**1. Spatial Models**

Spatial dependency is the co-variation of properties within geographic space: characteristics at proximal locations appear to be correlated, either positively or negatively. Spatial dependency leads to the spatial auto-correlation problem in statistics since, like temporal auto-correlation, this violates standard statistical techniques that assume independence among observations

**2. Time Series**

Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and recently wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In time domain, correlation analyses can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in frequency domain.

Additionally, time series analysis techniques may be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure.

Methods of time series analysis may also be divided into linear and non-linear, and univariate and multivariate.

**3. Survival Analysis**

Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modelling in economics, and event history analysis in sociology. Survival analysis attempts to answer questions such as: what is the proportion of a population which will survive past a certain time? Of those that survive, at what rate will they die or fail? Can multiple causes of death or failure be taken into account? How do particular circumstances or characteristics increase or decrease the probability of survival? Survival models are used by actuaries and statisticians, but also by marketers designing churn and user retention models.

Survival models are also used to predict time-to-event (time from becoming radicalized to turning into a terrorist, or time between when a gun is purchased and when it is used in a murder), or to model and predict decay.

**4. Market Segmentation**

Market segmentation, also called customer profiling, is a marketing strategy which involves dividing a broad target market into subsets of consumers,businesses, or countries that have, or are perceived to have, common needs, interests, and priorities, and then designing and implementing strategies to target them. Market segmentation strategies are generally used to identify and further define the target customers, and provide supporting data for marketing plan elements such as positioning to achieve certain marketing plan objectives. Businesses may develop product differentiation strategies, or an undifferentiated approach, involving specific products or product lines depending on the specific demand and attributes of the target segment.

**5. Recommendation Systems**

Recommender systems or recommendation systems (sometimes replacing «system» with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to an item.

**6. Association Rule Learning**

Association rule learning is a method for discovering interesting relations between variables in large databases. For example, the rule { onions, potatoes } ==> { burger } found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. In fraud detection, association rules are used to detect patterns associated with fraud. Linkage analysis is performed to identify additional fraud cases: if credit card transaction from user A was used to make a fraudulent purchase at store B, by analyzing all transactions from store B, we might find another user C with fraudulent activity.

**7. Attribution Modeling**

An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Google Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. Macro-economic models use long-term, aggregated historical data to assign, for each sale or conversion, an attribution weight to a number of channels. These models are also used for advertising mix optimization.

**8. Scoring**

Scoring model is a special kind of predictive models. Predictive models can predict defaulting on loan payments, risk of accident, client churn or attrition, or chance of buying a good. Scoring models typically use a logarithmic scale (each additional 50 points in your score reducing the risk of defaulting by 50%), and are based on logistic regression and decision trees, or a combination of multiple algorithms. Scoring technology is typically applied to transactional data, sometimes in real time (credit card fraud detection, click fraud).

**9. Predictive Modeling**

Predictive modeling leverages statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. They may also used for weather forecasting, to predict stock market prices, or to predict sales, incorporating time series or spatial models. Neural networks, linear regression, decision trees and naive Bayes are some of the techniques used for predictive modeling. They are associated with creating a training set, cross-validation, and model fitting and selection.

**10. Clustering**

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.

Unlike supervised classification (below), clustering does not use training sets. Though there are some hybrid implementations, called semi-supervised learning.

**11. Supervised Classification**

Supervised classification, also called supervised learning, is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called label, class or category). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.

**12. Extreme Value Theory**

Extreme value theory or extreme value analysis (EVA) is a branch of statistics dealing with the extreme deviations from the median of probability distributions. It seeks to assess, from a given ordered sample of a given random variable, the probability of events that are more extreme than any previously observed. For instance, floods that occur once every 10, 100, or 500 years. These models have been performing poorly recently, to predict catastrophic events, resulting in massive losses for insurance companies.

**13. Simulations**

Monte-Carlo simulations are used in many contexts: to produce high quality pseudo-random numbers, in complex settings such as multi-layer spatio-temporal hierarchical Bayesian models, to estimate parameters (see picture below), to compute statistics associated with very rare events, or even to generate large amount of data (for instance cross and auto-correlated time series) to test and compare various algorithms, especially for stock trading or in engineering.

1**4. Churn Analysis**

Customer churn analysis helps you identify and focus on higher value customers, determine what actions typically precede a lost customer or sale, and better understand what factors influence customer retention. Statistical techniques involved include survival analysis as well as Markov chains with four states: brand new customer, returning customer, inactive (lost) customer, and re-acquired customer, along with path analysis (including root cause analysis) to understand how customers move from one state to another, to maximize profit. Related topics: customer lifetime value, cost of user acquisition, user retention.

**15. Inventory management**

Inventory management is the overseeing and controlling of the ordering, storage and use of components that a company will use in the production of the items it will sell as well as the overseeing and controlling of quantities of finished products for sale. Inventory management is an operations research technique leveraging analytics (time series, seasonality, regression), especially for sales forecasting and optimum pricing — broken down per product category, market segment, and geography. It is strongly related to pricing optimization. This is not just for brick and mortar operations: inventory could mean the amount of available banner ad slots on a publisher website in the next 60 days, with estimates of how much traffic (and conversions) each banner ad slot is expected to deliver to the potential advertiser. You don’t want to over-sell or under-sell this virtual inventory, and thus you need good statistical models to predict the web traffic and conversions (to pre-sell the inventory), for each advertiser category.

