Evita PīlēģeLKA Latvijas Kultūras koledžas

Attīstības un projektu vadības nodaļas vadītājaevita.pilege@lkk.gov.lv

26.06.2017.

TehnoloģijasIT ML AI

DatiBig data

Datu zinātneData scientist

BiznessLēmumu

pieņemšana

Pieprasījums

(Insight Data Science White Paper)

Ietekme menedžmentā

Dati, tehnoloģijas maina to, ko

• zina,

• var,

• ir vērts darīt pašam.

(Accenture Institute for High Performance, 2016)

Ietekme industrijā: TV

Ietekme industrijā: Mode

(Simo Serra,Neuroaesthetics in Fashion, 2015)

Ietekme industrijā: Mūzika

(https://www.nextbigsound.com/)

Ietekme industrijā: Dizains

(https://prisma-ai.com/)

LKK piedāvājums

Datu dizainers (data artist)

Biznessdatu

Analīze DizainsR

• Radošums

A

• Analīze

D

• Dizains

B

• Bizness

Radoš-

ums

Biz

nes

s An

alīze

Dizains

Paldies!

Mums ir Ideja

Drosme

Plāns

Zināšanas

Priecāsimies par Kritiku

Datiem

Sadarbības partneriem

Avoti

1. Brynjolfsson, Erik, McAfee . Big Data: The Management Revolution. Harward business review, 2012. https://hbr.org/2012/10/big-data-the-management-revolution

2. Burby, Jason, Shane Atchison. Big data and creativity: What we can learn from 'House of Cards’. The next web, 2016. https://thenextweb.com/insider/2016/03/20/datainspirescreativity/#.tnw_jLuzRdCv

3. Carr, David. Giving Viewers What They Want. The New York Times, 2013. http://www.nytimes.com/2013/02/25/business/media/forhouseofcardsusingbigdatatoguaranteeitspopularity.html?_r=0

4. Girling, Rob. AI and the future of design: Will machines take your job? O'Reilly Media, 2016. https://www.oreilly.com/ideas/aiandthefutureofdesignwillmachinestakeyourjob

5. Granville, Vincent. The Rise of the Data Artist in Business. Data science central, 2013. http://www.datasciencecentral.com/profiles/blogs/theriseofthedataartistinbusiness

6. Gutierrez, Sebastian. Data Scientists at Work. Appres, 2014.

7. Hayes , Bob. Industry Differences in Data Science Roles, Skills and Project Outcomes. Business2community, 2016. http://www.business2community.com/bigdata/industrydifferencesdatasciencerolesskillsprojectoutcomes01539001#IMARCGoJDPzJVpwC.97

8. Harris,Harlan D., Sean Patrick Murphy, Marck Vaisman. Analyzing the Analyzers. O’Reilly Media, 2015.

9. Insight Data Science White Paper http://xyz.insightdatascience.com/Insight_White_Paper.pdf

10. Kolbjørnsrud, Vegard Richard Amico , Robert J. Thomas. The promise of artificial intelligence. Redefining management in the workforce of the future. Accenture Institute for High Performance, 2016.

11. Lau, Laura. Forget Being a Data Scientist and Become a Data Artist. The Tibco blog, 2013. https://www.tibco.com/blog/2013/05/04/forgetbeingadatascientistandbecomeadataartist

12. Next Big Sound. https://www.nextbigsound.com/

13. Ng, Alexis. How data driven creativity is changing advertising. The Drum, 2017

14. Prisma AI. https://prisma-ai.com

15. Simo-Serra, Edgar, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun Neuroaesthetics in Fashion: Modeling the Perception of Fashionability. Computer Vision Foundation, 2015. https://www.cs.toronto.edu/~urtasun/publications/simo_etal_cvpr15.pdf

16. This algorithm just solved fashion. Wired, 2015. http://www.wired.co.uk/article/fashionsolvingalgorithmjudgesyourinstagramphotos

17. Thread's fashion algorithm has 3.7 trillion style tips. Wired, 2015. http://www.wired.co.uk/article/algorithmthatcandressyouthread