Democratizing data science is the notion that anyone, with little to no expertise, can do data science if provided ample data and user-friendly analytics tools.Cognitive learning theory in the classroom supporting that idea, the new tool ingests datasets and generates sophisticated statistical models typically used by experts to analyze, interpret, and predict underlying patterns in data.Cognitive learning theory in the classroom

“the high-level goal is making data science accessible to people who are not experts in statistics,” says first author feras saad ’15, meng ’16, a phd student in the department of electrical engineering and computer science (EECS).Cognitive learning theory in the classroom “people have a lot of datasets that are sitting around, and our goal is to build systems that let people automatically get models they can use to ask questions about that data.”

cognitive learning theory in the classroom

Ultimately, the tool addresses a bottleneck in the data science field, says co-author vikash mansinghka ’05, meng ’09, phd ’09, a researcher in the department of brain and cognitive sciences (BCS) who runs the probabilistic computing project.Cognitive learning theory in the classroom “there is a widely recognized shortage of people who understand how to model data well,” he says. “this is a problem in governments, the nonprofit sector, and places where people can’t afford data scientists.”

cognitive learning theory in the classroom

The work uses bayesian modeling, a statistics method that continuously updates the probability of a variable as more information about that variable becomes available.Cognitive learning theory in the classroom for instance, statistician and writer nate silver uses bayesian-based models for his popular website fivethirtyeight. Leading up to a presidential election, the site’s models make an initial prediction that one of the candidates will win, based on various polls and other economic and demographic data.Cognitive learning theory in the classroom this prediction is the variable. On election day, the model uses that information, and weighs incoming votes and other data, to continuously update that probability of a candidate’s potential of winning.Cognitive learning theory in the classroom

More generally, bayesian models can be used to “forecast” — predict an unknown value in the dataset — and to uncover patterns in data and relationships between variables.Cognitive learning theory in the classroom in their work, the researchers focused on two types of datasets: time-series, a sequence of data points in chronological order; and tabular data, where each row represents an entity of interest and each column represents an attribute.Cognitive learning theory in the classroom

Time-series datasets can be used to predict, say, airline traffic in the coming months or years. A probabilistic model crunches scores of historical traffic data and produces a time-series chart with future traffic patterns plotted along the line.Cognitive learning theory in the classroom the model may also uncover periodic fluctuations correlated with other variables, such as time of year.

On the other hand, a tabular dataset used for, say, sociological research, may contain hundreds to millions of rows, each representing an individual person, with variables characterizing occupation, salary, home location, and answers to survey questions.Cognitive learning theory in the classroom probabilistic models could be used to fill in missing variables, such as predicting someone’s salary based on occupation and location, or to identify variables that inform one another, such as finding that a person’s age and occupation are predictive of their salary.Cognitive learning theory in the classroom

Statisticians view bayesian modeling as a gold standard for constructing models from data. But bayesian modeling is notoriously time-consuming and challenging.Cognitive learning theory in the classroom statisticians first take an educated guess at the necessary model structure and parameters, relying on their general knowledge of the problem and the data.Cognitive learning theory in the classroom using a statistical programming environment, such as R, a statistician then builds models, fits parameters, checks results, and repeats the process until they strike an appropriate performance tradeoff that weighs the model’s complexity and model quality.Cognitive learning theory in the classroom

The researchers’ tool automates a key part of this process. “we’re giving a software system a job you’d have a junior statistician or data scientist do,” mansinghka says.Cognitive learning theory in the classroom “the software can answer questions automatically from the data — forecasting predictions or telling you what the structure is — and it can do so rigorously, reporting quantitative measures of uncertainty.Cognitive learning theory in the classroom this level of automation and rigor is important if we’re trying to make data science more accessible.” bayesian synthesis

With the new approach, users write a line of code detailing the raw data’s location.Cognitive learning theory in the classroom the tool loads that data and creates multiple probabilistic programs that each represent a bayesian model of the data. All these automatically generated models are written in domain-specific probabilistic programming languages — coding languages developed for specific applications — that are optimized for representing bayesian models for a specific type of data.Cognitive learning theory in the classroom

The tool works using a modified version of a technique called “program synthesis,” which automatically creates computer programs given data and a language to work within.Cognitive learning theory in the classroom the technique is basically computer programming in reverse: given a set of input-output examples, program synthesis works its way backward, filling in the blanks to construct an algorithm that produces the example outputs based on the example inputs.Cognitive learning theory in the classroom

The approach is different from ordinary program synthesis in two ways. First, the tool synthesizes probabilistic programs that represent bayesian models for data, whereas traditional methods produce programs that do not model data at all.Cognitive learning theory in the classroom second, the tool synthesizes multiple programs simultaneously, while traditional methods produce only one at a time. Users can pick and choose which models best fit their application.Cognitive learning theory in the classroom

“when the system makes a model, it spits out a piece of code written in one of these domain-specific probabilistic programming languages … that people can understand and interpret,” mansinghka says.Cognitive learning theory in the classroom “for example, users can check if a time series dataset like airline traffic volume has seasonal variation just by reading the code — unlike with black-box machine learning and statistics methods, where users have to trust a model’s predictions but can’t read it to understand its structure.”

cognitive learning theory in the classroom

Probabilistic programming is an emerging field at the intersection of programming languages, artificial intelligence, and statistics. This year, MIT hosted the first international conference on probabilistic programming, which had more than 200 attendees, including leading industry players in probabilistic programming such as microsoft, uber, and google.Cognitive learning theory in the classroom

“my team at google AI builds probabilistic programming tools on top of tensorflow. Probabilistic programming is an important area for google, and time series modeling is a promising application area, with many use cases at google and for google’s users,” says ryan M.Cognitive learning theory in the classroom rifkin ’94, SM ’97, phd ’02, a google researcher who was not involved in the research. The researchers’ paper “shows how to apply probabilistic programming to solve this important problem — and reduces the effort needed to get started, by showing how the probabilistic programs can be synthesized from data, rather than written by people.”