FANS Network Meeting | Data Science | March 2, 2021 | Virtual/MS Teams | Nordic

[DorteBrix]

Welcome to FANS Network Meeting – focusing on Data Science

Due to the Coronavirus, this event will be virtual through MS Teams

Welcome to this new Data Science Network that is cross Nordic and is held in English.

Date: 2/3-2021

Time: 09:00-12:00 CET (GMT +1)

Agenda

Preliminary agenda, more details will be added.

• • Welcome
    • By: Frans Holm, SAS
  • Data Preparation for Data Science – Ingredients for a successful Machine Learning Model
    • In the last few years In-Memory Computing, GPUs and deep learning, but also self-service analytics have been a game changer in how we analyze data. We are able to run analyses much faster and find answers to new and relevant business questions.
    • Data preparation for data science, including feature engineering also evolved and benefits from these new technologies. For our supervised machine learning models, for example, we are able to enrich our training data with features from text data, network data and images/videos.
    • This presentation illustrates the necessary ingredients to build a powerful analytics base table (ABT) that be used to run the desired analyses. You will see that Data Preparation for Data Science is much more than just coding or analytic data processing: we take a look at the business background of our analyses and discuss the analytical aspects of data quality.
    • By Gerhard Svolba, SAS – See resume below
  • Privacy in Data
    • We will show you how to mask structured and unstructured data using standard data quality functions and visual text analytics functionality.
    • By Cecily Hoffritz, SAS & Patric Hamilton, SAS
  • Natural Language Processing and Text Analytics: From ABT to Modelling
    • Unstructured data is one of the fastest growing forms of data. It has become increasingly important to be able to make use of this form of data. This presentation will focus on the subset of unstructured data that is text and will be broken into two parts.
    • We will introduce common techniques and methods used when working with this data. As well as, discuss some popular applications. This portion of the presentation will focus giving an overview on the information needed prior to working with text
    • By Ina Conrado, SAS
    • In the second portion, we will walk through an applied example focusing on car recalls
    • By Antti Heino, SAS
  • Timeseries Analysis – From ABT to modelling
    • In this section we will discuss how the data structure affects time series data. We will start from the data and discuss how to apply statistical methods given the data structure. We will do some examples on how to understand your data better and a use case on how to estimate a large range of time series at one.
    • The presentation will be divided in the following sections:
      • Data structure, theory and focus points when handling time series data
      • – By Rune Nielsen, SAS
      • Applied data exploration with time series data
      • – By Rune Nielsen, SAS
      • Estimating a large set of time series
      • – By Pasi Helenius, SAS
  •  Closing & Lottery
    • By Frans Holm, SAS

  Dr. Gerhard Svolba is an analytic solutions architect and data scientist at SAS Institute Inc. in Austria. He is involved in numerous analytic and data science projects in different business and research domains like demand forecasting, analytical CRM, risk modeling, fraud prediction, and production quality. His project experience ranges from business and technical conceptual considerations to data preparation and analytic modeling across industries. He is the author of the SAS Press books Data Preparation for Analytics Using SAS®, Data Quality for Analytics Using SAS® and “Applying Data Science: Business Case Studies Using SAS”. As a part-time lecturer he teaches data science methods at the University of Vienna and the Medical University of Vienna as well as on business schools. See also contributions on: Medium|LinkedIn and Github.

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• The purpose of the Nordic Data Science Network is to facilitate the knowledge sharing of data science in the broad interpretation of the term.

  • We will for example discuss a specific estimation technique, model governance, implementation of models, the biases in model development and much more. The approach will be applied, with a focus on understanding a specific topic rather than a specific detail, and thereby enabling the audience to work further with the introduced topics.
  • To increase the applied focus, we hope that companies will be interested in joining the network, by presenting their interesting projects to an audience of data scientist.
  • While the network naturally will have a focus on SAS software, open source software will be welcome as well, as it fits with the open source connectivity of SAS.
  
  

After the event, you will find the presentations on this side, at the end.

Bring your colleagues and join us by register here:

Denmark / Finland / Norway / Sweden

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TextAnalytics_02032021_DataScienceNetwork.zip