25 years as a data scientist and statistician — reviewing my favorite topics

Table of Contents

  1. Simulating the Monopoly Board Game
  2. Writing the book “Applying Data Science — Business Case Studies Using SAS”
  3. Teaching statistics in primary school — My Dad has the most interesting job in the world. He is a data scientist
  4. Using mathematical optimization to generate business decisions
  5. Heading up the Analytic Community in the Austria-Germany-Switzerland region
  6. Sports Analytics — Supporting the Austrian Volleyball team with analyses for the European Championship 2019
  7. Demand Forecasting and Demand Planning in the retail and in the manufacturing industry
  8. Simulating the Development of Water Level at Lake Neusiedl
  9. Teaching the SAS Statistics/Machine Learning/Data Mining Education Trainings
  10. Applying Artifical Intelligence in a #data4good Project to protect our Environment
  11. Working as a Researcher in Medical Statistics and Biometry
  12. Yield Management in the Transportation Industry
  13. Data Science in Academics — Teaching at Universities and Business Schools
  14. Presenting Data Science Topics at large Analytic Conferences
  15. Writing the book “Data Preparation for Analytics Using SAS”
  16. Analyzing and Predicting Customer Behavior in the Telecommunications Sector
  17. Telling Statistical Stories
  18. Attending the SAS Global Technology and Product Manager Meetings
  19. Solving Statistical Problems and Performing Monte Carlo Simulations

1. Simulating the Monopoly Board Game

  • the visit frequency on the fields of the game
  • the profitability distribution of the properties that you can buy

2. Writing the book “Applying Data Science — Business Case Studies Using SAS”

3. Teaching statistics in primary school — My Dad has the most interesting job in the world. He is a data scientist.

4. Using mathematical optimization to generate business decisions

5. Heading up the Analytic Community in the Austria-Germany-Switzerland region

6. Sports Analytics — Supporting the Austrian Volleyball team with analyses for the European Championship 2019

  • The combination of which factors most often result in a point?
  • Under which circumstances do certain players act most efficiently?

7. Demand Forecasting and Demand Planning in the retail and in the manufacturing industry

8. Simulating the Water Level at Lake Neusiedl

9. Teaching the SAS Statistics/Machine Learning/Data Mining Education Trainings

10. Applying Artificial Intelligence in a #data4good Project to protect our Environment

11. Working as a Researcher in Medical Statistics and Biometry

  • dealing with high variability, small sample sizes and sparse data
  • handling data preparation and data quality issues
  • having to carefully select the appropriate statistical method based on the business questions and the nature of the data
  • being forced to be extremely accurate as the outcome directly affects our health
  • having to explain statistics to people who are experts in their field, however only have little knowledge in quantitative analysis

12. Yield Management in the Transportation Industry

  • You usually deal with big data, when analyzing detail sales data. And there are cases where you cannot just aggregated the data but you have to provide selected derived variables already on the detail data.
  • Data Quality varies across historic period, IT systems and also regions. Some data are not available to 100 %. And you have to make some educated guesses how your sample is biased and how you correct your data. Analytics can help here to provide meaningful imputation values across regions, seasons, segments, and data collectors.
  • Visualization is key. Over the last years the visualization capabilities have strongly improved, especially when it comes to interactive charts, big data visualizations, geo maps and the considerations of the time axis. Some of my projects started much earlier so we had to be flexible with the visualizations. In our project we enjoyed the flexibility of SAS/GRAPH and the capabilities to create individual geo map representations a lot. In this example your see that we mapped the course of the rail service and provided branches for the smaller local railways where we only had aggregated data.
  • Which travel relations (routes) are highly competitive and where can we quote a higher price as there is high demand?
  • Which segments between individual stations have high passengers numbers, a strong seasonal variation, or varying booking pattern?

13. Data Science in Academics — Teaching at Universities and Business Schools

  • I get feedback from young students and learn, how they view business problems, how they would approach them, what questions they have, what their expectations on data science are .
  • And I continuously improve my “explain statistics” capabilities as students and lectures are the best platform to extend your teaching vocabulary. Many of the examples that I use in my presentations on marketing events, originate from a discussion with students in my class room.

14. Presenting Data Science Topics at large Analytic Conferences

  • It is highly rewarding to present your work and your analysis ideas to a large audience and experience their interest.
  • I always loved to share my work. Consequently I always made my slides and the content available to others that they can benefit from it.
  • You receive important feedback. Presenting on conference allows you to expose yourself to the critical discussion of other data scientists. This allows you grow, get new inputs and ideas and improve your work over the years.
  • It also teaches you, how you can best present complex analytic topics in order to make it interesting and consumable to audience with a high diversity.

15. Writing the book “Data Preparation for Analytics Using SAS”

16. Analyzing and Predicting Customer Behavior in the Telecommunications Sector

  • From the first day on in consulting projects in the telecommunications sector I highly appreciated the openness, creativity and flexibility of people working in this industry. When I worked in the medical statistics area at the University all major decision where made by people with a large number of working years. In the telco projects most of us where around their 30s and we made important decisions about campaign plans, analysis strategies, and IT-implementations. For me this environment was quite a game changers and I replicated this open and collaborative attitude from the telecommunications industry in many of my other SAS projects.
  • We ran projects and analyses in different areas: churn prediction (of course ;-) ), customer segmentation, response profiling and analyses to fine tune campaign offerings. Market basket analyses, honeymoon-analyses (how do customers change their product and service usage after their first 3 months), network analyses, customer life-time values and many others.
  • A lot of these real-world experiences in preparing and quality checking for analytics were one of the triggers why I decided to write my first two books for SAS Press “Data Preparation for Analytics Using SAS” and “Data Quality for Analytics Using SAS”. I gained so much experiences in data mining and machine learning in these projects and I wanted to give them back to the SAS User Community.

17. Telling Statistical Stories

18. Attending the SAS Global Technology and Product Manager Meetings

19. Solving Statistical Problems and Performing Monte Carlo Simulations

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Applying data science and machine learning methods-Generating relevant findings to better understand business processes

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Gerhard Svolba

Gerhard Svolba

Applying data science and machine learning methods-Generating relevant findings to better understand business processes

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