Data Mining vs Statistics
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Statistical analysis and data mining both involve analyzing data, but have different objectives. Statistical analysis aims to describe datasets, while data mining aims to model data to predict, simulate, and optimize. Statistical analysis uses established methodology and hypothesis testing on structured data, while data mining uses heuristics to uncover hidden patterns in large, complex datasets. Data science incorporates techniques from statistics, data mining, and other fields to extract meaningful knowledge from data.
![DATA SCIENCE
Data science is the study of the
generalizable extraction of knowledge
from data,[1] yet the key word is science.
[2] It incorporates varying elements and
builds on techniques and theories from
many fields, including signal processing,
mathematics, probability models, machine
learning, statistical learning, computer
programming, data engineering, pattern
recognition and learning, visualization,
uncertainty modeling, data warehousing,
and high performance computing with the
goal of extracting meaning from data and
creating data products.](https://image.slidesharecdn.com/dataminingvsstatistics-140513015515-phpapp02/85/Data-Mining-vs-Statistics-6-320.jpg)
Data Mining vs Statistics
- 2. OBJECTIVE (FOR BOTH) Both used for Data Analysis, But They are both different tools Statistical Role is to describe more or less efficient a dataset while DM is to model for predict, simulate and optimize
- 3. STATISTICS FACTS Well established, centuries old methodology of science No scope of heuristics think Use sample to generalize the conclusion about the population (hypothese testing, p-values, etc). It needs confidence level of our generalization Provide Theory first and test it using statistical tools Deal with structured data in order to solve structured problems, result are software/researcher independent, inference reflects statistical hypothesis testing Knowledge are not hidden, we are directly able to observe the knowledge. It prove our observation (hypothese) scientifically, so the community will accept our hypothese. Concern about data collection It has problem with too little data available and unable to uncover knowledge from complex (interactions) data
- 4. DATA MINING FACTS Just come recently with the availibility of large volume and complex data make generous use of heuristics think Used on population (or very large data), to find the pattern in the data Dig out the data and find some patterns, and then make theories Deal with structured data in order to solve unstructured problems, result are software/researcher dependent, inference reflects computational properties of data mining algorithm at hand.Accurate prediction is more desirable than the explanation Exploratory tool, we have no idea about the hidden knowledge of the data and it let us discover those invisible knowledge Less concerned about data collection No problem with data size and able to uncover knowledge from complex (relations) data and also difficult for direct observations
- 5. COMPARISON STATISTICS DATA MINING Confirmative Explorative Small Data Set Larga Data Set Small Number of Variable Large Number of Variable Deductive (no predictions) Inductive Numeric Data Numeric and Non-Numeric Data Clean Data Data Cleaning source from slideshare.net
- 6. DATA SCIENCE Data science is the study of the generalizable extraction of knowledge from data,[1] yet the key word is science. [2] It incorporates varying elements and builds on techniques and theories from many fields, including signal processing, mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.
- 7. CONCLUSION The availibility of large volume data set should make business and sosial science (as well as other sciences) to use DM tools Business and Sosical Science (as well as other sciences) need to use more DM tools, because of the usability of DM to model, predict and optimize phenomenon DM/KDD/Data Science are more and more utilized as standard of decision making in modern business