US Budget proposal – 2018.
It’s been a little over a month ago since the U.S. Office of Management and Budget released a proposed blueprint for the 2018 budget . Comparing to the 2017 budget , largest cuts (in terms of the funding amount) would affect Health and Human Services, Agency for International Development (USAID), Education, Department of Housing and Urban Development (HUD) *, and Agriculture departments. Departments of Defense, Veteran Affairs, and Homeland Security will be the largest beneficiaries of the new plan, all receiving a substantial boost in funding. To make this plan a reality, the Congress will have to approve this proposal next month, something that would be quite a hard sell based on the current reception of the budget.
Review of leading (mostly) free Web Marketing Certifications.
I spent the last few areas of my working life practicing the fine art of Web Analytics while supporting Digital Marketing efforts of my employers. As a typical analyst I try to think critically, and the most common feedback I get is that I need to think like a marketer , not an analyst (e.g. impressions are great for building a brand, even though they don’t directly result in any meaningful eCommerce activity). Sure, I’ve taken my share of Marketing courses in school and am familiar with all 4 P’s of Marketing , yet I felt that I still needed some concrete proof of my digital marketing acumen to establish a level of trust needed to implement my ideas. Hence I found myself researching top Digital Marketing certifications that hopefully wouldn’t break the bank. After careful consideration I set my eye on the following: Google Analytics (I suspect there is no need to introduce GAIQ ), Google AdWords (Between Paid Search, Display, Video, and Mobile, Google has the bases of most paid channels covered, while also introducing some SEO concepts in the process), HubSpot (HubSpot should be familiar to any Marketing professional, since they pioneered the idea of Inbound Marketing; among other certifications they offer Inbound, and Email Marketing certifications). Finally, the most crowded digital marketing channel is arguably Social Media; as a top player in the Social field, I was hoping that HootSuite certification is one of the most reputable of the bunch. With the only channel missing from this mix being Affiliates Marketing, I embarked on the journey of earning these certifications earlier this month. Okay, I must admit I should have probably sat for these certifications sooner, but better late than never, right?!
Working with sample datasets in BigQuery
In the previous post we added public tables to our BigQuery interface. However, Google already provides sample data on various topics by default. While most of these tables are not updated, they still present some interest in terms of learning trends or insights on a multitude of topics. We will focus on 3 of these tables:
Natality (daily US births from 1969 to 2008),
GSOD (daily weather information by a station number from 1929 to 2009),
and Shakespeare (word index of Shakespeare’s works.)
Let’s start our exploration with the Natality dataset. The graph above charts share of teenager births, comparing to grand total by year. Between 1969, nominal number of births by teenagers went up from 307,561 to 441,110. However, this is not necessarily a bad news, considering growing US population. While in 1973, almost every fifth birth (19.55%) was by a teenager mother, by 2005 this ratio dropped to every 10th birth (10.18%.) To pull relevant source data, we simply need to run the following query (which would incidentally retrieve preteen births as well [outliers representing fewer than 200 births a year.]):
Getting started with Google BigQuery and GDELT Project
Once upon the time, the new kid on the block left more established search engines in the dust, then, after reinventing web-based email service, Google introduced its Apps. Today, let’s talk about one of the myriad services Google offers to us: BigQuery. Basically, this cloud-based service allows us to utilize Google’s hardware to store our own datasets or access public data on the go. Google provides API for Java, PHP, and Python access. In addition, various third-party tools now connect directly to BigQuery: Tableau, R, JasperSoft, and Simba to name a few. We get a 1 TB monthly usage quota to query BigQuery’s data for free. Some of the downsides of this service include: premiums for storing our own data and querying in excess of the free quota. We are also limited with data manipulation tasks we can perform in BigQuery; in fact, we can only append records to our table, we cannot update or delete them. Finally, this service uses a SQL language dialect, which lacks some of the SQL commands we are accustomed to: DISTINCT comes to mind, or resort us to some convoluted workarounds (try using the TOP command.) Meet, the GDELT Project – “the largest, most comprehensive, and highest resolution open database of human society ever created.” In this tutorial, we will learn some interesting facts about different countries, using GDELT data in BigQuery.