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Back at you! Here is the fourth fantastic delivery of an amazing and fabulous selection of free and widely available business analytics learning content, which has been prepared… just for you.

Data Mining – Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets (“big data”) involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD.

Another interesting article on the subject is Raymond Li’s piece titled ‘Top 10 Data Mining Algorithms, Explained’ which is available on Gregory Piatetsky-Shapiro’s great KDNuggets site:

Decision analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision, for prescribing a recommended course of action by applying the maximum expected utility action axiom to a well-formed representation of the decision, and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker and other stakeholders.

You may also be interested in this old yet timely piece featured on the Harvard Business Review site – Decision Analysis Comes of Age by Jacob W. Ulvila and Rex V. Brown –

Engineering analytics. Engineering is the application of mathematics, empirical evidence and scientific, economic, social, and practical knowledge in order to invent, innovate, design, build, maintain, research, and improve structures, machines, tools, systems, components, materials, and processes.

The usually wonderful Google also provide a free download Introduction to Lean Analytics (in Ebook, PDF and EPUB formats)

Forecasting analytics. Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms “forecast” and “forecasting” are sometimes reserved for estimates of values at certain specific future times, while the term “prediction” is used for more general estimates, such as the number of times floods will occur over a long period.

Here’s another great piece from HBR, How to Choose the Right Forecasting Technique by John C. ChambersSatinder, K. MullickDonald and D. Smith. It’s from 1971, but it rocks the numbers.

Game analytics. Game theory is “the study of mathematical models of conflict and cooperation between intelligent rational decision-makers.” Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science, biology and poker. Originally, it addressed zero-sum games, in which one person’s gains result in losses for the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

You may also be interested in a very well written research report available from the LSE, titled Game Theory. Thanks to Theodore L. Turocy of Texas A&M and Berhard von Stengel of the London School of Economics (at the time of publishing).

Industrial analytics. Industrial engineering is a branch of engineering which deals with the optimization of complex processes or systems. Industrial engineers work to eliminate waste of time, money, materials, man-hours, machine time, energy and other resources that do not generate value. According to the Institute of Industrial Engineers, they figure out how to do things better. They engineer processes and systems that improve quality and productivity.

Datawatch Corporation have also produced a great little paper entitled ‘Industrial analytics powered by the internet of things’. You might like to check it out:

Logistics analytics. Logistics is generally the detailed organization and implementation of a complex operation. In a general business sense, logistics is the management of the flow of things between the point of origin and the point of consumption in order to meet requirements of customers or corporations. The resources managed in logistics can include physical items, such as food, materials, animals, equipment and liquids, as well as abstract items, such as time and information. The logistics of physical items usually involves the integration of information flow, material handling, production, packaging, inventory, transportation, warehousing, and often security.

Cap Gemini have also produced a great little paper on Logistics Analysis, which may be found here:

Modelling analytics. A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines (such as computer science, artificial intelligence), as well as in the social sciences (such as economics, psychology, sociology, political science). Physicists, engineers, statisticians, operations research analysts, and economists use mathematical models most extensively. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour.

Optimisation analytics. Mathematical optimisation. In mathematics, computer science and operations research, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.

Probability analytics. Probability is the measure of the likelihood that an event will occur. Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty). The higher the probability of an event, the more certain we are that the event will occur. A simple example is the tossing of a fair (unbiased) coin. Since the coin is unbiased, the two outcomes (“head” and “tail”) are equally probable; the probability of “head” equals the probability of “tail.” Since no other outcome is possible, the probability is 1/2 (or 50%) of either “head” or “tail”. In other words, the probability of “head” is 1 out of 2 outcomes and the probability of “tail” is also, 1 out of 2 outcomes.

Here’s a real book on probability from the Harvard web site. Probability Theory and Stochastic Processes with Applications, by Oliver Knill – Well worth a perusal –

Project analytics. Project management is the discipline of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria. A project is a temporary endeavor designed to produce a unique product, service or result with a defined beginning and end (usually time-constrained, and often constrained by funding or deliverables) undertaken to meet unique goals and objectives, typically to bring about beneficial change or added value.

You might also like to check out this snazzy paper from Deloitte, entitled Predictive project analytics – Will your project be successful?

Simulation analytics. Simulation is the imitation of the operation of a real-world process or system over time. The act of simulating something first requires that a model be developed; this model represents the key characteristics or behaviors/functions of the selected physical or abstract system or process. The model represents the system itself, whereas the simulation represents the operation of the system over time.

RAND put together this comprehensive paper on Modeling, Simulation, and Operations Analysis in Afghanistan and Iraq –

Social analysis. Social networks. A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Engage more with insights and predictive tools. Here is a link to some great social analytics content from Adobe:

Supply chain analytics. Supply chain management (SCM) is the management of the flow of goods and services. It includes the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption. Interconnected or interlinked networks, channels and node businesses are involved in the provision of products and services required by end customers in a supply chain. Supply chain management has been defined as the “design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand and measuring performance globally.”

This is something else that might also take your interest to the next level. Supply Chain Analytics: What is it and Why is it so Important? by Paul Myerson in The Lean Supply Chain:

I hope you find the content useful. Of course, all thanks should really go to Wikipedia and their unpaid expert contributors, as well as additional references and content providers.

I will try to get the next part of ‘ Free Business Analytics Content’ onto Linked Pulse over the next week.

Many thanks for reading.

Just a few points before closing.

Firstly, please consider joining The Big Data Contrarians, here on LinkedIn:

Secondly, keep in touch. My strategy blog is here and I can be followed on Twitter at @GoodStratTweet. Please also connect on LinkedIn if you wish. If you have any follow-up questions then leave a comment or send me an email on

Thirdly, you may be interested in other articles I have written, such as:

The Business Analytics pieces go here:

Free Business Analytics Content –Thanks to Wikipedia – Part 1:

Free Business Analytics Content –Thanks to Wikipedia – Part 2:

Free Business Analytics Content –Thanks to Wikipedia – Part 3:

Data Warehousing explained to Big Data friends –

Stuff a great data architect should know –

Big Data is not Data Warehousing –

What can data warehousing do for us now –

Looking for your most valuable data? Follow the money –