NEW EPISODE #175

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“All data scientists and analysts should spend more time in the business, outside the data sets, just to see how the actual business works. Because then you have the context, and then you understand the columns you’re seeing in the data.”
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David Asboth, author of “Solve Any Data Analysis Problem” and co-host of the “Half Stack Data Science” podcast, shares practical tips for solving real-world data analysis challenges. He highlights the gap between academic training and industry demands, emphasizing the importance of understanding the business problem and maintaining a results-driven approach.

David offers practical insights on data dictionary, data modeling, data cleaning, data lake, and prediction analysis. We also explore AI’s impact on data analysis and the importance of critical thinking when leveraging AI solutions. Tune in to level up your skills and become an indispensable, results-driven data analyst.

Listen out for:

• Half Stack Data Science Podcast
• Real-World Data Analysis Gaps
• Understanding the Business/Problem
• Result-Driven Data Analysis
• Data Dictionary
• Data Modeling
• Data Cleaning
• Data Lake
• Prediction Analysis
• The Impact of AI on Data Analysis
• Importance of Critical Thinking
• Common Tasks Solved by AI
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Show notes & transcript: techleadjournal.dev/episodes/175.
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​3 Key Takeaways

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1. Data insights start with business understanding.
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   Data scientists and analysts should immerse themselves in the business beyond just working with datasets. Understanding the business context is crucial for comprehending the data and delivering relevant, valuable insights. Without this deeper understanding, data professionals may struggle to deliver impactful results, or worse, provide inaccurate insights.
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2. Data analysis without purpose is just noise.
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   Data analysis should always be driven by a clear purpose or problem to solve. It is a misconception that doing any data analysis can generate inherent value and insights. Without a well-defined goal, data initiatives are unlikely to succeed and be sustainable.
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3. Actionable insights over analysis paralysis.
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   Focus on solving the stakeholder’s problem with the simplest possible solution, even if it’s just a basic CSV or a bar chart! Prioritize the minimum viable answer to address the core business problem and avoid unnecessary complexity. Don’t get lost in the data and chase rabbit holes. Focus on delivering actionable insights through frequent iterations.
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​2 Quotes for Reflection

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​1 Tech Lead Wisdom

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