ECIM 2019 - 16th. to 18th. September in Haugesund


'Digital Transformation - from Vision to Reality'


Bootcamp - Program Details - Monday 16th.


Please note that this session requires binding registration. You can choose to attend the BootCamp when you make your registration to the Conference. (For participation a Fee of NOK 360,- pluss VAT will be added to your payment).


Data Science for Everyone: How to think like a data scientist


Session managers and presenters


Attila Balazs, Principal Consultant at Sword Venture

Bio: Over the last years I have been helping BP to develop their data science capability and to grow the data science community. I have been involved in many data science projects in the subsurface domain, applying Natural Language Processing (NLP), Computer Vision and Timeseries Analysis.

Contact Attila



Ruairi Dunne - A Data Science graduate with 1 years’ experience at BP

Bio: Having a BSc and MSc in Geology & Petroleum Geoscience, Imperial College, and Energy Industry internships. Undertakes a role that includes programming in python for data visualization, wrangling, exploratory data analysis (EDA) and machine learning. contact Ruairi




BootCamp Program 

15:00  Opening presentation – Introduction to Data Science

15:25  Hands-on lab

Exploratory Data analysis - Data Visualisation - Data Wrangling


16:15  Coffee break


16:30  Hands-on lab
Machine learning - Model evaluation - Deployment


17:45  Summary 

Scope of the Bootcamp


In this session you will learn how to think like a data scientist and, after a brief introduction to the data science workflow, you will work through an example problem. You will learn how data scientists approach problems, which techniques and tools they use and how you can apply them in your daily work to be more productive and to explore new ideas.
We will walk you through the Data Science workflow: Exploratory data analysis (EDA), data visualisation, data wrangling, feature engineering and data cleaning, machine learning, model evaluation and deployment.


Why join and End result

On completion of this bootcamp you will have a great understanding of the end to end data science workflow and you would have had a chance to try out the tools and techniques data scientists use to solve difficult business problems.  We will develop code together which takes you through the data science process and you can use this as a reference when applying these techniques within your daily work.

The field of data science is one of the fastest-growing and most in-demand fields in the world, as data accumulates, organizations are using data scientists to find meaning in the numbers and drive business decisions. A great community and open source tools are making it easier for more people to enter the field. This session will give you a good understanding of how data science and machine learning is applied to solve business problems and you will have a chance to get hands-on experience with the tools behind today’s hot topics AI, Machine Learning and Deep learning.

Who should join

The session is aimed at beginner to intermediate, but anyone interested in the data science workflow is welcome. We will aim to balance theory and practice, where you will get an insight into how data scientists approach a problem. You will also get an opportunity to code, therefore some familiarity with the Python programming language is useful, but not necessary and everyone is welcome.



Practical information - To participate please verify the requirements listed below


- Have a laptop, connect to wifi

There are two ways you can participate, either you have everything installed on your laptop or you can just login into Google Colab using your google account and access the bootcamp code and data there. We will provide more details closer to the session.


- Use Google Colab needs google account:

- Installation on your laptop

Bootcamp will be aimed at beginner and intermediate level. Some prior knowledge or python, jupyter, git is nice to have, but not necessary.

We will be using Python, Jupyter Notebooks, Scikit-Learn, Matplotlib, Pandas, Numpy and Git.