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An engaging introduction to [Data Science](https://www.learnpythonwithrune.org/data-science-2/) with Python
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An engaging journey to become a [Data Scientist](https://www.learnpythonwithrune.org/data-science-2/) with Python
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## TL;DR
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- Download all Jupyter Notebooks from repo ([zip-file-download](https://github.com/LearnPythonWithRune/DataScienceWithPython/archive/refs/heads/main.zip)).
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- Unzip download (main.zip) appropriate place.
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- Unzip download (main.zip) an appropriate place.
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- Launch Ananconda and start JuPyter Notebook ([Install it from here if needed](https://www.anaconda.com/products/individual))
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- Open the first Notebook from download.
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- Start watching the first video lesson ([YouTube](INSERT LINK)).
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- Start watching the first video lesson ([YouTube](https://localhost)).
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## Why most fail with Data Science?
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-Focus on getting good at all technical aspects:
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## Why do most fail with Data Science?
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-Most focus on getting good at all technical aspects:
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- Math
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- Stat
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- Python
@@ -20,17 +20,44 @@ An engaging introduction to [Data Science](https://www.learnpythonwithrune.org/d
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- pandas
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- NumPy
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- PyTorch
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...and the list could go on and dive into each category (you get the point)
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**This is the wrong way to learn!**
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*...and the list could go on and we didn't dive into sub-categories (but you get the point)*
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## Understand the Data Science Workflow
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**DISCLAIMER!!!*****This is the wrong (long) way to learn!***
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## Master the Data Science Workflow
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- Understanding what matters
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- The full flow
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- The full workflow
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- How to add value to customers
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- Focus how to add value with simple setup - then you get far
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- Later you can become expert in any of it
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- Focus on how to add value
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- This can be done with limited technical knowledge
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-*...and we will cover all you need*
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- Later you can become an expert in whatever your interest are
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- But you should first understand the *WHY!*
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This course will cover all aspects of it with the focus to get you there as fast as possible!
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## What will we cover?
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- Data Science Workflow
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- Acquire - Prepare - Analyze - Report - Actions
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- Data Visualization
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- pandas for Data Science
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- Data Sources
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- Web Scraping
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- Databases
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- CSV, Excel & parquet files
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- Where to find data
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- Join (combine) data
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- Statistics you need to know
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- Machine Learning Models
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- Linear Regression
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- Classification
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- ...also check out the [Machine Learning Course](https://www.learnpythonwithrune.org/machine-learning/)
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- Cleaning Data
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- Feature Scaling
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- Feature Selection
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- Model Selection
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At the end of the course you are provided with a template covering all aspects of the Data Science Workflow
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