What do you do if you want to improve problem solving in Data Mining with domain knowledge and expertise?

To enhance problem-solving skills in Data Mining with domain knowledge and expertise, it's essential to immerse oneself in the specific industry or domain of interest. Collaborating with domain experts, continuously learning, practicing with hands-on projects, experimenting with different techniques, and seeking feedback are key strategies. By integrating domain insights with Data Mining techniques, professionals can effectively address complex challenges and deliver valuable insights tailored to real-world business needs.

Domain knowledge refers to the deep understanding of the specific field, industry, or problem area your data relates to. Here's I put some guidelines: . Framing the Right Questions: Domain experts ask focused questions that target the genuine concern, rather than surface-level issues . Identifying Relevant Data: Knowing the field helps you quickly separate useful data from noise, saving time and effort . Feature Engineering: Creating meaningful features (data transformations suitable for modeling) often depends on insights that domain experts already possess . Interpreting Results: Domain knowledge gives you the context to make sense of modeling output, avoiding misinterpretations

To Explore and preprocess the data, it is crucial to explore and work with the data: . Use descriptive statistics and visualizations to explore the data jointly with domain experts . Seek their feedback on any patterns, anomalies, or relationships they observe. This informs your understanding of what aspects of the data are likely to be important . Use domain knowledge to identify and address potential biases in the dataset that may affect your results

In a data sience context we need to use the techniques as a box of tools, that is, each problem demands a different tool. Match techniques to the problem based on what you know: . Type of Problem: Classification, regression, clustering, anomaly detection, etc., depend on your goal and the data . Data Characteristics: Size, dimensionality, data types (numerical, categorical) influence algorithm suitability . Domain-Specific Techniques: Some domains have specialized algorithms that leverage existing knowledge

The following resources can greatly benefit your learning journey: LeetCode (Python) Codewars (Python) Stratascratch (SQL) HackerRank (Python) Kaggle (Data Science) W3 Resource (pandas)