Fireside Chat with Mirza Rahim Baig - Business Problem Solving and Data Science Career Tips

Imaad Mohamed Khan · Intermediate ·📊 Data Analytics & Business Intelligence ·5y ago

Key Takeaways

Mirza Rahim Baig discusses business problem solving and data science career tips, drawing from his experience as Lead Analyst at Zalando SE and Analytics Lead at Flipkart, covering frameworks for formulating business problems and solutions, and sharing advice for transitioning into the field of Data Science and Analytics.

Original Description

In this episode of the Fireside Chat, we have Mirza Rahim Baig, Lead Analyst at Zalando SE. He earlier worked as Analytics Lead at Flipkart. Before that he was an electronics engineer before making a transition to Data Science and Analytics. He's a published author and an educator as well. In this chat, we discuss how business problems can be identified, framed and solved and how important it is to have the right frameworks for formulating business problems and solutions. Rahim draws from his 10+ years of experience in the field and shares examples that highlight the importance of problem solving. Finally, we talk about his career transition and his advice for people entering the field of Data Science and Analytics.
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In this fireside chat, Mirza Rahim Baig shares his expertise in business problem solving and data science career tips, providing valuable insights for those transitioning into the field. He discusses the importance of problem framing and solution formulation, and offers advice for building a successful career in Data Science and Analytics. By watching this video, viewers can gain practical knowledge on how to identify, frame, and solve business problems, and learn from Rahim's experiences in the

Key Takeaways
  1. Identify business problems
  2. Frame problems using appropriate frameworks
  3. Formulate solutions
  4. Build data pipelines
  5. Deploy models
  6. Query databases
  7. Analyze data
💡 Having the right frameworks for formulating business problems and solutions is crucial for success in Data Science and Analytics.

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