Review of leading (mostly) free Web Marketing Certifications.


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?!

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Working with sample datasets in BigQuery

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.]):
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Getting started with Google BigQuery and GDELT Project

Getting started with Google BigQuery and GDELT

          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.

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Where are the jobs? 2014 INC 5000 List might provide some answers…

2014 INC 5000 data viz

          What do Domino’s Pizza, Microsoft, Timberland, Tough Mudder, Intuit, Vermont Teddy Bear, E*Trade, Lending Club, Morningstar, Oracle, Fuhu, Cold Stone Creamery, Under Armour, and GoDaddy have in common? They all have appeared on Inc Magazine’s INC 500 list of the fastest-growing American private companies. Looking at the last year’s INC 5000 list might help us draw employment insights and identify best industries and places to look for a job. By definition, these company are experiencing tremendous growth, and as such, could be representative of future job opportunities in the US. In addition, according to Forbes , small businesses added over 65% of the net new jobs in the past two decades. So, where are the jobs?! Based on INC 5000 data, Chicago, IL employs over 5% [56,813] of all INC 5000 workers. Another 14% [144,847] of INC 5000 employees work in California. Top 3 industries by employment include: Human Resources, Business Products & Services, and IT Services . Together, they are responsible for almost 400,000 jobs, or 38% of the grand total.

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