Data mining, data science and big data Flashcards
What is the significance of data mining in the modern world?
Due to the exponential growth of data, data mining has a very big meaning:
- It helps in extracting meaningful patterns, insights, and knowledge from vast amounts of data - which in turn is used in various sectors like healthcare, finance and marketing.
- This is all crucial for decision-making, forecasting, and improving operational efficiency.
How does machine learning contribute to the field of data analysis?
By providing methods and algorithms that can:
* Learn from data, identify patterns, and make decisions with minimal or no human intervention.
* This enhances the scope and accuracy of the data analysis, which enables more predictive and sophisticated.
In what ways do data mining and machine learning intersect with ethical considerations?
It intersects with ethical considerations in areas such as:
* privacy
* data security
* algorithmic decision-making bias
* personal/sensitive data.
Ethical practices are essential to ensure that the technologies are used responsibly, protecting individual rights and promoting fairness and transparency.
Why is the process of data mining considered a systematic one?
it is systematic because it involves several structured steps, including:
* data collection
* data cleaning
* data analysis
* data interpretation.
This systematic approach is essential to ensure the validity and reliability of the findings, and to transform raw data into actionable insights.
What role does statistics play in the field of machine learning?
By providing foundational methods for data analysis, inference, and prediction.
Statistical techniques are integral to developing and evaluating machine learning models, helping to quantify uncertainty, test hypotheses, and draw meaningful conclusions from data.
Why is computing considered a vital component in data science?
Enables the application of analyse techniques to large and diverse datasets.
Which can include not just numbers but also text, images, videos, and sensor readings.
This requires computing power is essential for managing, processing, and extracting insights from these complex data types.
What are the fundamental components of data science and why are they important?
Data science fundamentals include:
* statistical analysis
* computational technology
* data visualization
* domain expertise.
These components are crucial because they enable data scientists to extract meaningful insights from complex and diverse datasets, predict future trends, and make data-driven decisions.
How has the evolution of computing technology impacted data science?
The evolution of computing technology has significantly impacted data science by providing powerful tools for data processing, analysis, and visualization. Advanced computing capabilities have made it possible to handle large-scale datasets, perform complex simulations, and apply machine learning algorithms, thus expanding the scope and accuracy of data science.
Why is statistical literacy important for data scientists?
Statistical literacy is important for data scientists as it provides the foundation for making sense of data, understanding variability, assessing uncertainty, and making informed inferences. Knowledge of statistics allows data scientists to apply appropriate methodologies, interpret results correctly, and avoid misleading conclusions.
In what ways do data visualization techniques contribute to data science?
Data visualization techniques contribute to data science by enabling the clear and effective communication of data insights. Visualization helps in identifying patterns, trends, and outliers in data, making complex data more accessible and understandable to a wider audience, and facilitating better decision-making.
What role does domain expertise play in the field of data science?
Domain expertise plays a crucial role in data science by providing contextual understanding and relevance to the data being analyzed. It allows data scientists to ask the right questions, apply suitable analysis techniques, and interpret the findings in a meaningful way within the specific context of the domain.
What are the key characteristics that distinguish ‘big data’ from traditional data analytics?
Big data is characterized by its volume, velocity, and variety. It involves handling massive volumes of data, processing data at high speeds, and dealing with a wide range of data types and sources, including structured and unstructured data. This contrasts with traditional data analytics, which typically deal with smaller, more manageable datasets and slower data processing speeds.
How does big data impact decision-making in businesses?
Big data significantly enhances business decision-making by providing deeper insights, more accurate predictions, and the ability to process and analyze vast amounts of data in real-time. This leads to more informed, data-driven decisions, allowing businesses to respond quickly to market changes, understand customer needs better, and identify new opportunities.
What challenges do companies face when integrating big data into their operations?
Companies face several challenges when integrating big data, including technological hurdles, the need for skilled data scientists, managing the sheer volume and variety of data, ensuring data privacy and security, and adapting organizational culture to be more data-driven.
Why is there a growing demand for data scientists in the era of big data?
There is a growing demand for data scientists because they possess the unique skills needed to handle big data. They are adept at cleaning and organizing large data sets, applying advanced statistical and machine learning techniques, and translating data insights into business strategies. Their expertise is crucial in extracting meaningful information from large and complex datasets.
