Career Transition Towards Data Analytics & Science. Here’s my Story

Career Transition Towards Data Analytics & Science. Here’s my Story

In 1992, I entered the job market and landed a job as an advertising copywriter for McDonald’s. I was tasked with ideating radio, TV and print advertisements to curb burger, fries and soft drink sales. The internet did not exist in the public domain back then, and my first laptop was actually a mechanical type writer. Around 2000, I became a freelance marketing manager, working for small and mid sized businesses. At that time, my English was not good enough to work for companies outside of my home country Germany (it’s still far from perfect).
Fast forward 10 years, I was still working as a marketing guy, yet after years of self-study, my English became profoundly workable. I managed to acquire some of the largest US based IT and software companies as my clients, and in 2013, I started teaching myself to code. Back then, I was increasingly worried that as a technology illiterate, I might be flushed out of the job market in a forseeable future.
At the moment of writing this post, I am bootstrapping a data literacy consultancy, catering to large enterprises around the globe. I teach business users how to work with Excel in ways they haven’t seen before. Plus, I teach them how to code and work with data in a utility scale environment. My learning journey was tough, but it can be smooth for any business leveraging on my experience.
My biggest fear of becoming jobless turned into the business opportunity of my lifetime.

T-Systems employees protesting against their employer’s decision to release 10,000 workers who don’t possess any coding skills. Source: Verdi | Markus Fring
10 observations I made during my own transition, which might propel yours
You might be tempted to say: “Nah, that’s not me. An ad guy turned consultant!” And you know what? You’re right! I‘m not you. Just take my observations and use them to craft your own, unique career transition story. Use my learnings to avoid unpleasant surprises and expensive mistakes. In 5 to 10 years, so I hope, we will have thousands of stories, and distinct career transition patterns will emerge.
The observations below are not sorted in order of importance, I rather arranged them for the sake of an easily digestible narrative. Let’s get started.
1st observation: just learning to code will get you nowhere
My liason with coding was rather erratic, with many ups and downs, and it wasn’t until 2016 that my learning journey took a promising direction. All I knew back in 2013, when I wrote my first “hello world” line of code, was that programming will be the skill of the future for virtually any type of occupation. I had no idea though how that applied to me.
First, I tried to wrap my head around .NET which went terribly wrong. I then gave it a shot and learned the basics of Ruby on Rails, Python Django and HTML5/CSS. Ultimately, I was able to build sleek, beautiful websites using the Bootstrap framework, but instead of being proud, I felt frustrated: “What’s the point of being able to build simple websites if anyone with zero programming skills can create his own page via a website builder for just a couple bucks?” I felt that I put a lot of effort into acquiring ubiquitous and automatable skills. Even if I invested further energy into my web developer skills, I would eventually end up competing against highly skilled professionals from all over the world, many of which would be capable of delivering better results at significantly lower rates.
Eventually, I developed in interest in crunching data. Initially, I just wanted to better manage my personal finances. That was it back then, but it was the beginning of an exciting journey.
2nd observation: Excel is dead, long live Excel
Yes, Excel is the ultimate entry level drug for working with data. After using Google Sheets for a while, I realized that Excel is playing in a league of its own. It’s highly addictive, and after learning the basics, I discovered the cool stuff built into Excel – and beyond:

Power Query for processing and mashing up data
Power Pivot for advanced calculations with Pivot Tables
Power BI for interactive data visualizations

