Data Science and Machine Learning Bootcamp with Python

Master control flow in Python for data science and machine learning, building robust programs for data-driven insights.

Explore how to define and use functions in Python to build data science and machine learning workflows.

Explore data structures in the context of Python-powered data science and machine learning, building a solid foundation for efficient data handling and algorithm design.

Introduce Python classes and object-oriented concepts within the data science and machine learning bootcamp, building foundational skills for Python programming for data science.

Learn to structure Python projects with modules and packages, use from module import name and import, create __init__.py, and explore absolute and relative imports, dir, and __name__ for script usage.

Master the Python standard library to streamline data science and machine learning workflows, gaining practical tools and patterns covered in this bootcamp module.

Explore the Python package index to understand how Python packages support data science and machine learning workflows.

Explore popular Python packages for data science and machine learning. See how these tools support projects in a Python bootcamp.

Build web applications with Django in part 2, applying Python-based web development techniques within the data science and machine learning bootcamp.

Master machine learning with Python in a data science bootcamp, applying practical techniques and projects to build, evaluate, and optimize models.

Create a dice rolling game in Python to practice problem solving, input validation, and looping, generating two random numbers and displaying them with f-strings.

Learn to build a currency converter and a quiz game in Python, integrating data science concepts and machine learning techniques within a hands-on bootcamp framework.

Build a text-based tic-tac-toe game in Python with a 3x3 list-of-lists board and modular functions for printing, winning checks, and color output (red for X, green for O).

Build two Python projects, pig dice game and cows and bulls, teaching turn-based play, dice rolling, scoring, and guessing with cows and bulls feedback.

Develop a Python password strength checker and generator, using regular expressions and string and random modules to handle lowercase, uppercase, digits, and punctuation with a 0 to 5 scale.

Learn how to retrieve data from a single table, applying practical techniques for querying and extracting information for data science and machine learning projects with Python.

Develop the skills to write complex queries in Python for data science and machine learning workflows.

Discover how views simplify complex SQL queries and act as reusable virtual tables. Learn to create, update through, and secure updatable views, including with check option.

Explore stored procedures within the data science and machine learning bootcamp with Python to understand their role in this course curriculum.

Explore transactions and concurrency in Python-driven data science and machine learning workflows, focusing on safe, efficient data handling and parallel execution.

Explore MySQL data types across string, numeric, date and time, blob, and json categories; compare char and varchar, integers, decimal, boolean, and understand enums, sets, and lookup tables.

Designing databases part 1 in the data science and machine learning bootcamp with Python offers an overview of database design concepts for managing data.

Advance through designing databases part 2 within the data science and machine learning bootcamp with python.

Secure MySQL databases by creating restricted user accounts and granting minimal privileges for applications. Learn to revoke privileges, manage hosts and passwords, and view grants to ensure proper access control.

Learn how data science turns raw data into actionable insights through data collection, cleaning, exploration, feature engineering, model building, evaluation, deployment, and monitoring, using Python, statistics, and machine learning.

Master git as a distributed version control system by creating snapshots, browsing history, and branching and merging, handling end-of-lines, and collaborating with GitHub; install and configure git.

Engage in rewriting history through data science and machine learning bootcamp with Python. Explore Python-powered data science and machine learning methods in this bootcamp to deepen historical analysis.

Explore big O notation and algorithm design using data structures like arrays, linked lists, trees, and graphs; practice scalable, interview-ready problem solving with Java.

Explore linked lists using Python within the data science and machine learning bootcamp, understanding their structure and practical applications.

Explore stacks in data science and machine learning bootcamp with python. Learn how python supports stacks in data science and machine learning contexts.

Explore the queue data structure and how it supports data science and machine learning workflows in Python.

Explore hash tables and their role in data structures within data science and machine learning projects using Python, enabling efficient storage and retrieval of data.

Explore binary trees as fundamental data structures, and learn how to implement and analyze them in Python for data science and machine learning applications.

Explore AVL trees in the data science and machine learning bootcamp with Python, focusing on practical applications in programming.

Explore graphs in data science and machine learning with Python, introducing graph concepts and how they apply to real-world data problems.

Explore undirected graphs, their properties, and applications in data science and machine learning using Python.

Learn sorting algorithms in Python to support data science and machine learning workflows for practical data tasks.

Explore string manipulation algorithms in Python to process text data for data science and machine learning tasks.