HOSD Mentoring — Our Mission

Jonas Schröder
9 min readMay 18, 2021

The future of work and why we offer free one-to-one mentoring for data students.

Dear Reader,

if you did not come here directly from our HOSD website or don’t know about us and our mission, I make it short for you:

We are a group of Data Professionals that offer free one-to-one mentoring for those interested in becoming a Data Analyst, Data Scientist, Machine Learning Engineer, etc.

We do that because it’s important for the individual we guide as well as the society we live in. Why? This will be answered by this blog post on the future of work. And if you’re not interested in a rather philosophical discussion, feel free to jump at the end to “HOSD Mentoring — Our offer to you”.

It particular this blog post is about:

  • the future of work (will automation replace us all?)
  • career change through self-directed learning
  • the overlooked characteristics of a successful data professional
  • what HOSD stands for and how we help our students for free

The Digital Future of Work

There are so many studies about the future of work that I don’t even know where to begin. They all have in common that some kind of jobs will disappear, most will definitely change, and plenty of new job positions will be created. These shifts are historically inevitable and often caused by technological improvements.

Before the second Industrial Revolution in the early 18th century happened, most western civilizations have been characterized by agriculture. Then we moved from the field into the factory, and later from production to service. Today, less than 7% of the world’s GDP is attributed to agriculture, while 30% is coming form the production sector and 63% from the service industries (CIA World Factbook).

The Industrial Revolution was characterized by the shift from hand production to machine production, powered by inventions like the steam machine. In the recent past, the biggest influencing factors have undoubtedly been the advancements in computer techologies and networking systems. Modern technology allows for automation far greater than in the Industrial Revolution, not just in the factory, but also for office work.

This can be seen as a risk to many of the most common jobs in our service economy or as an opportunity — but it will definitely be a challenge.

Emergent and Disappearing Jobs by 2025

The World Economic Forum regularly publishes reports on the future of work. Their 2020 edition (which you can download here) made a number of relevant points for this dicussion.

New technologies (cloud computing, big data analytics, e-commerce, robotics, …) are driving future growth across industries but make workforce disruptions inevitable. The extent of disruption varies for the individual. Especially redundant jobs and those that can be fully automated by machines will be at risk.

“Based on these figures, we estimate that by 2025, 85 million jobs may be displaced by a shift in the division of labour between humans and machines, while 97 million new roles may emerge that are more adapted to the new division of labour between humans, machines and algorithms, across the 15 industries and 26 economies covered by the report.” — World Economic Forum 2020

While the report states that pretty much every part of work will be affected to a certain degree by these technology shifts, the elephant in the room is the question which roles will be replaced most likely and which will emerge.

Top 5 jobs with DECREASING demand:

  • Data Entry Clerks
  • Administrative and Executive Secretaries
  • Accounting, Bookkeeping and Payroll Clerks
  • Accountants and Auditors
  • Assembly and Factory Workers

Top 5 jobs with INCREASING demand:

  • Data Analysts and Scientists
  • AI and Machine Learning Specialists
  • Big Data Specialists
  • Digital Marketing and Strategy Specialists
  • Process Automation Specialists

All five jobs with increasing demand have in common that they are about dealing with data while those jobs who will be replaced by machines are rather of administrative nature.

We might feel some optimism from a societal perspective when we read that more jobs are created (97 million) than replaced (85 million). Jobs have alway come and gone, some might say. However, the risk of complete replacement by machines and automation through technological revolution is at a new height and this time, some researchers say, we should expect mass technological unemployment as a very likely scenario.

The sceptic would say that we cannot train factory workers to be data scientists. Thus, the ones losing their job are not the same as those who will benefit from these shifts. An older country in a demographic sense might be less likely to adapt as well as younger countries. However, this is almost irrelevant from an individual’s perspective. I find this discussion fascinating, especially how to deal with a society that won’t be made up of full-time workers (basic income? tax revolutions?). But only want to hint to these questions as a side note here.

We are not doomed

The sceptic might be right for many cases but is no reason to just give up. I would even go so far to say that for most people the personal shift is possible and has never been easier. Let’s take an accountant for example, an at-risk job based on the report. She is a person that is comfortable dealing with numbers. She learned a complex system (for example taxation) that is regularly changing, and kept her knowledge up to date.

If we ignore the “what” of her work for a second and focus on the “how”, we have a person that proved to have two of the most important success factors for the emergent roles: dealing with data and constant learning.

Data Professionals are already coming from all kinds of fields today. Based on LinkedIn data, 72% pivoted to Data and AI from an unrelated field (as stated in the report on page 33). Career pivots will become more common in pretty much any field in the next years.

