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Application Skeleton for Flask and AngularJS

Flask and AngularJS

A constant challenge we face at IST Research is ensuring we build all of our applications in a way that makes them easy to scale. During my practice of deep work this week and thinking about that challenge, I decided that every application I build needs the following three things:

  1. Logging
  2. Statistics
  3. API (Application Programming Interface)

All three of these are very important when building and scaling fully distributed applications. [Read more…]

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Examples of Data Pipelines You Can Build Today

PipelineOver the past few months I started hearing the term “data pipeline” more and more at the local data meetups. Curious as to just what that meant, I looked it up. In this post I'm going to tell you what I found, and more importantly provide real-world examples of data pipelines you can use for your data projects. [Read more…]

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Create a Simple Python Web Scraper to Get Pricing Data

Python LogoThere are many methods in Python to create a web scraper. One of the simplest is using a combination of the built-in requests library (to obtain web pages) and the Beautiful Soup library (to parse the pages and extract data). With my book – Python Business Intelligence Cookbook – being published soon, I was curious how, or if, the pricing my publisher sets changes over time. In order to track it, I created a simple web scraper. Code below… [Read more…]

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Python Business Intelligence Cookbook Pre-Order Offer

I know how hard it can be to take what you read in a book, especially a data science book, and apply it to the data you have to work with. I want to save you that experience.

Python Business Intelligence CookbookIf you pre-order my upcoming book – Python Business Intelligence Cookbook – you'll receive a metric ton of bonus materials and resources that will help you take what you learn in the book, and apply it to your data. Specifically, when you pre-order, you'll receive:

  • Pre-webinar videos
  • A seat at the two-hour live webinar
  • Business intelligence project checklist
  • List of additional resources
  • 30-days of post-release email Q&A

Learn more about the book and this offer at pythonbicookbook.com.

Thank you for your support!

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Python Web Scraper Docker Image

Docker PythonFrankly, I couldn't come up with a cooler title than what you just read 😛

I was thinking to create a long post giving you the step-by-step instructions on how to install everything you need to scrape the web with Python. Then I got a bit smarter about it and made a Docker image you can download and run literally anywhere. [Read more…]

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Three Pandas Tips for Pandas Noobs

Happy PandaLast week after my talk at Data Science DC on how to create your first predictive model in Python, a fellow meetup member asked me about using Pandas for some data engineering work he was doing. In short, his DataFrame didn't seem to be applying the changes he was attempting to make. After a bit of conversation I found out he was missing three key pieces of functionality in Pandas:

  1. Using inplace=True to make changes stick
  2. Applying a function to a single column of a DataFrame
  3. Applying a function that takes arguments to a DataFrame

While the Pandas documentation is very good, it isn't 100% clear on how to use this functionality. So to help him, and to help you, I've created an iPython Notebook which shows you how to do all of this!

Get the code below, and leave any questions you have in the comments section. [Read more…]

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How to Create Your First Predictive Model in Python

Python LogoIf you’ve been reading books and blog posts on machine learning and predictive analytics and are still left wondering how to create a predictive model and apply it to your own data, this presentation will give you the steps you need to take to do just that.

Tonight at Data Science DC I will be presenting on the steps you need to take in order to go from raw data to a trained predictive model you can implement in a production system. [Read more…]

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Data Acquisition and Wrangling with Python Workshop

District Data Labs

Eighty percent or more of the time spent on data science projects is spent acquiring data, cleaning it, and preparing it for analysis. That data can come from a variety of sources, including APIs or individual web pages. However, not all data is created equal. Once we have automated its acquisition, much of it requires lengthy cleaning and formatting before it can be used. In this course you will learn how to obtain, clean, and mashup data in preparation for analysis. [Read more…]

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Using PyParsing For Web Scraping, Application Control and Data Wrangling

WebscrapingThat's what I'll be talking about at DC Python on April 7th.

In this talk you’ll learn how to use pyparsing, a free Python module, to create and execute simple grammars for web scraping, application control and data wrangling. Dump the nested if statements and get parsing. Oh yes, there will be code, and lots of it, to get you started!

RSVP today >> [Read more…]

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Join Me Wednesday for Growth Hacking and Knowledge Sharing

Growth Hacking

The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely.

– Shawn DuBravac, Chief Economist and Sr. Director of Research, Consumer Electronics Association

Growth hacking is a marketing technique that blends creativity, analytical thinking, and social metrics to gain exposure and sell products. Put another way, growth hacking is concerned with using data and the insights gained from data to get more exposure (marketing) and sell more products (sales) while keeping costs low and revenues high.

On Wednesday nightI'll be talking about growth hacking at Nightowls Coworking @ Techshop in Arlington, VA., specifically: [Read more…]