The main difference between time series analysis and other forms of analysis is that each instance is not independant of each other instance. (e.g. Sales on day $t$ may be related to sales on day $t-1$ while sales by clerk $x$ are (hopefully) independant of sales by clerk $x-1$)
The notation AR($p$) refers to the autoregressive model of order $p$. The AR($p$) model is written
$$ X_t = c + \sum_{i=1}^p \varphi_i X_{t-i}+ \varepsilon_t .\, $$where $\varphi_1, \ldots, \varphi_p$ are parameters, $c$ is a constant, and the random variable $\varepsilon_t$ is white noise.
The notation MA($q$) refers to the moving average model of order $q$:
$$ X_t = \mu + \varepsilon_t + \sum_{i=1}^q \theta_i \varepsilon_{t-i}\, $$where the $\theta_1, \ldots, \theta_q$ are the parameters of the model, $\mu$ is the expectation of $X_t$ (often assumed to equal 0), and the $\varepsilon_t$, $\varepsilon_{t-1}$,... are again, white noise error terms.
The notation ARMA($p, q$) refers to the model with $p$ autoregressive terms and $q$ moving-average terms. This model contains the AR($p$) and MA($q$) models,
$$ X_t = c + \varepsilon_t + \sum_{i=1}^p \varphi_i X_{t-i} + \sum_{i=1}^q \theta_i \varepsilon_{t-i}.\,$$*(from [Wikipedia](http://en.wikipedia.org/wiki/Autoregressive_moving_average))*
Extensions include:
Other time series methods include:
Graphs contain:
Graphs can be:
Directed graphs can be:
(A tree is an example of an directed acyclic graph or DAG)
...becomes...
| 1 | 2 | 3 | 4 | 5 | 6 | |
|---|---|---|---|---|---|---|
| 1 | 1 | 0 | 0 | 1 | 0 | |
| 2 | 1 | 0 | 1 | 0 | ||
| 3 | 1 | 0 | 0 | |||
| 4 | 1 | 1 | ||||
| 5 | 0 |
*(according to [Vincent Granville](http://www.datasciencecentral.com/profiles/blogs/six-categories-of-data-scientists))*
*(according to [Tomasz Tunguz](https://www.linkedin.com/today/post/article/20131002174328-4444200-which-of-the-five-types-of-data-science-does-your-startup-need))*
*(emphasis mine)*
*(according to [Harlan D. Harris](http://strata.oreilly.com/2013/06/theres-more-than-one-kind-of-data-scientist.html))*
*(according to [Brendan Tierney](http://www.oralytics.com/2013/03/type-i-and-type-ii-data-scientists.html))*
The Type I Data Scientist specializes in...
The Type II Data Scientist approaches the types of problems that organisations are facing in a different way. They will concentrate on the business goals and business problems that the organisation are facing. Based on these they will identify what the data scientist project will focus on, ensuring that there is a measurable outcome and business goal. The Type II Data Scientist will be a good communicator, being able to translate between the business problem and the technical environment necessary to deliver what is needed. During the project the data science team will discovery various insight about the data. The Type II Data Scientist will prioritise these and feed them back to the various business units. Some of these insights can range from something new, verifying business knowledge beliefs, areas where better data capture is needed, improvements in applications, etc.
*(according to [Steve Jones](http://service-architecture.blogspot.com/2014/03/what-are-types-of-data-scientist.html))*
Q. How do I go from Data Hacker to a Data Operator or Resident Data Scientist?