Getting the Most Out of Your Data: 2-Day Workshop


As a supervisor a data science-based project, use this workshop to learn how to extract the most value out of your real, imperfect data - and make it in the best way possible using Microsoft Azure!

It may be not so challenging to come up with a sensible model when your data is perfect - but in real life it almost never happens. 99% of the time real data has missing values, noise, outliers, excessive information, and all those things that make it harder to use.

Data preprocessing is the most time consuming - and expensive - part of a DS-based project. Properly processed and well laid-out data, as well as efficient, domain-specific features are the key success factors.

At this workshop, you will learn some techniques of dealing with real datasets. Also, using Azure ML Studio you will see how data processing techniques can affect the performance of different models. Having completed this workshop, you will understand the challenges your machine learning team faces, why data preprocessing is so challenging and important, and how you can help them achieve the best results.

To run the analysis, we will use Microsoft Azure Notebooks to develop and run Jupyter Notebooks in the cloud. The models will be created and analyzed in Azure ML Studio. Data for the workshop would be downloaded from a datastore on Microsoft Azure Cloud.


    Day 1

  1. Preliminary Data Analysis and Processing
  2. Exploratory Data Analysis
  3. Day 2

  4. Feature Engineering
  5. Machine Learning model training and evaluation

Audience Requirements:

  • Basic knowledge of Python and ML
  • A Microsoft ID to access the enviroment

If you have no coding experience, you are welcome to join us to get a sense of how much effort is invested to develop a machine learning project. Solutions will be provided at the end.

This workshop is available on-premise or online, by arrangement. Price per person for on-site training, excl. travel costs. A min. number of participants are required for on-site training.