18 Most Recommended Data Science Platforms To Learn Python and SQL

 

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You’ve heard of tax havens. After Brexit, the UK could become a ‘data haven’. Carissa Véliz in The Guardian, Sat 17 Oct 2020 09.00 BSTLast modified on Sat 17 Oct 2020 14.32 BST

The United Kingdom is at a crossroads. On the verge of Brexit, it has to decide where it stands in relation to privacy: will it loosen data protection regulation, moving more towards China’s model, or will it guarantee its citizens’ right to privacy, moving more towards a Californian approach and securing a data adequacy agreement with the EU? It would be a mistake to choose the former.

Last month, the UK published its national data strategy. Oliver Dowden, the digital secretary, wrote that under the UK’s strategy, “Data and data use are seen as opportunities to be embraced, rather than threats against which to be guarded.” No one doubts there are welcome opportunities in data, but to overly focus on the potential benefits of data and neglect the threats that the collection and use of personal data entail would be unwise.

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Top 5 Mistakes of Greenhorn Data Scientists. Jun 30, 2018·6 min read

This article examines 5 common mistakes of early Data Scientists. The list was assembled together with Dr. Sébastien Foucaud, who has >20 years of experience in mentoring and leading young Data Scientists in both academia and industry. This post aims to help you better prepare for your work in real-life.

Top 5 mistakes:

1. Enter “Generation Kaggle”
2. Neural Networks are the cure to everything

3. Machine Learning is the Product

4. Confuse Causation with Correlation

5. Optimize the wrong metrics

 

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H&M kažnjen sa 35,3 miliona evra zbog monitoringa zaposlenih. Tekst je prvobitno objavljen na sajtu Advokatske kancelarije Žunić.

Nezakonito postupanje drugog najvećeg proizvođača odeće u svetu otkriveno je nakon što su u oktobru 2019. godine prikupljeni podaci postali dostupni javnosti na nekoliko sati, usled napada na sistem H&M-a.

Rezultati istrage hamburškog Poverenika za zaštitu podataka o ličnosti i slobodu informisanja su zabrinjavajući: utvrđeno je da su tokom godina prikupljeni brojni podaci o ličnosti koji se odnose na više stotina zaposlenih u glavnom korisničkom centru H&M-a u Nirnbergu. Između ostalog, otkriveno je da je ova kompanija u svojim bazama čuvala podatke o privatnim životima svojih zaposlenih: o njihovom porodičnom statusu, religijskom opredeljenju, boravcima u inostranstvu, pa čak i o zdravstvenom stanju, istoriji bolesti i postavljenim dijagnozama. Ni detalji o avanturama na putovanjima ili simptomima bolesti po povratku iz inostranstva nisu prošli neprimećeno, već su i oni beleženi „za svaki slučaj“. Štaviše, evidencije o određenim podacima vođene su u kontinuitetu, te su s vremena na vreme ažurirane novim informacijama, na taj način prateći razvoj pojedinih životnih situacija zaposlenih lica.

Na osnovu ovih (privatnih) informacija vršene su, između ostalog, detaljne procene rada pojedinačnih zaposlenih, kao i njihovo profilisanje. Naročito je alarmantna činjenica da su prikupljeni podaci direktno uticali na odluke o pitanjima u vezi sa radnim odnosima zaposlenih, kao i da su potencijalno doveli do sprovođenja različitih mera usmerenih prema zaposlenim licima.

Hamburški Poverenik je u zvaničnom saopštenju za medije utvrdio da prikupljanje podataka o privatnom životu zaposlenih, u kombinaciji sa njihovim beleženjem i čuvanjem, predstavlja ozbiljno zadiranje u građanska prava lica na koje se podaci odnose. Prema rečima ovog organa, slučaj H&M-a je do sada neviđen primer postupanja jedne kompanije koje za posledicu ima incident u oblasti zaštite podataka o ličnosti.

 

Koja je pouka za poslodavce u Srbiji?

Nedovoljna informisanost u pogledu zaštite podataka o ličnosti na radnom mestu može dovesti do kršenja prava zaposlenih, te je neophodno na vreme se informisati, kako bi se blagovremeno izbegla sudbina koja je zadesila H&M.

Samo neke od osnovnih obaveza poslodavaca su:

1. Mapiranje podataka – podrazumeva proces identifikacije svih vrsta podataka koji se obrađuju, svrhe zbog koje se obrađuju i ustanovljavanja pravnog osnova obrade.
2. Kompletno revidiranje potencijalno problematičnih obrada podataka i prilagođavanje/uvođenje procedura.
3. Adekvatno obaveštavanje zaposlenih o podacima koje prikupljaju, načinima obrade, svrsi, pravnom osnovu, roku čuvanja podataka, licima sa kojima dele podatke i svim drugim obaveznim informacijama.
4. Periodična organizacija treninga HR osoblja i menadžmenta o usklađenosti sa Zakonom o zaštiti podataka o ličnosti.
5. Uvođenje strogih pravila kontrole pristupa podacima o ličnosti zaposlenih.
6. Jasno razdvajanje privatnih aktivnosti zaposlenih od poslovnih.
7. Poslodavci moraju imati u vidu da im pandemija ne omogućava da uvedu ograničenja prava na privatnost niti da zanemare svoje zakonske obaveze.

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Applications of Data Science. Emergence of Data Science in Every Industry. People often feel that Data Science is just related to analysing data and providing with an optimised solution. But Data Science also covers a Technology savvy domain that is Artificial Intelligence (AI) and Machine Learning (ML) and other fields like IoT and other sectors too. Artificial Intelligence and Machine Learning are all subsets of Data Science and the trends, transforming every industry to use technologies for precise business analysis and to optimize operations.

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Top Strategic Predictions for 2020 and Beyond. In a Gartner Special Report annual top 9 strategic predictions examine how the human condition is being challenged as technology creates varied and ever-changing expectations of humans. As workers and citizens see technology as an enhancement of their abilities, the human condition changes as well.

Trends:

1. Internet of Behaviors (IoB)

2. Total experience

3. Privacy-enhancing computation

4. Distributed cloud

5. Anywhere operations

6. Cybersecurity mesh

7. Intelligent composable business

8. AI engineering

9. Hyperautomation

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Unlocking value from unstructured data.

• Unstructured data—or almost 80 percent of an enterprise’s data—holds untapped value when it comes to addressing challenges and embracing opportunities.
• Extracting valuable insights from unstructured data has been difficult because it involves complex and time-consuming data analytics processes.
• However, with the help of natural language processing and machine learning, this is changing fast

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Roles in Data Science Team

Data-driven organization. You’ve likely heard this buzz phrase hundreds of times. But what does it really mean? And who are people who make data useful?

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What Is Data Science, and What Does a Data Scientist Do?

The Pillars Of Data Science Expertise:

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What Is Data Science, and What Does a Data Scientist Do?

The Pillars Of Data Science Expertise:

A Very Short History Of Data Science

The term “Data Science” has emerged only recently to specifically designate a new profession that is expected to make sense of the vast stores of big data. But making sense of data has a long history and has been discussed by scientists, statisticians, librarians, computer scientists and others for years. The following timeline traces the evolution of the term “Data Science” and its use, attempts to define it, and related terms

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