Intellium

30 Ağustos Zafer Bayramı’mız Kutlu Olsun!

Büyük Zafer’in 102. yılında, bağımsızlığımız için mücadele eden başta Gazi Mustafa Kemal Atatürk ve silah arkadaşları olmak üzere tüm kahramanlarımızı rahmet ve minnetle anıyoruz.

Zafer Bayramı’mız kutlu olsun!

8 Mart Dünya Kadınlar Günü kutlu olsun 🌸🌟🍀🌟🌸

Happy March 8 International Women's Day 🌸🌟🍀🌟🌸

Offensive vs Defensive Data Strategy: Do You Really Need to Choose?

When talking about offensive and defensive strategies, it is often said that you cannot have it both ways. One is focused on catering to the product or business side of the organization, prioritizing and use cases to drive superior commercial or financial outcomes, and the other prioritizes preventing negative outcomes by reducing legal, accounting, and/or regulatory risk by focusing on , , and capabilities.

You’d have to choose how to prioritize and balance defense and offense as they would require differing approaches and investments. Although you cannot be fully offensive and fully defensive at the same time, you can enable offensive and defensive objectives through a number of foundational capabilities and an use of that enables domain-driven through a .

Data Products emphasize formal, fit-for-purpose controls so that a multitude of downstream consumers can use them for their respective use cases. It is specifically the defensive-oriented components of Data Products such as an accurate and transparent description of the data, its provenance, and quality considerations, that enable offensive-minded consumers to find, trust, and indeed use it. then enables domain-based teams to govern the data that they know the best, while facilitating access to the rest of the enterprise through a set of interoperability standards.

It’s quite literally defense enabling offense. What do you think?

Source: https://lnkd.in/esiQ6jTk

The future of is here, and it's projected that the machine learning engineer role will be the fastest-growing in the space through 2023.

CDAOs 👋 It's time to start creating data and analytics roles and skills that are fit for the future:

https://gtnr.it/3WY5nk8

Data Science Project Life Cycle♻️

Steps Explained 💡

Learn about the different stages involved in building a data science product or model in a project, including business problem understanding, data collection, data cleaning and processing, exploratory data analysis, model building and evaluation, model communication, model deployment, and evaluation.

Data Science vs. Data Analytics: Understanding the Differences in their requirements and skillset

Both data analysts and data scientists work with data, but they do so in different ways.

Here is a more concrete list of technical skills data analysts and data scientists need to get their jobs done. 

Yazmaya Buradan Başla!

SoftITO Yazılım-Bilişim Akademisi, sektörlerimize yazılımcı yetiştirmek için eğitime başlıyor.

🗓️ Son Başvuru: 4 Ocak 2023
📌Sınırlı Kontenjan
📌ÜCRETSİZ

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👉www.softito.com.tr

Cumhuriyetimizin kurucusu Gazi Mustafa Kemal Atatürk'ü, ebediyete irtihalinin 84. yıl dönümünde saygı ve rahmetle yad ediyoruz.

29 Ekim Cumhuriyet Bayramımızın 99. Yılı Kutlu Olsun.

 

29 Ekim Cumhuriyet Bayramımızın 99. Yılı Kutlu Olsun.