Training Catalog

Some fundamental ideas and perspectives apply to any kind of data practice. This course introduces learners to these fundamentals by providing case studies in data analysis and machine learning, presenting, in broad strokes, the underlying concepts that support data-related work and leaving them with some thought-provoking questions on these topics to ponder and discuss. The main goal of this course is to ensure that learners start their data practitioner or data scientist journey in the right direction.

Data analysis can’t happen without data, and that data must come from somewhere. Data collection and and data processing typically take up the bulk of the time spent on any data project; how well this is accomplished carries through to the end of the project.
In this course, we survey the options for cleaning and transforming the data in order to make it more suitable for analysis.

(details coming soon)

This course is where the “rubber hits the road” and where learners start to work with data in a hands-on fashion. Its focus is on putting into practice, in a preliminary and basic way, many of the topics used in data analysis: data visualization, data exploration, data wrangling, and data modeling. This is done via basic analyses of data: summaries of variables, simple calculations related to these summaries, exploring subsets of the data, and gaining some basic insights into the information contained in datasets.

Devising well-constructed strategies to measure the properties of key objects of interest, and then combining these measures into more abstract metrics that relate to research hypotheses or organization goals is an important component of data analysis. Measures and metrics are typically very specific to a domain or problem of interest, but case studies in one domain can provide ideas and best practices that are applicable to others. In this course, in addition to considering some of the fundamental concepts relating to these topics, learners are provided with several case studies of measures and metric development.

Data and knowledge architecture concepts are critical in designing a data-driven project, and there have been a number of new and important developments on that front in recent years (e.g. NoSQL, graph databases, data lakes). The high-level goal of this course is to expose learners to many of the concepts and terms at play in today’s data world and to help them to decide which of these is most applicable to their particular data projects.

Once the dataset has been successfully explored, described, and cleaned, analysts are in a position to use it to do more than just understand what has already happened. With enough of the right kind of data, they may also be able to predict novel or future occurrences. Predictive analysis has traditionally relied on regression as its technique of choice. New types and increasing amounts of data have led to the recent development of new techniques in this area, including time series analysis techniques and machine learning techniques, such as neural networks. This course gives an overview of these prominent prediction techniques, which provides learners with a starting point from which to begin developing predictive analysis skills.

Prior to running analyses, it is crucial to gain an understanding of the dataset and how it is behaving. This process starts while cleaning the data. Increasingly sophisticated visualization strategies can play an important role at this stage, as well as at the end of the data analysis process, when insights are presented to others. This course presents simple methods to visualize data, with R/Python examples.

Prior to running analyses, it is crucial to gain an understanding of the dataset and how it is behaving. This process starts while cleaning the data. Increasingly sophisticated visualization strategies can play an important role at this stage, as well as at the end of the data analysis process, when insights are presented to others. This course presents mutlivariate methods to visualize data, with R/Python examples, and design suggestions to generate engaging visualizations.