Information Technology: Data Analytics

Data analysis is the process of inspecting, cleansing, transforming and modelling data in order to support better decision-making. Data analysts work with a diverse range of data and leverage various software and tools to turn data into reports and visual representations for stakeholders.

This course introduces students to the data analyst profession, including its history, trends, roles, and responsibilities. Students gain a deep understanding of data literacy and explore the data analytics pipeline. Foundations of Data Analytics examines the importance of governance and ethics in the profession. In addition, students have the opportunity to work with foundational data analyst tools.

Every data analysis project begins with a goal, otherwise known as a question. This question addresses a business inquiry or concern, acts as a framework for an analysis project, and allows an analyst to scope and structure a plan.
 
This course teaches students how to work with stakeholders to determine a project goal by asking the right questions. Students explore statistical analysis theory in order to translate their goals into analytical questions. In addition, students learn how to create an analysis plan, establish proper workflow, and how to scope a project.

Obtaining data is the procedure of gathering evidence to answer an analytical question. During this process, an analyst may encounter various forms of data from several sources; therefore, they must know how to handle diverse datasets. In addition, analysts should know how to perform extractions in a way that ensures accurate, quality data, aligned to their project goal.

This course provides students with the knowledge required to interpret laws and regulations and comply with professional ethics and data governance. Students examine database theory and the necessary steps to perform a statistical investigation. Using R and Tableau, students will organize, format, and manipulate data to prepare for the next phase of the analysis pipeline.

Exploratory Data Analysis (EDA) is the process of using visualizations and summary statistics to gain a feel for a dataset. During this process, an analyst will populate graphs and tools in order to explore data. Leveraging visualization techniques, analysts can quickly identify outliers and look for trends and patterns within the data. In addition, this process allows an analyst to rapidly generate and test hypotheses to establish leads for the analysis phase.

This course teaches students how to explore and define the characteristics of a dataset using statistics and visualization tools. Students learn to identify potential problems that might affect the analysis and how to lay the groundwork for running models during the next phase.

Preparing and cleaning data is an essential step in the analysis pipeline. This step ensures input data is organized and error-free, allowing for a more streamlined, accurate analysis. 
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In this course, students learn to prepare for analysis by cleaning and transforming datasets. Students will learn how to handle outliers, identify and reconcile errors and validate data. In addition, students will explore data aggregation, filters, dedupes and joins. Students will advance their skills in R, Tableau, and SQL.

Analyzing data using models and tests is the most critical part of the analysis pipeline. During this phase, analysts revisit stakeholders to refine business requirements as they gather, manipulate and organize data. Analysts work to plot and visualize plot data to feed statistical tests and models.

This course teaches students how to use models in R and Tableau to examine their data. Students learn how to visualize data to gain deeper understanding and frame their data stories. In addition, students will ensure reproducible results using proper documentation methods.

Presenting findings is the procedure of summarizing and communicating the story a dataset has to tell using text, tables, and graphs. During this step, an analyst uses visualization best practices to organize and chart data into consumable outputs that guide and inform stakeholders.
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In this course, students learn how to determine proper presentation output based on their audience and data. Students explore graphic design theory to ensure intuitive visual communication. In addition, students study how to create and deploy interactive dashboards and learn business presentation skills.

This course allows students to work through the lifecycle of a data analytics project from beginning to end by applying what they learn in class. Working with faculty, students select a topic based on an area of interest. Working independently, students formulate an analysis goal and begin mapping out their project to the analysis pipeline. Using this class time, students have the opportunity to explore and gain a deeper understanding of data analytics theory, best practices, software applications and procedures.
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This course culminates with a final presentation of the full project and findings. The presentation should make use of data visualization and presentation techniques.