Promoting Business Value Creation through Data Science Applications
I am a Data Scientist / Oil & Gas Professional with a passion for data analytics, programming, creating business models and problem solving.
Solving problems by playing through different scenarios with Machine Learning and Monte Carlo models fascinates me – however, doing so should always have a clear purpose: creating new business, or improving an existing business by implementing smart data applications.
A venture that doesn’t create value for others is a hobby.
– Josh Kaufman
Me in a Nutshell
Programming / Application Skills:
- R Programming
- R Web Applications with Shiny
- Rapidminer, Azure ML
- Visual Basic for Applications
- HTML, CSS, PHP, SQL
Data Science Skills:
- Exploratory Data Analysis
- Web Scraping and Data Cleaning
- Statistical Inference
- Regression Analysis
- Machine Learning Algorithms
- Monte Carlo Models
- Data Visualization
- Prototype Data Products
- Interactive Web Applications
- Log Interpretation
- Formation Evaluation
- Reservoir Engineering
- Petroleum Engineering
- Oilfield Operations
- Oilfield Development
- Petroleum Economics
- Digital Oilfield
- Sales & Marketing
- Business Development
The Most Important Thing
I already knew I wanted to become an engineer when I had still two years left at school – at a time when I had to choose the two major subjects for these years, which would make up a big part of my final exams. The obvious choice would have been Mathematics and Physics (so I was told by the “experts”). Instead I chose Literature and Art, and I had to spent the next 2 years justifying my unorthodox choice to my family, friends, university advisers and even government representatives.
“Are you sure you want to become an engineer?” was the first question I heard. It was the wrong question to ask. I had not chosen these subjects because I was good at them “Asking the right questions is probably the most important skill of a data scientist.” (in fact, I never was great in painting stuff), but to broaden my horizon and venture into topics that I was unfamiliar with. Nobody even bothered to check on my mathematics or physics skills! Asking the right questions is probably the most important skill of a data scientist (and the second would be curiosity!).
The story has a happy ending – I did graduate in mechanical engineering instead of becoming an artist (as forecasted by the same, most worried about my career experts) and, more importantly, I learnt an important lesson: Expert advice can be dangerously misleading when applied without proper background knowledge.
This is even more valid today in the exciting time of data science and big data: Subject matter expertise (also called “domain knowledge”) is extremely important when implementing data science applications.
You can read more about this topic from these articles from Forbes and KD Nuggets:
Get in Touch
Questions or suggestions? Write me an e-mail.