**16. Optimum Bidding**

This is an example of automated, black-box, machine-to-machine communication system, sometimes working in real time, via various API’s. It is backed by statistical models. Applications include detecting and purchasing the right keywords at the right price on Google AdWords, based on expected conversion rates for millions of keywords, most of them having no historical data; keywords are categorized using an indexation algorithm (see item #18 in this article) and aggregated into buckets (categories) to get some historical data with statistical significance, at the bucket level. This is a real problem for companies such as Amazon or eBay. Or it could be used as the core algorithm for automated high frequency stock trading.

**17. Optimum Pricing**

While at first glance it sounds like an econometric problem handled with efficiency curves, or even a pure business problem, it is highly statistical in nature. Optimum pricing takes into account available and predicted inventory, production costs, prices from competitors, and profit margins. Price elasticity models are often used to determine how high prices can be boosted before reaching strong resistance. Modern systems offer prices-on-demand, in real time, for instance when booking a flight or an hotel room. User-dependent pricing — a way to further optimize pricing, offering different prices based on user segment — is a controversial issue. It is accepted in the insurance industry: bad car drivers paying more than good ones for the same coverage, or smokers / women / old people paying a different fee for healthcare insurance (this is the only price discrimination allowed by Obamacare).

**18. Indexation**

Any system based on taxonomies use an indexation algorithm, created to build and maintain the taxonomy. For instance product reviews (both products and reviewers must be categorized using an indexation algorithm, then mapped onto each other), scoring algorithms to detect the top people to follow in a specific domain, digital content management, and of course search engine technology. Indexation is a very efficient clustering algorithm, and the time used to index massive amounts of content grows linearly — that is very fast — with the size of your dataset. Basically, it relies on a few hundreds categories manually selected after parsing tons of documents, extracting billions of keywords, filtering them, producing a keyword frequency table, and focusing on top keywords.

Last but not least, an indexation algorithm can be used to automatically create an index for any document — report, article, blog, website, data repository, metadata, catalog, or book. Indeed, that’s the origin of the word indexation. Surprisingly, publishers still pay people today for indexing jobs: you can find these jobs listed on the American Society for Indexing website. This is an opportunity for data scientist entrepreneurs: offering publishers a software that does this job automatically, at a fraction of the cost.

**19. Search Engines**

Good search engine technology relies heavily on statistical modeling. Enterprise search engines help companies — for instance Amazon — sell their products, by providing users with an easy way to find them. Our own Data Science Central search is of high quality (superior to Google search), and one of the most used features on our website. The core algorithm used in any search engine is an indexation or automated tagging system. Google search could be improved as follows: (1) eliminate page rank — this algorithm has been fooled by cheaters developing link farms and other web spam, (2) add new content more frequently in your index to make search results less static, less frozen in time, (3) show more relevant articles using better user / search keyword / landing page matching algorithms which ultimately means better indexation systems, and (4) use better attribution models to show the source of an article, not copies published on LinkedIn or elsewhere. (this could be as simple as putting more weights on small publishers, and identifying the first occurrence of an article, that is, time stamp detection and management).

**20. Cross-Selling**

Usually based on collaborative filtering algorithms, the idea is to find — especially in retail — which products to sell to a client based on recent purchases or interests. For instance, trying to sell engine oil to a customer buying gasoline. In banking, a company might want to sell several services: a checking account first, then a saving account, then a business account, then a loan and so on, to a specific customer segment. The challenge is to identify the correct order in which products must be promoted, the correct customer segments, and the optimum time lag between the various promotions. Cross-selling is different from up-selling.

**21. Clinical trials**

Clinical trials are experiments done in clinical research, usually involving small data. Such prospective biomedical or behavioral research studies on human participants are designed to answer specific questions about biomedical or behavioral interventions, including new treatments and known interventions that warrant further study and comparison. Clinical trials generate data on safety and efficacy. Major concerns include how test patients are sampled (especially if they are compensated), conflict of interests in these studies, and the lack of reproducibility.

**22. Multivariate Testing**

Multivariate testing is a technique for testing an hypothesis in which multiple variables are modified. The goal is to determine which combination of variations performs the best out of all of the possible combinations. Websites and mobile apps are made of combinations of changeable elements, that are optimized using multivariate testing. This involves careful design-of-experiment, and the tiny, temporary difference (in yield or web traffic) between two versions of a webpage might not have statistical significance. While ANOVA and tests of hypotheses are used by industrial or healthcare statisticians for multivariate testing, we have developed systems that are model-free, data-driven, based on data binning and model-free confidence intervals. Stopping a multivariate testing experiment (they usually last 14 days for web page optimization) as soon as the winning combination is identified, helps save a lot of money. Note that external events — for instance an holiday or some server outage — can impact the results of multivariate testing, and need to be addressed.

**23. Queuing Systems**

A queue management system is used to control queues. Queues of people form in various situations and locations in a queue area, for instance in a call center. The process of queue formation and propagation is defined as queuing theory. Arrival of people in a queue is typically modeled using a Poisson process, with time to serve a client modeled using an exponential distribution. While being a statistical problem, it is considered to be part of operations research.

**24. Supply Chain Optimization**

Supply chain optimization is the application of processes and tools to ensure the optimal operation of a manufacturing and distribution supply chain. This includes the optimal placement of inventory within the supply chain, minimizing operating costs (including manufacturing costs, transportation costs, and distribution costs). This often involves the application of mathematical modelling techniques such as graph theory to find optimum delivery routes (and optimum locations of warehouses), the simplex algorithm, and Monte Carlo simulations.