How do you identify and scope problems for data science frameworks?

1 Define the problem

The first step is to define the problem you want to solve with data science. This means clarifying the objective, the context, the stakeholders, and the expected outcomes of your project. You can use a problem statement to summarize the problem in one or two sentences, answering the questions: What is the problem? Why is it important? Who is affected by it? How will you measure success? A clear and concise problem statement will help you communicate your goal and scope to others, and guide your next steps.

2 Explore the data

The next step is to explore the data that is relevant to your problem. This means finding, collecting, and analyzing the data sources that can help you understand and solve the problem. You can use data exploration techniques such as descriptive statistics, visualizations, and data quality checks to get a sense of the data characteristics, distribution, patterns, and potential issues. Data exploration will help you assess the availability and suitability of the data for your problem, and identify any gaps or limitations that need to be addressed.

3 Frame the question

The third step is to frame the question that you want to answer with data science. This means translating the problem statement into a specific and measurable question that can be answered with data analysis and modeling. You can use a question framework such as SMART (Specific, Measurable, Achievable, Relevant, Time-bound) to help you formulate a good question that is aligned with your objective, data, and expected outcomes. A well-framed question will help you define the scope and approach of your data science project, and evaluate your results.

4 Choose the method

The fourth step is to choose the method that you will use to answer the question with data science. This means selecting the appropriate data science techniques and tools that can help you analyze and model the data, and generate insights and solutions. You can use a method framework such as CRISP-DM (Cross-Industry Standard Process for Data Mining) to help you plan and execute the data science process, covering stages such as data preparation, data modeling, evaluation, and deployment. A suitable method will help you apply the best practices and standards of data science to your problem, and ensure the quality and validity of your results.

5 Estimate the effort

The fifth step is to estimate the effort that you will need to complete the data science project. This means estimating the time, resources, and skills that are required to implement the method and answer the question. You can use a effort framework such as COCOMO (Constructive Cost Model) to help you calculate the effort based on factors such as project size, complexity, and productivity. A realistic effort estimate will help you plan and manage your data science project, and avoid any delays or overruns.

6 Communicate the value

The sixth and final step is to communicate the value that you can deliver with data science. This means presenting and explaining your results, insights, and solutions to the stakeholders and users of your project, and demonstrating how they can help solve the problem and achieve the desired outcomes. You can use a value framework such as WIIFM (What's In It For Me) to help you tailor your communication to the needs and interests of your audience, and highlight the benefits and impact of your data science project. A clear and convincing communication will help you showcase your data science skills and value, and gain support and feedback for your project.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?