# Useful libraries for data visualization

Great overview / comparison of these libraries, at pbpython.com.

There are so many libraries and frameworks used in Python for data analysis that I had to take a step back and illustrate how they were laid out.

**PyDataSet**- Provides instant access to many datasets right from Python (in pandas DataFrame structure).

**NumyPy**- The fundamental package for scientific computing with Python. Fairly low level tool.
**Pandas**- Built on NumPy, and adds much more. Provides rich time series functionality, data alignment, NA-friendly statistics, groupby, merge and join methods, and lots of other conveniences.

**SciPy**- Collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.
**Scikit-learn****I ALWAYS USE THIS**- Module for machine learning built on top of SciPy
- Simple and efficient tools for data mining and data analysis
- Built on NumPy, SciPy, and matplotlib

- Collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data.

**Matplotlib**- You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code.
**Seanborn**- Visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

**Plot.ly**- Easily turn your data into eye-catching and informative graphics using our sophisticated, open source, visualization library and our online chart creation tool.
**Will not work in Azure Notebooks**. Can be hosted in their cloud, or offline, but requires a large amount of data IO, which exceeds what the notebook can handle.

- Easily turn your data into eye-catching and informative graphics using our sophisticated, open source, visualization library and our online chart creation tool.

# Useful Python Tools for ML

These will allow you to get data science and machine learning tools running on your local machine, browser, or even the cloud. I use all three of these in my day-to-day work.

**Azure ML Workbench (Local)**- Integrated, end-to-end data science and advanced analytics solution. It helps professional data scientists to prepare data, develop experiments, and deploy models at cloud scale.

**Enthought Canopy****(Local)**- scientific and analytic Python package distribution plus key integrated tools for iterative data analysis, data visualization, and application development.

**Azure Notebooks****(Browser)**- Do all of your ML work from within a Jupyter (Python) notebook inside the browser. No setup / install. You can have public / private notebooks. Great way to share your work.
**Free.**

- Do all of your ML work from within a Jupyter (Python) notebook inside the browser. No setup / install. You can have public / private notebooks. Great way to share your work.

**Azure data science virtual machine****(VM )**- You can run this either as a
**Windows**or**Linux (Ubuntu)**VM. Click some buttons and you are good to go. Just make sure you turn**auto-shutdown**on! - The VM will have just about every tool you’d ever need to do data analysis or ml.

- You can run this either as a

# Udemy Courses

I get a lot of value from seeing how other people code, so I’ll watch these videos and code alongside them in an Azure Notebook. They are also **very affordable**, at around $12 per course. Typing in each line of code helps me remember. You can find all of it here.

## Machine Learning

**Python for data science and machine learning bootcamp**- Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

**Data science and machine learning with python hands on**- I found this one to be useful for the ML aspects, and got far more out of it
**after**I completed the python for ds/ml course above first.

- I found this one to be useful for the ML aspects, and got far more out of it

## Deep Learning

**Zero to Deep Learning with Python and Keras**- Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano, so I use it in any Deep Learning work.

**Complete Guide to TensorFlow for Deep Learning with Python**- Also taught by Jose Portilla, who teaches the first course I listed above.

# Books

Sometimes I prefer having a tangible item in my hand to highlight, take notes, and read on a plane. I’ve found these three books to be the most useful in my studies.

**Hands-On Machine Learning with Scikit-Learn and TensorFlow**- Now that you understand Python and the applicable libraries, you can actually use it for ML

**Python for Data Analysis**- Fantastic for
**learning Python**and growing familiar with the libraries you’ll use in data analysis. It is from the creator of the**Pandas**framework.

- Fantastic for
**Python Data Science Handbook**- Great overviews of Juypter notebooks, NumPy, Pandas, Matplotlib, Scikit-learn
**Free**lite version & code from the author here.

**Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking**- So helpful for explaining the business use case for ML and data science to non-technical individuals
- Also helps boil down problems into data science terms, and realize if it really is an ML problem

# Azure Notebooks

I mentioned these above, but for me the real value lies in samples and tutorials provided with them. The image below is how I progressed through them, broken down by difficulty. If you are brand new to the field of data science it would be a great place to start.

These are all available on the Azure Notebooks landing page.

Here is a .pdf of the order I would do them in, beginner -> advanced

# Additional Resources

**Machine learning cheatsheet (.pdf)****Extremely helpful**resource to learn the basics and use as a reference

**Machine learning algorithm cheatsheet****Machine learning basics with algorithm examples****[YouTube] Data School courses for learning ML**

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@DaveVoyles