A continuación, te explicamos cómo puedes distinguir la minería de datos de otros campos relacionados, como el aprendizaje automático o el análisis de datos.

Data mining is a like a data science in many ways. They should learn a pattern and creating an insight. Machine learning is another way is a predictive analytics using certain algorithm. The three terms have a same side in the context of the data, so learning the workflow or pipeline how to extract data and modeling for predictive analytics can be useful. We can apply for three of them as a job.

Data mining is a critical process for extracting valuable insights from extensive datasets. It uses sophisticated algorithms to identify hidden patterns and correlations, providing actionable information that supports informed decision-making. Unlike machine learning, which is oriented towards creating predictive models, data mining focuses on uncovering and understanding existing data structures. This distinction highlights its importance in fields where understanding past data behavior is crucial for strategic planning and analysis.

Machine learning (ML) represents a significant advancement in artificial intelligence, allowing computers to autonomously learn from data and make predictions or decisions. Unlike traditional programming, ML employs algorithms to analyze and learn from data, continuously improving its models as more data becomes available. While data mining can serve as a preliminary step in the ML process, ML extends beyond by creating dynamic models that evolve and enhance their accuracy over time. This capability makes ML indispensable for applications requiring adaptive and predictive analytics.

In data analytics, it's essential to see beyond raw numbers, identify trends and patterns, and use this insight to make predictions or recommendations. For example, analyzing sales data for a product launch can reveal demographics more likely to purchase, helping to target future marketing efforts. Data mining involves extracting valuable insights from large datasets by using statistical analysis and computer programming skills. For example, it can help identify popular products among specific customer demographics and inform decisions about inventory levels, pricing, and new product development.

Data analytics is essential for transforming raw data into meaningful insights by identifying trends and answering specific questions. It encompasses a wide range of activities, including statistical analysis and data mining, to analyze and interpret data. Unlike data mining, which focuses on discovering unknown patterns, data analytics is centered on answering predefined questions and testing hypotheses. This approach makes data analytics a crucial tool for informed decision-making and strategic planning.

Distinguishing between these fields highlights their unique objectives: data mining focuses on discovering hidden patterns, machine learning emphasizes prediction and automating decision-making, and data analytics is dedicated to interpreting data to draw conclusions and support decisions. Each serves a distinct role in the data processing pipeline, collectively contributing to a comprehensive understanding of data.

A very good combination here might be use : - Google colab and Python for machine learning. - R for Data mining - Tableau for data analytics.