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Data Science and Analysis
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| Six Types Of Analyses Every Data Scientist Should Know | |
| There are 6 types of data analyses problems | |
| 1: Descriptive | |
| 2: Exploratory | |
| 3: Inferential | |
| 4: Predictive | |
| 5: Causal | |
| 6: Mechanistic | |
| Descriptive Analysis: The discipline of quantitatively describing the main features of a collection of data. | |
| A: The description and interpretation processes are different steps | |
| B: Univariate and Bivariate are two types of statistical descriptive analyses | |
| C: Type of data set applied to: Census Data Set – a whole population | |
| Exploratory Analysis: An approach to analyzing data sets to find previously unknown relationships. | |
| A: Exploratory models are good for discovering new connections | |
| B: They are also useful for defining future studies/questions | |
| C: Exploratory analyses are usually not the definitive answer to the question at hand, but only the start | |
| D: Exploratory analyses alone should not be used for generalizing and/or predicting | |
| E: Remember: correlation does not imply causation | |
| F: Type of data set applied to: Census and Convenience Sample Data Set (typically non-uniform) – a random sample | |
| with many variables measured | |
| Inferential Analysis: Aims to test theories about the nature of the world in general (or some part of it) based on | |
| samples of "subjects" taken from the world (or some part of it). That is, use a relatively small sample of data to | |
| say something about a bigger population. | |
| A: Inference is commonly the goal of statistical models | |
| B: Inference involves estimating both the quantity you care about and your uncertainty about your estimate | |
| C: Inference depends heavily on both the population and the sampling scheme | |
| D: Type of data set applied to: Observational, Cross Sectional Time Study, and Retrospective Data Set – the right, | |
| randomly sampled population | |
| Predictive Analysis: The various types of methods that analyze current and historical facts to make predictions about | |
| future events. In essence, to use the data on some objects to predict values for another object. | |
| A: The models predicts, but it does not mean that the independent variables cause | |
| B: Accurate prediction depends heavily on measuring the right variables | |
| C: Although there are better and worse prediction models, more data and a simple model works really well | |
| D: Prediction is very hard, especially about the future references | |
| E: Type of data set applied to: Prediction Study Data Set – a training and test data set from the same population | |
| Causal Analysis: To find out what happens to one variable when you change another. | |
| A: Implementation usually requires randomized studies | |
| B: There are approaches to inferring causation in non-randomized studies | |
| C: Causal models are said to be the "gold standard" for data analysis | |
| D: Type of data set applied to: Randomized Trial Data Set – data from a randomized study | |
| Mechanistic Analysis: (most amount of effort): Understand the exact changes in variables that lead to changes in other | |
| variables for individual objects. | |
| A: Incredibly hard to infer, except in simple situations | |
| B: Usually modeled by a deterministic set of equations (physical/engineering science) | |
| C: Generally the random component of the data is measurement error | |
| D: If the equations are known but the parameters are not, they may be inferred with data analysis | |
| E: Type of data set applied to: Randomized Trial Data Set – data about all components of the system | |
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