Setting up Your Analytics Stack with Jupyter Notebook & AWS Redshift

@machinelearnbot

From querying your data and visualizing it all in one place, to documenting your work and building interactive charts and dashboards, to running machine learning algorithms on top of your data and sharing the results with your team, there are very few limits to what one can do with the Jupyter Redshift stack. However, setting everything up and resolving all the package dependencies can be a painful experience. In this blog post I will walk you though the exact steps needed to set up Jupyter Notebook to connect to your private data warehouse in AWS Redshift. Jupyter Notebook is an open-source data science tool used by many data scientists and data analysts at some of the most data-driven organizations in the world, including Google, Microsoft, IBM, Bloomberg, O'Reilly and NASA. An extension of the IPython project, Jupyter Notebook is an application that runs directly in your browser and allows you to create and share documents with live code from over 40 different languages.


Setting up Your Analytics Stack with Jupyter Notebook & AWS Redshift

@machinelearnbot

From querying your data and visualizing it all in one place, to documenting your work and building interactive charts and dashboards, to running machine learning algorithms on top of your data and sharing the results with your team, there are very few limits to what one can do with the Jupyter Redshift stack. However, setting everything up and resolving all the package dependencies can be a painful experience. In this blog post I will walk you though the exact steps needed to set up Jupyter Notebook to connect to your private data warehouse in AWS Redshift. Install PostgreSQL First, since Amazon Redshift is based on PostgreSQL 8.0.2, we will need a PostgreSQL client library. If using Mac OS X, simply simply open up your terminal and type brew install postgresql For other operating systems, please see the installation instructions here .


Installing and configuring Python machine learning packages on IBM AIX

#artificialintelligence

Machine learning is a branch of artificial intelligence that helps enterprises to discover hidden insights from large amounts of data and run predictions. Machine learning algorithms are written by data scientists to understand data trends and provide predictions beyond simple analysis. Python is a popular programming language that is used extensively to write machine learning algorithms due to its simplicity and applicability. Many packages are written in Python that can help data scientists to perform data analysis, data visualization, data preprocessing, feature extraction, model building, training, evaluation, and model deployment of machine learning algorithms. This tutorial describes the installation and configuration of Python-based ecosystem of machine learning packages on IBM AIX .


Remotely Send R and Python Execution to SQL Server from Jupyter Notebooks

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Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to where your data lives. This tutorial will show you an example of how you can send your python code from Juptyter notebooks to execute within SQL Server.


Data Science for Startups: R - Python – Ben Weber – Medium

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One of the pieces of feedback I received for my blog series Data Science for Startups was that Python would be a better choice for data scientists joining a startup. This makes a lot of sense if Python is already your go to language for performing data science tasks. In my case, I have much more experience in R and wanted to provide an introduction to working with startups using a language that I've previously used to solve problems. Now that I've completed the series and turned it into a book, I want to start digging into Python as a scripting language for data science. For now I still prefer Java for productizing models, using DataFlow, but that preference may change as I become more familiar with the language.