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Aryng Blogs

Data Science Consulting | Data Literacy | Data-driven Culture | Customer Analytics | Product Analytics | Fraud Analytics | Marketing Analytics | Data Science Training

A collection of blogs written by experts from Aryng on different subject matters. You can find a detailed description of basic terminologies and advanced terms in these blogs.

Startups and Analytics

Can analytics save startups from failing?

Introduction Starting a business is a risky endeavor, with many startups facing a high failure rate. According to a study by the Bureau of Labor Statistics (BLS), approximately 20% of small businesses fail within their first year, and around 50% fail ...

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Start-Up Readiness
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What is next in Data Literacy? 2023 & beyond…

For several years, executives and leaders have called data and analytics a core skill and top priority. But, the reason and expectations have dramatically changed over time. It helps to understand the past to see where enterprise data literacy is going an ...

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Data Literacy

Mythbuster: ML/AI are the Best Tools for the Future

It's frequently said that machine learning (ML) and artificial intelligence (AI) are the ultimate tools for the future. Yet, they can only solve 5–10% of business problems. That is because advanced development methods and profound insights into human natu ...

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Data Science Myths

The Mythbuster: Where there is Data, There is Chaos

The role of data in business The benefits of using data correctly Best practice for handling data The role of data in business Data is important in business, and it can help you make better decisions that lead to more success. However, like ...

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Data Science Process

Mythbuster: With Auto ML, No need for Data Scientists

There are a lot of talks these days about Auto Machine Learning but does it really powerful enough to eliminate the need for hiring Data Scientists? Some companies believe that they don't actually need data scientists at all - they can instead use auto-ML ...

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Data Science Process
AI/ML is the future of problem solving myth buster

Myth Buster: Machine Learning or AI is the Future...

Machine learning holds the key to solving many industry problems currently, but it's not the only tool for the future. As long as you have simple techniques for a problem that you're solving, machine learning is not necessary. This is important to know be ...

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Data Science Process
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Mythbuster: The best analysts are the fastest order takers

Often, an analyst is a person who extracts necessary values from the database and produces values and insights for business managers. And thereby, the faster one can do this, the better analyst they are. Sounds familiar? This post elaborates on the com ...

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Data Science Myths
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Mythbuster: Only Data Scientists Can Analyze Data

Many people think you need to be a data scientist to extract insights from it. This can’t be further from the truth. Typically, a data scientist is considered as someone who uses statistical methods, machine learning, and AI approaches to solve problems, ...

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Data Science Myths
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Categories

  • Agentic AI (2)
  • Analytics Maturity (2)
  • Campaign Analytics (3)
  • Citizen Analyst (2)
  • Customer Analytics (17)
  • Data Culture (11)
  • Data Literacy (9)
  • Data Management (3)
  • Data Science Algorithms (1)
  • Data Science Consulting (5)
  • Data Science in Fintech (2)
  • Data Science Myths (5)
  • Data Science Process (11)
  • Data Talent Hiring (1)
  • Decision Science (2)
  • Decision-Making (3)
  • Experimentation (4)
  • GenAI (4)
  • Product Analytics (7)
  • Start-Up Readiness (9)

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Archives

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  • December 2022 (1)
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  • October 2021 (2)
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  • July 2021 (4)
  • June 2021 (1)
  • May 2021 (3)
  • April 2021 (2)
  • February 2021 (1)
  • January 2021 (1)
  • December 2020 (2)
  • November 2020 (3)
  • October 2020 (1)
  • May 2020 (2)

Blog Main Pages

  • 3 Secrets to Creating Custom Data Literacy Assessments
  • Campaign Analytics: How to optimize your marketing funnel
  • Customer Analytics: Knowing & Understanding Your Customers Better
  • Text Analytics and its importance
  • Introduction to Marketing Analytics

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