ptzagk / applied-data-science-with-python-and-jupyter

Use powerful industry-standard tools to unlock new, actionable insights from your data

Applied Data Science with Python and Jupyter

This course demonstrates how you can leverage the more advanced offerings of Jupyter, and take your understanding of it to the next level. From performing efficient exploratory analysis of your data to creating interactive reports and dashboards, you will also learn how you can deploy and secure your Jupyter noteboook. You will understand how you can integrate third party plugins to Jupyter for a host of other tasks. You will also see how you can run your notebook in batch mode and use it non-interactively for your ETL and reporting tasks. The book will also show you can run scripts in different languages with the Jupyter notebook efficiently.By the end of this book, you will be comfortable in using the Jupyter notebook for not just your routine – but also much more complex tasks.

What you will learn

• Understand why Jupyter notebooks are a perfect fit for your data science tasks
• Perform scientfic computing and data analysis tasks with Jupyter
• Interpret and explore different kinds of data visually with charts, histograms and more
• Extend SQL's capabilities with Jupyter notebooks
• Combine the power of R and Python with Jupyter to create dynamic notebooks
• Create interactive dashboards and dynamic presentations
• Master the best coding practices and deploy your Jupyter notebooks efficiently

Hardware requirements

For an optimal student experience, we recommend the following hardware configuration:

• Processor: i5 with minimum 2.6 GHz or higher, preferably multi-core
• Memory: 8GB RAM
• Hard disk: 10GB or more
• An Internet connection

Software requirements

You’ll also need the following software installed in advance:

• Python 3.5+
• Anaconda 4.3+

Python libraries included with Anaconda installation:

• matplotlib 2.1.0+
• ipython 6.1.0+
• requests 2.18.4+
• beautifulsoup4 4.6.0+
• numpy 1.13.1+
• pandas 0.20.3+
• scikit-learn 0.19.0+
• seaborn 0.8.0+
• bokeh 0.12.10+

Python libraries that require manual installation:

• mlxtend
• version_information
• ipython-sql
• pdir2
• graphviz