Again, I am not saying that all accounts will or even can become big data analysts or programmers. What I want to point at is that we cannot generalize really, and the personality and mindset is at least as important as the prior work experience, if not more.

If you consider yourself a constant learner and are generally curious about technology and how the world works, you are probably less likely to be replaced by a machine, regardless of your today’s work. But you should think about your tomorrow’s work. And you won’t have to create the machine but learn how to work with it.

Career Change through Self-directed Learning is possible

Let’s assume that reading this made you feel more excited and inspired than scared. You are generally curious, want to be prepared for the future of work, and are willing to put in significant time to learn new skills. Great! But you might not know where to begin.

I can only talk about my experience as a self-taught Data Analyst (progressing to Data Science):

The biggest challenge is not getting access to knowledge and resources, it’s knowing where to start and how to deal with information overload.

And it’s more about mindset than you think! There is not a single person in the world that knows how to make a pencil (if you don’t believe it read the essay I, Pencil by Leonard E. Read) and there is not a single person that knows everything about Data Science or Machine Learning.

These fields are huge and ever-changing. The best Machine Learning researchers in one field often have a rather basic knowledge of other ML techniques. They are highly specialized and they have to be to perform at the level they do. So don’t be afraid to not know it all, because that’s impossible.

Dealing with data nowadays is only really possible when you’re able to code (e.g. in Python). But you don’t need to be a programmer in a sense that you are able to build your own software application or a complex system of scripts all by yourself. More than 2,000 software engineers worked on the creation of Windows 10, with the support of 2,000 more. Technology like any other field is one of collaboration. Division of labour and therefore division of knowledge is the norm.

Since last year I support some students who want to learn more about data as part of TechLabs. And in my conversations with them I found these three points the most overlooked ones:

  • You don’t need to learn everything to get started and even work in the field (because you will never be able to learn everything anyway).
  • While knowing to work with code will empower you and is to some degree necessary to be an effective Data Professional, you don’t need to know Mark Lutz’ 1,600 page book “Learning Python” by heart or take a three-month course before opening a Jupyter Notebook.
  • It doesn’t really matter whether you use Jupyter Notebook or Spyder or Pycharm or any other IDE (text editor and the console might be too complicated) when you start out. Also it doesn’t matter in which language you learn programming fundamentals (we recommend Python). Just get started and comfortable. Getting to results fast will be the best motivator.

In short: Get started anywhere and don’t overthink.

HOSD Mentoring — Our offer to you

Thinking too much about the questions above (i.e., “Can I do this?” and “Am I doing this right?”) will hinder your progress or might even block you from getting started. That’s why we at HOSD mentoring pay so much attention to the mindset and not just to technical aspects of becoming a Data Professional.

We are not “teaching” our students anything in the sense of teaching how it’s done by schools or online courses. Everybody’s background and motivation is different. That is why first of all we listen in our one-to-one sessions. We need to understand who you are and where you’re coming from. From there on everybody has their own journey and the world is already full of free class A material. Let’s take a look together!

Our mentors are doing this for free to be able to help anyone who’s interested. We all have people that guide us on our way and we want to pay it forward to those who want to join the data community. Working in data is not just a very secure position that comes with above-average pay, it is quite frankly a heck lot of fun!

We don’t expect anything from our students other than that they are self-directed and hands-on. This is what HOSD literally stands for. We believe that in today’s world (not just in the field of data, btw) it’s necessary to have these characteristics.

  • Self-directed means you choose your own path. Nobody will and give one to you. This freedom can be challenging but the upside is far greater than the uncertainty.
  • Hands-on means that your focus is more on practice rather than theory. Our mentors as well as our students have a bias-for-action (that’s what Google and Amazon says about their employees). You have a problem, you search for the solution and try it, then repeat. This iterative process is at the heart of any programmer. Don’t be afraid to get your hands dirty. There’s nothing you can break.

That’s it. You don’t need to have a degree in anything. It could be the first time you opened a programming editor, it doesn’t matter. The important thing is that you want to get started, are hands-on and self-directed. Then we would love to get to know you and support you where we can.

If that sounds interesting to you, you are just a few clicks away from talking to one of our mentors. You can simply book one of the open slots on our website. Our mentors and you will automatically get a Google Calendar and Meet invitation. It’s that simple.

Alright, again we hope you’re rather excited than scared about the statistics we shows you in this blog post. A great future lies ahead of us and anyone who want to be part of it can.


Jonas and the HOSD community.



Jonas Schröder

Writes about how #AI and #ML applications help in different fields like #Finance and #Marketing. Data Scientist at Otto GmbH