---
esco_uri: http://data.europa.eu/esco/skill/25f0ea33-b4a2-4f31-b7b4-7d20e827b180
esco_label: "data mining"
relation: essential
onet_soc: 15-2051.00
source: stackexchange
confidence: high
qa_count: 5
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# data mining — Data Scientist

As a Data Scientist, 'data mining' isn’t just *running* algorithms; it's the entire iterative process of discovering actionable insights from data. Daily work involves translating business problems into analytical tasks – like identifying customer segments (using clustering techniques like K-Means, but being mindful of mixed data types and potentially using k-modes instead), predicting churn (often with models like Random Forests due to their robustness and lower tuning needs compared to SVMs), or detecting fraudulent transactions. It's about feature engineering (creating useful inputs for your models), cleaning messy real-world datasets, and then applying the right algorithms from a broad toolkit.

## Praxiswissen (Stack Exchange, CC-BY-SA 4.0)

**The user has a dataset with both numeric and categorical features and wants to know if converting the single categorical feature into multiple binary (0/1) variables is a valid way to use standard K-Means clustering.**

Applying K-Means directly to mixed data types is problematic because distance calculations are not meaningful for discrete, non-ordered categorical values. While one approach is to binarize the categories, more robust algorithms like k-modes or k-prototypes exist that are specifically designed to handle categorical and mixed datasets by using appropriate distance metrics (like Hamming distance). Exploring specialized clustering methods will likely yield better results than forcing a standard numeric algorithm onto non-numeric data.

*Source: [K-Means clustering for mixed numeric and categorical data](https://datascience.stackexchange.com/q/22) — answer by Tim Goodman, CC-BY-SA 4.0*

**The asker questions whether Support Vector Machines (SVMs) are still widely used, given a comment suggesting they've fallen out of favor. They seek to understand if this is due to newer algorithms or changes in computing power.**

While SVMs remain powerful classifiers with benefits like efficiency and kernel flexibility, they suffer from sensitivity to parameter tuning which requires significant effort for optimization. Newer methods, such as Random Forests, have gained popularity because of their ease of use – requiring fewer parameters to tune while still achieving strong performance. The rise of these simpler-to-implement algorithms has led to a shift in preference despite SVM's inherent strengths.

*Source: [Are Support Vector Machines still considered "state of the art" in their niche?](https://datascience.stackexchange.com/q/711) — answer by Debasis, CC-BY-SA 4.0*

**The user needs to implement anomaly detection on time-series log data within a Python environment, having previously explored commercial solutions and encountering issues with open-source R ports or Windows compatibility.**

Effective anomaly detection often relies on identifying deviations from established patterns. Simple but powerful techniques like calculating moving averages and standard deviations can highlight unusual values without requiring complex machine learning models. While more sophisticated probabilistic methods exist, starting with these basic statistical approaches provides a practical foundation for real-world implementation.

*Source: [Open source Anomaly Detection in Python](https://datascience.stackexchange.com/q/6547) — answer by Kasra Manshaei, CC-BY-SA 4.0*

**The user is asking why complex ensemble methods like XGBoost and Random Forest are necessary instead of simply using a single decision tree. They want to understand the benefits of combining multiple 'weak' learners.**

Ensemble methods improve model stability and accuracy by leveraging the power of many models. Techniques like bagging (Random Forest) reduce variance by creating diverse models from resampled data, making predictions less sensitive to small changes in the training set. Boosting techniques (like XGBoost & Gradient Boosting) further refine this by sequentially building models that correct the errors of their predecessors, reducing both bias and variance – a strategy proven effective in many real-world applications.

*Source: [Why do we need XGBoost and Random Forest?](https://datascience.stackexchange.com/q/23789) — answer by Ricardo Cruz, CC-BY-SA 4.0*

**The user is seeking methods to quantify the similarity between documents (like web pages), prioritizing semantic distance over simple lexical comparisons.**

Choosing a document distance metric involves balancing accuracy with complexity. While basic measures like cosine and Levenshtein distances are easy to implement, they lack nuanced understanding of meaning. More advanced techniques – topic modeling approaches like LDA and Pachinko Allocation, or vector-based methods like word2vec – capture semantic relationships but require more computational resources and potentially specialized implementation.

*Source: [What are some standard ways of computing the distance between documents?](https://datascience.stackexchange.com/q/678) — answer by indico, CC-BY-SA 4.0*

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
