Data Analyst Boot camp 2026: Get Ready to Be a Data Analyst.

Learn basic string operations in Python, including concatenation with the plus operator, measuring length with the length function, and indexing characters starting at zero.

Explore essential Python string methods to manipulate text, including lower, upper, and strip, and see how these tools power effective text processing.

Learn to manipulate Python strings using split and replace to transform text into a list of substrings. Harness the full power of text processing in Python.

Master string formatting in Python by using the format method with placeholders and f-strings, enabling dynamic strings and concise interpolation for variables like name and age.

Explore Python lists and core sequence types, mastering indexing, slicing, and operations like append, insert, delete, pop, sort, and reverse. Practice iteration and converting lists to tuples or sets.

Explore Python tuples, an immutable, fast data structure for storing multiple items. Learn indexing, slicing, iteration, conversion to lists or sets, and using tuples as dictionary keys for heterogeneous data.

Explore Python sets, including creation with curly braces or set(), and perform add, remove, union, intersection, difference, and symmetric difference while noting duplicates are removed.

Explore Python dictionaries, a versatile key-value data structure, learning to access and modify values, add or remove items, and use methods like get, items, keys, values, and update.

Learn Python control structures using if, elif, and else to run code blocks by conditions, with examples on voting eligibility, age categories, discounts, and logical operators.

Learn how Python for loops repeat actions over sequences, with real examples using a basket of fruits, range for positions, enumerate for index and value, and zip for pairing lists.

Master advanced for loop techniques in Python, including unpacking tuples and dictionaries, range customization, break and continue control, safe list modification, filtering, enumeration, and parallel iteration with zip.

Explore Python looping concepts, including for loops, while loops, recursion, and list and dictionary comprehensions; learn break, else, and do while patterns to avoid infinite loops.

Explore advanced while loops in Python, compare them with for loops over lists and dictionaries, and apply break, continue, counting, event-driven, and input-driven patterns with best practices.

Learn best practices for Python while loops, including initializing loop variables before the loop, using descriptive names, preventing infinite loops with breaks, and differences versus for loops.

Explore how Python functions work as reusable machines: define with def, use parameters, and return values to perform input-driven tasks and produce outputs.

Explore Python function arguments, including positional and keyword arguments, and understand default parameters. Learn how these concepts add flexibility and control to functions.

Explore recursion in Python by learning how a function calls itself, using base and recursive cases to break problems into simpler subproblems, with countdown and factorial examples.

Explore Python function decorators and higher order functions to enhance function behavior and code flexibility. Learn syntax examples and practical uses by applying decorators and returning functions.

Learn how to define lambda functions in Python as concise anonymous one-line functions and use them with map, filter, and reduce.

Explore function caching and memoization to boost performance by storing and reusing results, illustrated with Python examples like Fibonacci and factorial using decorators.

Import python libraries and use modules to reuse code, with examples from Matplotlib and random. Open, read, and write files, including printing lines and binary operations.

Define a class with camel-case naming, distinguish class attributes from instance attributes, and implement a method that takes self to create objects and return greetings.

Explore object oriented programming by implementing constructors, creating a user class with name and age, and computing average ages and rectangle areas from instantiated objects.

Learn to implement dunder methods like __init__, __str__, and __len__ to customize object printing and length for a user class with name and age.

Explore inheritance in Python by building a superclass and subclasses to reuse and extend behavior, illustrated with a user data class and a phone number subclass.

Explains encapsulation as a core object-oriented property that hides internal parts and restricts access, using getter and setter methods to interact with object data.

Discover how overriding works when a subclass redefines a superclass method and uses super to call the superclass method.

Explore decorators, including class methods and static methods, and their role in accessing class state versus the instance self. See how a fruit example demonstrates readable code with these methods.

Explore how built-in decorators, especially the property decorator, help manage derived attributes in a Python class without requiring downstream code changes.

Use this project walkthrough to build the color choices game, covering design questions, player and computer input, win conditions, and replay options.

Explore helpful notes and step-by-step logic for the color the choices game, including creating player and computer choices, input handling, win/tie conditions, and a project walkthrough.

Develop and test the colors choices game code, mapping red, yellow, green, and blue to numbers, and manage player and computer scores with turn-based logic and a replay option.

Explore the Hangman game project in Bison, covering letter guessing, two modules (hangmen guessing and hangmen life), and progressive shapes as you learn gameplay and coding ideas.

Explore helpful notes for building a hangman game in Python, including selecting a random word, tracking guesses, managing six tries, and printing game progress.

This lecture guides coding a hangman game in a Python notebook, importing random and a guess list, selecting a random word with random.choice, and initializing six tries with underscores.

Complete the hangman game project by building underscore placeholders and selecting a random word. Process letter guesses and update the display until victory or loss.

Build a game project by implementing a game board class, item setters, decorators, and game flow with a game manager to launch and test the game.

This lecture provides a guided approach to building a game project, detailing the game board setup, item creation, functions to control flow, player management, and launching the game.

Build and manage a 3x3 game board, handle player input for positions 1–9, and implement win, draw, and end conditions within a structured game loop.

Learn how to build a Twitter chat bot and connect your Python code to the Twitter API by creating a developer account, applying for access, and configuring educational use.

Learn to set up a Twitter developer app, install Tweepy, and run code to fetch timeline tweets, user details, and friends' screen names from the Twitter API.

Enhance the data analyst boot camp 2025 course by adding Twitter chat board features, enabling follow actions for all followers, managing read/write API tokens, and handling rate limits with sleep.

Explore the Python web framework Flask, its small core and easy extension, and build a hello world web app from installation to running on a local server.

Log into Facebook developer, create a Messenger app named Kenotic Chatbot, link it to a page Kenotic Learning Academy, and enable Messenger integration; download Ngrok to expose localhost.

Install and import python libraries for a facebook chat app, including by messenger, requests, and flask, and set up a token-based bot with a /webhook.

Hook and run the Facebook chat bot app using Ngrok by building a webhook that handles get and post requests, verifies the token, processes messages and sender IDs.

Explore NumPy basics, its role in turning data into numeric arrays for data preparation and machine learning, and how vectorization and broadcasting speed Python code by avoiding loops.

Explore NumPy basics with ndarrays, shapes, and sizes, including 1d, 2d, and 3d arrays, and learn to reshape arrays and convert them to pandas dataframes.

Explore creating numpy ndarrays with zeros and ones, use arange to generate sequences, and create random arrays with random and seed for reproducible results.

Learn numpy.unique and array slicing on a multi-dimensional rand array, use shape to identify dimensions, and practice indexing to retrieve first, second, and last elements.

Master numpy calculations and operators by manipulating arrays, using ones and zeros, performing addition, multiplication, division, power, modulo, and applying exponential and log functions, with broadcasting and shape considerations.

Learn NumPy aggregation functions for arrays, including sum, mean, max, min, standard deviation, variance, and square root. Explore practical examples to apply these functions and compare results across arrays.

Master numpy reshape and transpose to align array shapes for multiplication, avoid value errors from broadcasting, and explore shape aware array operations.

Learn to compare numpy arrays using greater than, less than, equal, and not equal operators, with shape-aware, element-wise results. Understand dot products with np.dot and array shapes.

Explore using numpy to convert images into number arrays for processing, including reading images with matplotlib, inspecting array size and shape, and understanding rgb pixel values.