You can find an excellent post on the technologies listed above here.
3rd observation: the more you share, the more you learn
It initially felt very uncomfortable sharing my learnings as I go. “What if I don’t know enough about my subject and people criticize me for that reason?” I thought. It turned out that my worries were pointless.
Technology is evolving faster than ever, and businesses are being shaken up at an unforeseen magnitude. We just can’t wait decades for an expert culture to evolve, because by then, it’s going to be too late for us to learn from them. Peer learning, peer consulting and peer working is becoming the predominant way for individuals and businesses to keep pace with change, one iteration at a time.
I shared my learnings on Power Query and the built in M-Language in a series of blog posts. It was very well received within the global Excel, Power BI community and this series landed me some first assignments at large multinationals. I started delivering workshops, trainings, and surprisingly, the vast majority of my participants was not aware of latest technologies built into Excel. This came as a surprise to me: “Here I am, an Excel rookie, teaching veterans with +25 years of experience how to use Excel.” Each of my workshops I delivered so far was overbooked. To date, not a single participant had any objections against my peer learning approach, quite the contrary.
4th observation: citizen data scientists are coming, yet their scope is limited
Citizen data scientists is a new category of knowledge workers without a formal data science related educational background. They consume data science “as a service” from various vendors to perform data analysis, and that’s about it.
For example, you can connect to Microsoft Azure Cognitive Services via API from within Power BI, in order to run a sentiment analysis on your customer feedback data for example. I am guessing that you can use cognitive services provided by Amazon or Google, I admittedly haven’t tested their offerings yet. Likewise, you might use Tableau or Qlick as your business intelligence tool, which I am both not familiar with yet.
My point is that, regardless of the underlying technology, you don’t need to be a fully fledged data scientists in order to utilize machine learning capabilities. However, as a citizen data scientist, you can only consume services without any insight into the machine learning models at use. These are all black boxes and you need to put some faith into your vendor’s capabilities.
Apart from those limitations, I grew increasingly allergic against vendor marketing which claims that anyone can be a citizen data scientist, and all it takes is just joyfully clicking through a colorful UI. That’s just utterly wrong. It takes a significant learning effort to master business applications tailored for citizen data scientists.
5th observation: it takes a significant time investment, ideally a learning sabbatical to transition
From my own experience, it took me roughly 3 months of full time study to become a versatile citizen data scientist. So much about Microsoft Power BI marketing claim: “5 minutes to wow!” If you aim to make a significant career transition, you will most likely need to take a break from your work. Burning midnight oil and working through weekends might just get you dangerously close to a collapse.
You might consider a one month break, 3 months, 6 or even an entire year as I did. I will share details on planning a learning sabbatical in upcoming blog posts. If you are contemplating to take a full year learning sabbatical, consider this:

Opportunity costs: I turned down a whole year’s project worth north of 200,000 USD
Costs of living: I cut my personal expenses from 60,000 USD to 20,000 USD annually
Social costs: My environment did neither comprehend nor support my decision, such as my back-then-financée. Luckily, I kept the dog

Getting back to business from my learning sabbatical, I can speak from personal experience what it means to undergo a digital transformation. I can empathize with people who’s jobs are at risk due to their insufficient skills for a technology centric future. Digital transformation is not about opening loft office spaces with colorful bean bags, and playing table football games between casual work engagements –  leaving anyone who is not cool enough outside. It’s about making tough choices in the first place and putting in a lot of work over an extended period of time. A committed organization though can move mountains.
6th observation: blend your current expertise with new data analytics & science related skills
During my learning sabbatical, I focused primarily on mastering:

Excel & Power BI advanced features
Python in conjunction with Pandas
Anaconda Jupyter Notebooks

In addition to that, I learned the fundamentals of:

Math, Probability & Statistics
Statistical Machine Learning

I am now in the process of mastering:

Querying SQL server
Using Linux Command Line Interface
Apache Hadoop, primarily Pig, Hive and Impala

I have sufficient skills under my belt to apply for an entry level Data Analyst opening. “But what’s the point of taking a job with 40k in annual salary or less?” so I thought. Instead, I realized, it’s by far more lucrative to blend those skills I have acquired over 25 years with those I have been adding over the recent few years.
As I copywriter, I learned to write with clarity. “Why not start teaching business users how to become data literate, thus monetize on my learning experience?” was an obvious choice for me.
As a data literacy consultant, my daily rates are 100% higher compared to those in my previous freelance marketing manager role. More importantly, due to my strict personal expense regiment, I feel not pressured to quickly return to my previous annual income. It’s not about money alone anymore. For now, I feel comfortable living frugally, way below my means, enjoy the freedom it brings and focus on the long term goal of building a sustainable business (I am talking 10, 20 years here).
Instead of maximizing on billable workdays as I did in the past, I focus on productizing my service. Tens of thousands of people are losing their jobs, because they lack sufficient technology and data skills. I am on a mission to transform the way business users work, just as I transformed the way I work and do business. If I am capable of making a highly unlike transition, so can anyone else.
You just learned about my transition. I am curious to hear about yours. Maybe you have questions I didn’t answer in my write-up? Please leave a comment or reach out to me via email or via LinkedIn.

Link: Career Transition Towards Data Analytics & Science. Here’s my Story