Training schedule
IN-COMPANY TRAINING PROGRAMS
Contact Giovanni Lanzani, if you want to know more about custom data & AI training for your teams. He’ll be happy to help you!
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Experiments the Right Way
Have you ever wondered how Google chooses a color for its home page? How do they decide between two variations? In business, how can you know if changing something will improve it or not?
The two-day A/B Testing and Experiments training will teach you everything you need to successfully run your own experiments.
This training is for you if…
You are a data scientist and you are interested in conducting experiments with statistics
- You are somewhat comfortable with Python & Statistics, and you are interested in understanding more
This training is not for you if…
- You are not comfortable with Python & Statistics and only want the basic concepts. (Check out the Innovation Through Experiments training course instead).
- You would rather deep dive into statistics. (Check out the Bayesian Statistics training instead).
Clients we've helped
What you'll learn
Designing and Running Experiments
- How to design meaningful experiments.
- The intuition behind the statistics needed to make an appropriate decision.
- The practical skills necessary to run your own experiments.
- Common mistakes made in the industry and how to avoid them.
Scaling Up Experimentation
- How to run multiple experiments at the same time
- How long to run an A/B test
- Why stopping early is a bad idea
- How to bootstrap to estimate uncertainties
Improving by Experimenting
- Conduct statistical experiments using Python.
- Avoid common pitfalls that can invalidate your experiments.
- Have confidence that you are making real improvements.
The schedule
- The importance of A/B testing
- How to determine the success of your experiment
- How to design an experiment:
- How to split the population
- How to determine the right metric for your experiment
- Experiment pitfalls and how to avoid them
- Classical hypothesis testing
- Further techniques for conducting A/B tests
- How long to run an A/B test and why early stopping is a bad idea
- Bootstrapping to estimate uncertainties
- Bandit algorithms for real-time A/B testing
After the training you will be able to:
- Conduct statistical experiments using Python.
- Avoid common pitfalls that can invalidate your experiments.
- Confidently make choices that lead to real improvements.
learning journey
Data Science Learning Journey
Airflow 
AWS 
dbt 
Docker 
Google Cloud Platform 
Keras 
Kubernetes 
Power BI 
PyMC 
Spark 
Rogier van der Geer
Data CharmerBefore joining GoDataDriven, Rogier obtained a PhD in particle physics. Rogier gained hands-on experience with handling enormous quantities of data and processing, or ‘charming,’ them into a manageable format before performing complicated analyses. After his PhD he exchanged physical science for data science at GoDataDriven, where he is now putting his skills to use on more business-driven problems. He likes applying data science to anything; be it his daily commute, improving his photography skills or the contents of his lunch box.
Clients include: Bakkersland, App Annie, Costa Cruises, ING, NPO, Ebay, KLM, and RoyalFlora Holland
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.