Thomas W. Miller [Thomas W. Miller] - Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science

Well, heres another nice mess youve gotten me into!

OLIVER HARDY AS OLIVER IN Sons of the Desert (1933)

As noted earlier, there have always been more data than we can use. What is new today is the ease of collecting data and the low cost of storing data. Data come from many sources. There are unstructured text data from online systems. There are pixels from sensors and cameras. There are data from mobile phones, tablets, and computers worldwide, located in space and time. Flexible, scalable, distributed systems are needed to accommodate these data.

Relational databases have a row-and-column table structure, similar to a spreadsheet. We access and manipulate these data using structured query language (SQL). Because they are transaction-oriented with enforced data integrity, relational databases provide the foundation for sales order processing and financial accounting systems.

It is easy to understand why non-relational (NoSQL) databases have received so much attention. Non-relational databases focus upon availability and scalability. They may employ key-value, column-oriented, document-oriented, or graph structures. Some are designed for online or real-time applications, where fast response times are key. Others are well suited for massive storage and off-line analysis, with map-reduce providing a key data aggregation tool.

Many firms are moving away from internally owned, centralized computing systems and toward distributed cloud-based services. Distributed hardware and software systems, including database systems, can be expanded more easily as the data management needs of organizations grow.

Doing data science means being able to gather data from the full range of database systems, relational and non-relational, commercial and open source. We employ database query and analysis tools, gathering information across distributed systems, collating information, creating contingency tables, and computing indices of relationship across variables of interest. We use information technology and database systems as far as they can take us, and then we do more, applying what we know about statistical inference and the modeling techniques of predictive analytics.

Regarding analytics, we acknowledge an unwritten code in data science. We do not select only the data we prefer. We do not change data to conform to what we would like to see or expect to see. A two of clubs that destroys the meld is part of the natural variability in the game and must be played with the other cards. We play the hand that is dealt. The hallmarks of science are an appreciation of variability, an understanding of sources of error, and a respect for data. Data science is science.

We are often asked to make a model out of a mess. Management needs answers, and the data are replete with miscoded and missing observations, outliers and values of dubious origin. We use our best judgement in preparing data for analysis, recognizing that many decisions we make are subjective and difficult to justify.

Missing data present problems in applied research because many modeling algorithms require complete data sets. With large numbers of explanatory variables, most cases have missing data on at least one of the variables. Listwise deletion of cases with missing data is not an option. Filling in missing data fields with a single value, such as the mean, median, or mode, would distort the distribution of a variable, as well as its relationship with other variables. Filling in missing data fields with values randomly selected from the data adds noise, making it more difficult to discover relationships with other variables. The method preferred by statisticians is multiple imputation.

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