Artilogist Blog

Google invites applications for Summer Internship in Software Engineering in May/June 2020 for 10-12 weeks in duration. As a Software Engineer, you will work on Google’s core products and services as well as those who support critical functions of their engineering operations. Applications are open from August 26 to August 28, 2019, only. Minimum qualifications Currently pursuing a BS, MS or PhD in computer science or a related technical field. Experience in software development in one or more general-purpose programming languages. Ability to speak and write in English fluently and idiomatically. Preferred qualifications Returning to a degree program after completion of the internship. Experience in systems software or algorithms. Knowledge of Unix/Linux or Windows environments and APIs. Familiarity with TCP/IP and network programming. Effective implementation skills (C++, Java, Python). Areas of Internship Product and Systems Development: Whether it’s find...

Update on April 30, 2019: Jonathan Cornelissen will be stepping down as the chairperson of DataCamp’s Board of Directors, in addition to stepping down from his role as CEO of DataCamp for an indefinite leave of absence without pay, effective May 1, 2019. Mr. Cornelissen will also be recused from the independent third party review described below and any decisions relating to his future role at DataCamp. To Our Community Update on April 30, 2019: Jonathan Cornelissen will be stepping down as the chairperson of DataCamp’s Board of Directors, in addition to stepping down from his role as CEO of DataCamp for an indefinite leave of absence without pay, effective May 1, 2019. Mr. Cornelissen will also be recused from the independent third party review described below and any decisions relating to his future role at DataCamp. To Our Community As the DataCamp Board of Directors, we want to assure our community we are taking the incident from October 2017, involving ou...

Sean Law talks about data science research and development at TD Ameritrade and much more! Hugo Bowne-Anderson, the host of DataFramed, the DataCamp podcast, recently interviewed Sean Law , a Senior Applied Researcher and Data Scientist at TD Ameritrade. Introducing Sean Law Hugo: Hi there, Sean, and welcome to DataFramed. Sean: Thanks for having me, Hugo. Hugo: It's a real pleasure to have you on the show, and I'm really excited to have you here today to talk about the research and development sides of data science, particularly as it pertains to your work at TD Ameritrade financial services and brokerage. We're going to get into all of that, but before that I'd like to find out a bit about what you do. But in an org such as TD, I'm sure there are a lot of different impressions of what data science and R&D do and are capable of. I'd like to start off by having you tell us a bit about what your...

Learn why data science readiness should become a priority in your company if it isn't already. Learn why data science readiness should become a priority in your company if it isn't already. Organizational Readiness for Data Science Data science readiness is important for every organization—no matter if you’re in an industry that is traditionally associated with data science and analytics or not. KDnuggets summarizes the four components of organizational readiness as strategic, domain, cultural, and operational readiness. Defining Data Science Readiness Organizations must thoughtfully incorporate these four elements when preparing for data science initiatives. Strategic readiness helps companies make the business case to link data science to organizational priorities and get executive sponsorship. Domain readiness is about investing in infrastructure, namely the tools and skills required to easily access and analyze data. Cultural readiness establishes ...

Here's the true story of how a non-data scientist learned R and significantly improved business outcomes. Spoiler alert: today, he's a data scientist at DataCamp. In 2016, Chris Cardillo was a Strategist supporting the media buying team at M&C Saatchi Performance (known at the time as M&C Saatchi Mobile), a digital advertising agency with over 100 employees. His team faced a problem familiar to many: how to efficiently aggregate data for client reporting. The main culprit was multiple sources of information. The teams he supported had to pull and compile information from disparate platforms, including social media (Facebook, AdWords, Twitter), ad servers (DoubleClick), mobile measurement partners (Kochava, AppsFlyer), and network vendors. A lot of time was sunk into logging into each platform, downloading reports, copying and pasting data, enriching the raw data with client-critical information, and manually QA’ing reports. Depending on the ...

You just finished writing a piece of code and you are wondering what to do. Will you submit a pull request and have your teammates review the code? Or will you manually test the code? You should do both of these things, but with an additional step: you need to unit test your code to make sure that the code works as intended. Unit tests can pass or fail, and that makes them a great technique to check your code. In this tutorial, I will demonstrate how to write unit tests in Python and you'll see how easy it is to get them going in your own project. Getting started The best way you can understand testing is if you do it hands-on. For that purpose, in a file named name_function.py, I will write a simple function that takes a first and last name, and returns a full name: #Generate a formatted full name def formatted_name ( first_name , last_name ) : full_name = first_name + ' ' + last_name return full_name ...