functions, and classes with practical examples for beginners.

Explore Python variables as data placeholders, learn booleans, integers, floats, and strings, and understand dynamic typing and basic type conversions with hands-on examples.

Explore Python type conversion between integers, floats, strings, and booleans. Learn that the original value stays unchanged and that 0 is false while nonzero is true.

Explore arithmetic, assignment, comparison, and logical operators in Python, with examples of string concatenation, modulus, floor division, and exponentiation, plus ternary, identity, and membership operators.

Explore Python collections—lists, tuples, dictionaries, and ranges—and learn how to store multiple values with indices and keys. Understand mutability and when to choose each type.

Master Python lists and multidimensional lists by creating, indexing, and modifying items; measure length, use max and min, apply append, insert, remove, pop, and clear, with row and column access.

Explore tuples in python: objects are immutable, hold multiple values like item name and quantity in a tuple; access by index; use count, index, and length to inspect contents.

Explore Python dictionaries by creating, accessing, and modifying key-value pairs, using get for missing keys, and performing insertion, removal, and length, max, and min operations.

Explore Python ranges, covering start and end, optional step, reversing ranges, and in/not in checks, with practical examples using range objects, lists, and for loops.

Learn conditionals and control flow in Python, including if statements, elif, else, and complex tests using and/or, to decide which code runs based on variable states.

Explore Python control flow with if, elif, else, and the ternary operator using game-style examples like moving left or right and health and lives logic.

This lecture introduces loops in Python, compares while and for loops, and explains break and continue, with game loop examples illustrating how iterative code runs and ends.

Learn how to define and call functions in Python, using parameters and return values to create reusable, self-contained code blocks with global and local scope concepts explained.

Expands Python functions by introducing parameters and return values, demonstrates default parameters, passing arguments, and bounds-checked movement within a start and end range.

Discover how objects and classes model state and behavior, use initialization and instantiation, and apply inheritance and static members through Python examples like a game character.

Explore inheritance by subclassing a game character into a player character in Python, adding max health and lives, overriding take damage and is dead, and using super.

Learn how static members in Python belong to a class, not instances, enabling access without creating an instance, via static variables and static methods to share constants.

Explore Python language basics, from variables and operators to collections, conditionals, loops, functions, and classes, then practice by building simple programs and exploring libraries like pandas that support data analysis.

Upload a csv file to Google Colab, read it with pandas, then convert the quotes column to a Python list using StringIO and UTF-8 decoding.

Tokenize text data by sentences and words with nltk, then build a pandas data frame, clean tokens via stop words removal, part of speech tagging, and stemming.

Learn to clean token data, build a dataframe with tokens, lemmatization, and part of speech, and compute the most popular lemmatized words through grouping, counting, and sorting.

Group a data frame to extract counts by part of speech. Create separate data frames for nouns, adjectives, verbs, and adverbs, then prepare to visualize the results.

Visualize word frequency by building a descending plot from flattened tokens using nltk's frequency distribution and stop-word removal to reveal meaningful terms.