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Course details page: talkpython.fm/100days

#100DaysOfCode in Python is your perfect companion to take the 100 days of code challenge and be successful. This course is 1-part video lesson, 2-parts guided projects. You will be amazed at how many Python technologies and libraries you learn on this journey. Join the course and get started.

100 days of code is not just about the commitment. The true power and effectiveness is in having a guide and pursuing the "right-sized" projects. That’s why we have 33 deeply practical projects. Each paired with 20-40 minute lessons at the beginning of the project.

Just a small sampling of the projects you’ll work on include:

  • Understanding basic Python data structures
  • Searching large text corpuses with regular expressions
  • Consume HTTP services including the Twitter and GitHub APIs among others
  • Visual data with graphs using plotly
  • Convert your Python CLI (command line interface) app to a GUI application
  • Program against Excel in Python to automate your spreadsheet data
  • Build a text-based game and learn object-oriented programming
  • Automate multi-step web processes using selenium
  • Test your code with pytest and unit testing
  • Create a basic web app with Flask
  • Create a JSON-based online game service using Flask too
  • And 22 more projects!

View the full course outline.

This course is for anyone who wants to immerse themselves in Python for 100 days worth of learning and hands-on projects.

We don’t start from absolute zero in terms of programming but if you are new to Python we have a language appendix and we start somewhat slow. By the end of the course, we get into intermediate-level Python projects.

We have broken the 100 days worth of coding into 33 3-day segments. As you can see, the first day is largely learning the new topics (HTTP APIs, web scraping, databases, etc.). The following two days have some guidance but is much more hands-on than the first day.

Visit the full course page for all the details: talkpython.fm/100days

Modified on: Sep 16, 2019
0 clones

Azure Notebooks is a free hosted service to develop and run Jupyter notebooks in the cloud with no installation. Jupyter is an open source project that lets you easily combine markdown text, executable code (Python, R, and F#), persistent data, graphics, and visualizations onto a single, sharable canvas called a notebook.

Interactive Azure Notebooks provides security insights and actions to investigate anomalies and hunt for malicious behaviors. Each Azure Notebook is purpose-built with a self-contained workflow for a specific use case. Visualizations are included in each Azure Notebook for faster data exploration and threat hunting. Click on the button below to clone our prebuilt investigation and hunting Azure Notebooks into projects that belong to you. Modify and tailor your projects to your environment. Either run the Azure Notebooks for free or, for better performance, run them on a dedicated virtual host. Click here to

Azure Security Insights will provision notebooks and supporting modules for you in Azure Notebooks. You can also download the notebooks and modules and use them locally in a supported Python environment (Anaconda is recommended) or another notebook hosting environment such as or a JupyterHub environment that supports Python 3.6 or later.

Modified on: May 17, 2019

Your library is ready to go!

Here are a few things to get you started:

  • You can change the title and description of the library by clicking on the pencil icons in the upper right.
  • You can add new notebooks by clicking on the plus icon below.
  • Notebooks can be deleted by clicking the trashcan icon.
  • Open your notebooks by clicking the arrow button next to the notebook.
  • Share your notebook with others on social media using the sharing buttons in the top right.
Modified on: Sep 14, 2019
0 clones
Modified on: Jan 11, 2019
PySpark NLP
0 clones
Modified on: Feb 23, 2018

Demo files associated with "Threat Hunting with Notebook technologies" presented at Secureworld conferernce in Seattle, WA https://events.secureworldexpo.com/agenda/seattle-wa-2018/

Presentation: https://www.slideshare.net/ashwin_patil/threat-hunting-using-notebook-technologies

Github jupyter notebook viewer does not parse well, use online services such as nbviewer: https://nbviewer.jupyter.org or mybinder: https://mybinder.org/ to view and interact with notebooksby providing github notebook/repo URL.

Same repo is also cloned and available at azure notebooks: https://notebooks.azure.com/ashwinrp/projects/threat-hunting-with-notebooks

Basic Data Analysis and Visualization on Failed Logon Data

  • Data Source : Azure Data Explorer
  • Language: Python

Time series anomaly detection on successful logon data using anomalize package

  • Data Source : Azure Data Lake
  • Language: R

Threat Hunting with ip address from logs

  • Data Source : csv file with 4688 along with command line logs
  • Language: Python
Modified on: Sep 14, 2019
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