Remove us data-literacy
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Technical Literacy: A Practical Guide for Product Managers

The Product Coalition

Product excellence requires tech literacy. I have preached this more than once, technical literacy for product managers is essential. Today I will write about gaining technical literacy (free courses only). Here is how to level up your skills. A few assumptions: I assume you have zero technical background and start from scratch.

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Data Literacy 101

Piyanka Jain

One of these key focus areas is making the entire organization Data Literate. He believes developing Data Literacy across the organization would ultimately support the remaining three?—?customer-first We develop a year-long partnership of infusing data in the DNA of the organization. Data-driven leadership ?—?The

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What Google’s and SalesForce’s respective acquisition of Looker and Tableau Software means for…

Piyanka Jain

data generation, data capture, data storage, data access stratified by persona and data visualization. Because Data is the new currency. The organizations who are able to milk the data would remain to be the leaders in their industry. And it won’t stop there. I have myself been part of this bandwagon.

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Navigating the AI revolution in design

UX Planet

This evolution challenges us, as designers, to rethink our mindsets, skills, and tools. Tools such as ChatGPT, Gallie, and others enrich the design process, enabling us to craft more meaningful and personalised designs and user experiences. Skills for the AI-enabled designer Using AI in design extends beyond traditional skills.

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Essential Statistics for You and Me

Piyanka Jain

As we got to talking, I could see that he was a data skeptic?—?someone someone who did not believe in the power of data. Data Science is not for those who fall on the business side of the company, he said. This is why people think they cannot take advantage of Data Science as they lack such skills.

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Essential Statistics for You and Me

Piyanka Jain

As we got to talking, I could see that he was a data skeptic?—?someone someone who did not believe in the power of data. Data Science is not for those who fall on the business side of the company, he said. This is why people think they cannot take advantage of Data Science as they lack such skills.

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Deceptive Patterns in UX: How to Recognize and Avoid Them

UX Planet

These patterns can harm users and are particularly effective with vulnerable users who may have lower literacy or digital literacy levels. Designers should actively avoid and discourage the use of deceptive patterns to prioritize user interests. Is the exchange fair and appropriate when users consent to something?

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