DataScienceWithPython

Get started with Data Science with Python

An engaging journey to become a Data Scientist with Python

TL;DR

There are two options.

1. Download all the notebooks from this repository and run them in Jupyter Notebook. Chapter one in eBook will get you started with that.
2. Follow along using Google colab

Note: On each of those options, you'll find:

• A starter folder, which contains all the notebooks, that are empty in order to follow along.
• A final folder, which contains all the notebooks with all the source code.

Option 1

• Download all Jupyter Notebooks from repo (zip-file-download).
• Unzip download (main.zip) an appropriate place.
• Launch Ananconda and start JuPyter Notebook (Install it from here if needed)
• Open the first Notebook from download.
• Start watching the first video lesson (YouTube).

Option 2

• No installations needed.

• Go to Colab Notebooks Folder

• Start watching the first video lesson (YouTube).

• Note: On each notebook, click on "Open in Colab", in order to open it on Google Colab

Why do most fail with Data Science?

• Most focus on getting good at all technical aspects:
  • Math
  • Stat
  • Python
  • R
  • Machine Learning
  • pandas
  • NumPy
  • PyTorch

...and the list could go on and we didn't dive into sub-categories (but you get the point)

DISCLAIMER!!! This is the wrong (long) way to learn!

Master the Data Science Workflow

• Understanding what matters
  • The full workflow
  • How to add value to customers
• Focus on how to add value
  • This can be done with limited technical knowledge
  • ...and we will cover all you need
• Later you can become an expert in whatever your interest are
• But you should first understand the WHY!

This course will cover all aspects of it with the focus to get you there as fast as possible!

What will we cover?

• Data Science Workflow
  • Acquire - Prepare - Analyze - Report - Actions
• Data Visualization
• pandas for Data Science
• Data Sources
  • Web Scraping
  • Databases
  • CSV, Excel & parquet files
• Where to find data
• Join (combine) data
• Statistics you need to know
• Machine Learning Models
  • Linear Regression
  • Classification
  • ...also check out the Machine Learning Course
• Cleaning Data
• Feature Scaling
• Feature Selection
• Model Selection

At the end of the course you are provided with a template covering all aspects of the Data Science Workflow

• Acquire - Prepare - Analyze - Report - Actions