How to pass an analytics job test – Part I – SQL.
With the demand in data analytics professionals growing stronger than ever, recruiters find themselves in a peculiar position of having to screen hundreds of potential, seemingly qualified candidates. Some firms turned to the proven selection tool: pre-employment skills assessments; and analytics-related tests are on the rise, especially for the junior level industry positions. No tests are the same, but most are designed with the sole purpose of gauging candidate’s cognitive ability to understand the problem at hand and having the technical know-how to implement a working solution. Two types of analytics tests that my students shared involved using either: Microsoft Excel or SQL language. Most of relational databases can be queried using a dialect of SQL, and as such, knowledge of SQL is as essential for a data analytics professional, as their excellent communications skills. In this post we will go through an example of a SQL job test, while in the next article we would focus on an Excel problem.
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.]):