Python for Data Science - Beginner Friendly Full Course

Overview

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Embark on a comprehensive 5-hour journey into Python for Data Science, tailored for beginners. Master essential concepts including environment setup, coding fundamentals with a data science focus, and practical applications through hands-on projects. Explore topics such as variables, data types, conditions, loops, functions, classes, modules, packages, file handling, error management, and mathematical operations in Python. Gain proficiency in tools like Jupyter Notebooks, Google Colab, and Watson Studio while learning to work with APIs, parse JSON, and implement various Python projects. By the end of this course, acquire a solid foundation in Python programming specifically geared towards data science applications, setting you up for success in your data science career.

Syllabus

- Start
- Why you should learn Python
- How to get started
- Installing Anaconda
- Starting Jupyter Notebooks
- Creating a Jupyter Notebook
- Jupyter Shortcuts
- Exporting Jupyter to .py
- Cell Types
- Working with Markdown
- Accessing Documentation
- Google Colab
- Watson Studio
- SECTION 2 Variables & Data Types
- CRUD
- Variables
- Data Types
- Strings
- Integers
- Floats
- Booleans
- Lists
- Tuples
- Sets
- Dictionaries
- CRUD for Lists
- Creating a List
- Reading a List Using Indexing
- Updating List Values
- Using .append
- Using .insert
- CRUD for Dictionaries
- Create a Dictionary
- Read from a Dictionary
- Accessing Dictionary .keys
- Accessing Dictionary .values
- Updating Dictionaries
- Deleting from a Dictionary
- SECTION 3 Conditions & Loops
- Conditions and Logic
- if Statement
- else Statement
- elif Statement
- in Statement
- for Loop
- continue, break, pass
- while Loop
- Looping through Dictionaries
- List comprehensions
- SECTION 4 Functions
- Defining Functions
- Positional Arguments
- Multiple Positional Arguments
- Looping with an Index
- Keyword Arguments
- Combining Positional and Keyword Args
- return Keyword
- lambda Functions
- SECTION 5 Classes
- Classes
- class Statement
- __init__ Method
- self keyword
- Assigning properties
- Creating an object
- Methods
- Class Inheritance
- Defining a Child Class
- Inheriting using the super function
- SECTION 6 - Modules and Packages
- Modules
- Creating a helper module
- Importing modules
- Accessing Python Packages
- Working with APIs
- Installing packages with pip install
- Viewing installed packages with pip list
- Importing Packages
- Making API calls with requests.get
- Parsing JSON
- SECTION 7 Files & Error handling
- Working with Files
- Writing Files using the with statement
- Reading from files
- Error Handling
- Using try except statements
- SECTION 8 Math and Projects
- Math in Python
- Math Operators
- Addition
- Subtraction
- Division
- Floor Division
- Modulus
- Multiplication
- Power
- Rounding with round
- Absolute Values abs
- Math Package
- Python Projects