Steffen Pütz Data Scientist

Phase 1 - Starting my career!

After school leaving examination, I started my career as a bank advisor.

Early I got the responsibility for a small branch.

I liked the time, but soon I discovered that I was more interested in finding

general trends and approaches to sell specific products to special target groups.

I became very good in recognizing customer needs from buzzwords in small-talk topics and

generalizing best practices to greater target groups.

I also built some simple calculation and visualization tools that automatized processes

and improved closing efficiency.

One example: A lot of customers wanted to park their money on their current account

without interest until the interest rates go up. So I built a calculator that show, how

much interest rate needs to improve to additionally compensate the parking time.

The only question left to ask was: "Do you think such an improvement is realistic?"

So I decided to quit my job to study and find a job where I could work more on an

such an analytical and/or strategical level.

Phase 2 - Studying Bachelor & Master

Due to my banking background, I made my bachelor in Economics. During my studies I really

made myself aware of my love for math, statistics and computer science.

While I worked as tutor for math and statistics, I was very confident in my abilities.

On the other hand I underestimated my talent in programming.

The computer science course lasted two semesters and focused on SQL and VBA.

The stuff was quite easy for me but I thought I am "just good for an economist" in it.

I don't even know today, why the field of data science never came up to me that days.

So I would have probably started to focus on that career path earlier.

Instead, after my bachelor I started my Master in Sales Management at the RUB.

The masters program sounded very attractive at it was limited for around 15 students per semester

and I wanted to get more specialized.

I focused on statistic-heavy courses and taught myself statistic tools like MPlus and SPSS.

By the end of my Master I had to decide between getting into Industry or remain at the

university for my PH.D.. I considered my theoretical skills way better than my practical skills

so I decided to go working and built up practical experience.

Phase 3 - First Job after study!

As mentioned before I wanted to work on a more analytical and strategic level.

So I joined the Sales Coordination & Business Development Department of tkre.

I worked on several topics in the fields of MI, BI and Business Development and Pricing.

Due to my affinity to statistics and programming I got full control of the Sales BI-Topics.

As I searched for possibilities to scale up methods and skills of statistical analysis

for greater data I discovered the field of data science - and instantly knew that this was

what I was always looking for - jobwise.

Phase 4 - Getting deeper into data!

Since I found out about the wide field of data science I fully jumped into it.

Due to my studies I had a strong foundation in statistics. So I started with

teaching myself python and achieving a stronger knowledge in computer science.

I started with the cs50 course of Harvardx on edx.org. This was really fun and challenging.

Afterwards I worked through different courses on udemy and youtube.

I also started to integrate programming in my daily working business

by building pipelines, cleaning data, and finding duplicates across different tables.

The more I used my new skills and daily business the more my colleagues requested my skills

to help them for different automatization tasks and analyses. Also I learned SQL and PowerBI.

To further improve my skills I worked myself through different literature (especially O'Reilly),

my daily reading feed on medium and started the MicroMaster-program in Data Science via MITx .

I recently finished the second of four courses.

Phase 5 - What's next?

The next step is working on projects, projects, projects.

The best way for me to improve from this point is to work on practical implementations.

And it is finally the reason, why I started this Journey.

To create cool stuff from data :-)