Introduction

Part 1. Python. 1st Dataset. PDF files. Tika OCR. Regular Expression. Array und Function

Part 1. Pandas DataFrame. Kaggle. Jupiter Notebook.

Part 1. Independent Work Tasks. 2nd Dataset.

Part 1. GitHub. Desktop GitHub

Part 2. Python. PyTesseract OCR. Regular Expression. Array und Function.

Part 2. RegEx. Regular Expression in Python.

Part 2. Kaggle. Jupyter Notebook.

Part 3. Big Data Storage and MySQL.

Part 3. Practice. Export Excel worksheet data to a MySQL table

Part 3. A Storage System for Big Data. Hadoop.

Part 3. Practice. How Apache Spark makes your slow MySQL queries 10x faster.

Part 4. The Data Visualisation Tools. Introduction

Part 4. Practice. Data Visualization with Python. Kaggle and Jupyter Notebook.

Part 4. Online Data Visualization Tools. Introduction and getting started.

Part 5. Machine Learning. An Introduction.

Part 5. Practice. How does machine learning work?

Part 5. Workflow of a Machine Learning project.

Part 5. Practice. San Francisco – explore Building Permits Data. Build Predictive Model.

Python. Choosing python IDE. Anaconda. Install Python.

📚 Additional materials on topics covered in the course:

Download Visual Studio Code
https://code.visualstudio.com/download

Anaconda Distribution
https://www.anaconda.com/

Download Python
https://www.python.org/downloads/

Comparing Python to Other Languages
https://www.python.org/doc/essays/comparisons/

🔎 Topics covered in this course:

  • How to convert a PDF to text?

  • Python or Anaconda?

  • What is the best Python IDE for beginners?

  • How  do I install VS Code?

  • How do I install Python?

  • How to run Python in VS Code?

  • How does Python interpreter choose VS code?

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