From the course: The New AI Tech Stack: AI Literacy for Tech Leaders
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Working with data: Challenges and best practices
From the course: The New AI Tech Stack: AI Literacy for Tech Leaders
Working with data: Challenges and best practices
- Data is still one of the main headaches for companies who want to do data science. Since most companies are at the beginner level, they face a lot of data challenges. Let's go through those challenges one by one and talk about real-life solutions. The first challenge is narrow or poor-quality data. The solution is prepare data for future purposes. Don't just hold data that you already have, think about the future. If you have small data, follow the options in the previous video, "Big data and small data." The next challenge is not appreciating data. Many companies are like dragons who sleep on gold. Companies, even the ones who have data, often struggle with understanding its importance, and its key value in the whole data science ecosystem. The solution is education and introducing proper data managing processes. The third challenge is about talent. Data experts are still scarce, and data awareness in companies is relatively low, which makes it hard to introduce data-related…
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Contents
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Data is the new oil3m 3s
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Sources of data and synthetic data5m 4s
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Storing data4m 42s
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Processing data with GPU and QPU (quantum processor)2m 53s
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Data lifecycle in AI projects4m 49s
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Big data vs. small data3m 30s
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Data monetization2m 3s
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Working with data: Challenges and best practices4m 52s
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Data governance and data management3m 41s
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