Data Science Digest

Azure, Microsoft’s ecosystem for cloud services, offers a wide range of services including standard virtual machines, managed databases, and interactive notebooks, similar to its competitors Amazon Web Services and Google Cloud Platform. In this article, you’ll get a comprehensive overview of how to get started and which resources to turn to for your data and computing needs.

As mentioned in our prior article on on dashboard development, framing the problem is a critical first step in your process, and one that will ultimately drive utilization and outcomes if done correctly.

It sounds simple, but it's also equally important to outline and follow a project plan. Doing so will allow you to effectively organize your efforts, keep the project going, and ground you to core goals and objectives.

Migrating your information to a cloud-based infrastructure is a critical first step in digital transformation efforts. Doing so will allow you to scale your technology infrastructure to meet growing demands, eliminate the need to maintain hardware, and allow your company to become more agile.

Moving to the cloud streamlines operations and security, giving you added flexibility and more time to focus on the core of what you do best.

There are a several leading players in cloud-based data infrastructure, including Amazon Web Services (AWS). This overview will provide a reference for what AWS products might best support your efforts and take a look at some of the most valuable options for getting started when working with cloud-based data.

Supervised and unsupervised learning algorithms are often the first two ‘families’ of techniques introduced in machine learning classrooms and textbooks. So, what are they?

Designing a meaningful dashboard or visualization can be a complex and difficult task.

Outlining how best to display data on top of what metrics to track and highlight is a big ask, and doing it ineffectively can diminish the impact of your analytical insights.

This article will walk you through some design considerations and how to go about implementing your very own dashboard.

My first year as a data scientist, I witnessed myself and others retyping the same lines of code and retracing our work time and time again. Perhaps some of this did not warrant concern.

After all, how long does it take to type the standard imports,

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import pandas as pd

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import numpy as np

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import matplotlib.pyplot as plt

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%matplotlib inline

and the like?

Yet there were also plenty of real concerns, as my colleagues and I performed many of the same tasks repeatedly, filling null values, standardizing column names, and creating dummy variables. Shouldn’t we be able to standardize these rote processes and not have to recode the entire preprocessing pipeline every time?

Even worse, sometimes after a day’s worth of exploratory analysis, fruitful insights would surface, only to realize that the Jupyter notebook you’d been working on was a jumbled mess, having jumped around in the notebook repeatedly, fixing errors and rerunning cells. How on earth are you supposed to now repeat that process?

It’s also funny to me that despite proclaiming the immense value of object orientated programming, none of my instructors pointed out how to practically implement such a philosophy into a daily workflow.

I hope this article helps you sidestep the pitfalls many of us have fallen into in order to develop a more productive and sensible workflow.

So you've tried to scrape some data from the latest website, only to realize your current tool set of parsing HTML pages no longer suffices.

With the rise of AJAX, many of today's websites (including the likes of Netflix and AirBnB) use React.js or similar frameworks to build interactive interfaces where the DOM itself is updated fluently based on user interactions. This contrasts with older methods of navigating to a new URL and making an additional HTTP request.

In these scenarios, older tools such as BeautifulSoup may not be enough.

How can you use the Twitter API to keep a pulse on your customer base or market trends? From tracking followers to analyzing brand affinity, we’ll take a look at some various techniques that can be leveraged via the Twitter API along with logistic considerations, and regulations surrounding the Twitter API’s term of service.

In building a data-driven organization, unifying disparate datasets is essential, providing a comprehensive baseline for modeling and analysis.

But joining data together to establish this baseline can be messy.

As a data professional, time is at a premium. Here are some tips and trends you'll want to stay on top of!

Title: Towards open health analytics: our guide to sharing code safely on GitHub

Source: https://towardsdatascience.com/towards-open-health-analytics-our-guide-to-sharing-code-safely-on-github-5d1e018897cb
Author: Fiona Grimm
How: Provides step-by-step instructions and things to consider
When to use this: When preparing to create a GitHub page, especially which may include sensitive data
Why it's helpful: Case study with tips, instructions, checklist and links from someone who has done this before
Suggested application: Contribute to and benefit from the input of the global community
Business impact or insights to be gained: Good reference to provide management who might be resistant or concerned about sharing company code or information