Data science isn’t just about fancy models, it’s about solving real problems. To do that, you need to ask the right questions. First, ask "What problem are you trying to solve?" Then, dig deeper with "Why is this important to your business?" and "What’s your dream outcome?" Next, ask about previous attempts and motivations. Remember, it’s all about listening more than talking and staying curious.ππ€
π€ Key Takeaways
| Points |
|---|
| Don’t just study these questions – use them |
| Stay curious and prioritize learning over selling |
| Listen more than you talk and ask meaningful follow-up questions |
Every data scientist knows that data science is more than just building fancy machine learning models. When it comes down to it, the key objective of data science is to solve problems. However, at the outset of most data science projects, a well-defined problem is rarely available. In such situations, the role of a data scientist isn’t to have all the answers, but rather to ask the right questions. In this video, we will share five questions that every data scientist should hardcode into their brain to make identifying and defining business problems second nature.
π§ Context and Importance
When starting the data science journey, many focus heavily on learning tools and technologies. However, the key lesson is to focus on problems rather than technologies. This involves gaining a deep understanding of the business problem before writing a single line of code. Asking the right questions and understanding the client’s perspective is critical since solving the wrong problem can be a waste of time and resources.
π Problem Discovery Questions
| Question | Importance |
|---|---|
| What problem are you trying to solve? | Identifying the core issue |
| Why is this important to your business? | Uncovering motivations and goals |
| What’s your dream outcome? | Understanding the client’s vision |
| What have you tried so far? | Building upon existing work |
| Why me? | Revealing people’s true intentions |
The first question every data scientist should ask is "What problem are you trying to solve?" Clients are often unclear about the problem, prompting a need to dig deeper. Second, asking "Why?" unlocks the floodgates to the client’s motivations. "What’s your dream outcome?" shifts the focus towards the client’s vision, while "What have you tried so far?" helps narrow down potential solutions or build upon existing work. Finally, "Why me?" reveals the true motive behind the client’s approach.
π Developing Intuition Through Practice
A key lesson from industry experience is to learn these questions and hardcode them into the brain. However, the goal isn’t to mindlessly ask these questions but to develop the intuition to naturally form them during conversations. Three key takeaways include not just studying but using these questions, staying curious to learn, and listening more than talking during early stage conversations.
In conclusion, mastering the art of problem discovery in data science requires more than technical expertise. Asking the right questions and understanding the client’s perspective is crucial for project success. By hardcoding these questions into their brain, data scientists can transform problem identification into second nature. To read more about this topic, check out the blog in TS Data Science. Thank you for your time and for watching! π
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