Hands On Python Data Science - Data Science Bootcamp

Begin Python for data science by building a Jupyter notebook and mastering variables, data types, lists, dictionaries, loops, list comprehensions, and functions. Apply these skills in a class project.

Explore how Python powers data science with simple syntax, extensive libraries like Pandas, NumPy, and Matplotlib, and practical data analysis and visualization workflows.

Start your data science journey by installing Python, setting up Anaconda for Jupyter notebooks, and configuring the environment across Windows, Mac, or Linux.

Learn to create and run your first Jupyter notebook, write and execute Python code in cells, navigate with shortcuts, and export notebooks as Python scripts using Anaconda and VS Code.

Create your first Jupyter notebook and print hello world, learn to navigate cells, run code, save and export as Python script in a data science workflow.

Explore code and markdown cell types in Jupyter and Google Colab, learn switching between cells, and use the print function and documentation to explain data science workflows.

Learn how variables act as containers in Python for data science, assign and print values in a Jupyter notebook, identify data types such as string, int, float, and booleans.

Explore strings, integers, floats, and booleans as fundamental data types in Python for data science, learn variables, printing, types, and basic operations in a Jupyter notebook.

Learn to create lists and tuples in Python, using indexing to access and update elements. Understand that lists are mutable with square brackets, while tuples are immutable with parentheses.

Learn how sets work in Python, define sets with curly brackets, understand that a set is immutable, remove duplicates, and convert between sets and lists.

learn how to define a dictionary in python using curly braces, assign key-value pairs, and access or print values by their keys, with comparisons to lists, sets, and tuples.

Learn how to perform crud for lists in Python, including create, read, update, delete; use append, insert, clear, and remove to manage list items.

Learn the CRUD for dictionary in Python, including creating dictionaries, reading all and by key, updating values, adding new keys, and deleting keys with practical examples.

Explore Python conditional logic with if, else, and elif statements in data science using Jupyter notebooks, indentation, and comparison operators to control output.

Learn to use the in statement in Python to check country membership in a list and print a message, with attention to quotes, escaping, and case sensitivity.

Explore how to use Python's for loop to iterate over lists, print values, and apply if, elif, and else conditions, with examples of breaking the loop.

Explore the continue, break, and pass statements in Python within for loops. Learn how break stops a loop, continue skips to the next iteration, and pass serves as a placeholder.

Explore how the while loop works in Python in a Jupyter notebook, including managing conditions, break statements, and avoiding infinite loops, with a cricket-themed example counting balls bowled.

Learn to loop through dictionaries in Python by iterating over keys, values, and key-value pairs using for loops and the dictionary methods keys, values, and items.

Explore list comprehensions in Python to transform strings in a list, using the title method to capitalize names, and convert lists into arrays for data science workflows.

Learn how to define a function in Python with def, name it, indent its body, and call it to print outputs, including looping with range.

Learn how to use positional arguments in Python data science, define functions, format strings with .format, and print personalized greetings for a list of names using for loops.

Learn how to define and call functions with multiple positional arguments in Python for data science, including passing values like name and country in the correct order.

Create a Jupyter notebook and build a Python list with strings, integers, floats, and booleans. Execute, print the list, then save and upload the notebook to the project panel.

work on class project two by defining a function in Python with def, printing inside, calling and executing it in a Jupyter notebook, then save and upload.