Data Science for PRISMA. Christoph Euler September 20, 2017

Transcription

1 Data Science for PRISMA Christoph Euler September 20, 2017

2 About me University of Heidelberg First semesters of Physics University of Heidelberg Final semesters of Physics University of Mainz PhD student Capgemini Consulting Senior Data Scientist Process Analytics Factory Data Analyst University of Helsinki Erasmus Exchange National Astronomical Observatories, Beijing Diploma thesis 2

3 Your expectations 3

4 Content Job profile of a data scientist Success factors for your work as a data scientist Career path: General aspects 4

5 Content Job profile of a data scientist Success factors for your work as a data scientist Career path: General aspects 5

6 Large amounts of data are a key challenge in a connected world 1,820TB of data 168 Million+ s 98,000+ tweets 25 Billion connected things" ,2 Billion devices 217 New internet users 698,445 Google queries 3,5 BillionAutos 2.5 Billion users in social networks by Million WhatsApps 695,000 Status updates Source: World Economic Forum 6

7 More and more industries decide to use data as a source of truth Mobile IoT Analytics No limit to volume No limit to structure No limit to analyzing No limit to timing No limit to value Social Media Cloud 7

8 35% of German companies use big data technology. 24% plan to use them in the next years Expected benefit Analyze larger amounts of data Improve the quality of data sources 55% 57% Generate prediction models Analyze data from different sources 51% 50% Reduce access time to data 46% Accelerate decision making 31% Improve control over data flows 27% Optimize ressource usage for the environment 23% Automatize decision making 19% None 6% Other 1% Sources: Statista survey on the usage of big data technology in Germany, February 2016, CXP Etude BARC Usages et pratiques des Big Data

9 Useful definitions...what this Big Data is that people are talking about Defining properties Velocity Economic benefit Volume Variety Veracity DS* Data Analytics Identify previously unknown patterns Root cause analysis in complex situations Typical tasks Generate insights to optimize business model Business Intelligence *Data Science 9

10 The role of a data scientist Fortune teller Specialist Intuition Predictions Statistics Inventor Industry knowledge Expert knowledge Scientific way of thinking Nerd Enterpreneur Programmer Consultant Data management Code development Dashboard design Implementer Client interaction Business expertise Market domain knowledge 10

11 Content Job profile of a data scientist Success factors for your work as a data scientist Career path: General aspects 11

12 Data Science Toolbox 1. Data base 2. Extract, transform, load 3. Analytics 4. Visualization 12

13 Suggested skills Technical side Conceptual side Maths - Statistics - Linear algebra - Business Administration - Basic skills - Industry / domain knowledge Networking IT skills - Programming - Specific tools, e.g., R - Possibly server admin Soft Skills - Project management - Communication - Presenting skills - Mediation 13

14 Where to start: a quick intro into modelling strategies Supervised learning Unsupervised learning Provide algorithm with data and corresponding labels / solutions Examples: Regression, decision trees Use cases: Prediction, recommender engines Pro: Easy to implement and interpret Con: Substantial complexity and low speed; strong mathematical assumptions Allow the algorithm to find solutions for itself Examples: Clustering, text mining Use cases: Image and speech recognition, surveillance Pro: Applicable in complex situations and independent of data quality Con: Requires in-depth knowledge of algorithm and required data preparation 14

15 Example: Linear regression...something we do every day? Important checks Requirements concerning data quality Independence and Normality of residuals 15

16 Machine learning algorithms to dive into first Supervised learning Regression Supervised learning Trees (e.g., CART, randomforest) Unsupervised learning Clustering (e.g., k-means) Data preparation Principal Component Analysis Advanced models Boosting (e.g., xgboost)

17 Content Job profile of a data scientist Success factors for your work as a data scientist Career path: General aspects 17

18 Get to know area of work Get to know companies Define value for company Data Science for PRISMA General aspects of applying for a job Idea generation Approaching contacts Approaching companies Application Area of work Corporate culture Industry Others Caution! Be aware of peculiarities of the industry you re applying in! 18

19 Introductions / further reading 19

20 Online courses and ressources - Open Universities (e.g., Fernuniversität in Hagen) - Part-time MBA - Meetup - Magazines - Newsletters - Data Science Weekly - KDnuggets - Data Science Central - InsideBIGDATA - LinkedIn / Xing groups and feeds 20

21 Gather experience! 21

22 Thank you Normal distribution Paranormal distribution Contact me! J Epidemiol Community Health Jan; 60(1): 6J Epidemiol Community Health Jan; 60(1): 6 22