Foundational Learning Outcomes

Identify a line of inquiry that requires information, including formulating questions and determining the scope of the investigation. In each of the 14 weekly projects, the scope is described at a high level at the very top of the project. Students are expected to tie their analysis on the individual weekly questions back to the stated scope. As an example of the stated scope in a project: Understanding how to use Pandas and be able to develop functions allows for a systematic approach to analyzing data. In this project, students will already be familiar with Pandas but will not (yet) know at the outset how to "develop functions" and take a "systematic approach" to solving the questions. Students are expected to comment on each question about how their "line of inquiry" and "formulation of the question" ties back to the stated scope of the project. As the seminar progresses past the first few weeks, and the students are being asked to tackle more complex problems, they need to identify which Python, SQL, R, and UNIX tools to use, and which statements and queries to run (this is "formulating the questions"), in order to get to analyze the data, derive the results, and summary the results in writing and visualizations ("determining the scope of the investigation").

Locate information using effective search strategies and relevant information sources. The Data Mine seminar progresses by increasing the complexity of the problems. The students are being asked to solve complex problems using data science tools. Students need to "locate information" within technical documentation, API documentation, online manuals, online discussions such as Stack Overflow, etc. Within these online resources, they need to determine the "relevant information sources" and apply these sources to solve the data analysis problem at hand. They need to understand the context, motivation, technical notation, nomenclature of the tools, etc. We enable students to practice this skill on every weekly project during the semester, and we provide additional resources, such as Piazza (an online discussion platform to interact with peers, teaching assistants, and the instructor), office hours throughout the week, and attending in-person or virtual seminar, for interaction directly with the instructor.

Evaluate the credibility of information. The students work together this objective in several ways. They need evaluate and analyze the "credibility of information" and data from a wide array of resources, e.g., from the federal government, from Kaggle, from online repositories and archives, etc. Each project during the semester focuses attention on a large data repository, and the students need to understand the credible data, the missing data, the inaccurate data, the data that are outliers, etc. Some of the projects for students involve data cleansing efforts, data imputation, data standardization, etc. Students also need to validate, verify, determine any missing data, understand variables, correlation, contextual information, and produce models and data visualizations from the data under consideration.

Synthesize and organize information from different sources in order to communicate. This is a key aspect of The Data Mine. In many of the student projects, they need to assimilate geospatial data, categorical and numerical data, textual data, and visualizations, in order to have a comprehensive data analysis of a system or a model. The students can use help from Piazza, office hours, the videos from the instructor and seminar live sessions to synthesize and organize the information they are learning about, in each project. The students often need to also understand many different types of tools and aspects of data analysis, sometimes in the same project, e.g., APIs, data dictionaries, functions, concepts from software engineering such as scoping, encapsulation, containerization, and concepts from spatial and temporal analysis. Synthesizing many "different sources" to derive and "communicate" the analysis is a key aspect of the projects.

Attribute original ideas of others through proper citing, referencing, paraphrasing, summarizing, and quoting. In every project, students need to use "citations to sources" (online and written), "referencing" forums and blogs where their cutting-edge concepts are "documented", proper methods of "quotation" and "citation", documentation of any teamwork, etc. The students have a template for their project submissions in which they are required to provide the proper citation of any sources, collaborations, reference materials, etc., in each and every project that they submit every week.

Recognize relevant cultural and other contextual factors when using information. Students weekly project include data and information on data about (all types of genders), political data, geospatial questions, online forums and rating schema, textual data, information about books, music, online repositories, etc. Students need to understand not only the data analysis but also the "context" in which the data is provided, the data sources, the potential usage of the analysis and its "cultural" implications, etc. Students also complete professional development, attending several professional development and outside-the-classroom events each semester. The meet with alumni, business professionals, data practitioners, data engineers, managers, scientists from national labs, etc. They attend events about the "culture related to data science", and "multicultural events". Students are required to respond in writing to every such event, and their writing is graded and incorporated into the grades for the course.

Observe ethical and legal guidelines and requirements for the use of published, confidential, and/or proprietary information. Students complete an academic integrity quiz at the beginning of each semester that sets the stage of these "ethical and legal guidelines and requirements". They have documentation about proper data handling and data management techniques. They learn about the context of data usage, including (for instance) copyrights, the difference between open source and proprietary data, different types of software licenses, the need for confidentiality with Corporate Partners projects, etc.

Assessment method for this course. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided.

Identify a line of inquiry that requires information, including formulating questions and determining the scope of the investigation. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided. Students identify which R and Python statements and queries to run (this is formulating the questions), in order to get to the results they think they are looking for (determining the scope of the investigation).

Locate information using effective search strategies and relevant information sources. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided. The students are being asked to solve complex problems using data science tools. They need to figure out what they are looking to figure out, and to do that they need to figure out what to ask.

Evaluate the credibility of information. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided. Some of the projects that students complete in the course involve data cleansing efforts including validation, verification, missing data, and modeling and students must evaluate the credibility as they move through the project.

Synthesize and organize information from different sources in order to communicate. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided. Information on how to complete the projects is learned through many sources and student utilize an experiential learning model.

Attribute original ideas of others through proper citing, referencing, paraphrasing, summarizing, and quoting. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided set and then questions about the data set that engage the student in experiential learning. At the beginning of each project there is a question regarding citations for the project.

Recognize relevant cultural and other contextual factors when using information. Students are assigned a weekly project that usually includes a data set and then questions about the data set that engage the student in experiential learning. Each week, these projects are graded by teaching assistants based on solutions provided. For professional development event assessment – students are required to attend three approved events and then write a guided summary of the event.

Observe ethical and legal guidelines and requirements for the use of published, confidential, and/or proprietary information. Students complete an academic integrity quiz at the beginning of each semester, and they are also graded on their proper documentation and usage of data throughout the semester, on every weekly project.