Understanding the data-driven programming methodology and having a sound programming background are foundational skills for anyone interested in working with data. This course introduces students to the principles of programming and application design. In addition, students are exposed to the concepts of data structures and algorithms. Using a hands-on approach, students gain experience developing data driven software applications.

Effective data analysis requires the integration of business understanding and data understanding. In this course, learners will be introduced to a range of common business processes, workflows, and management strategies across a range of sectors (such as sales, marketing, accounting, quality improvement, product/service delivery, product development, and human resources), in addition to their associated data needs. They will develop their ability to apply this knowledge to real-world business contexts to identify and define business goals and design appropriate data projects.

The quality of a data analysis project is limited by the quality of the data used. In this course, learners locate and select relevant, high-quality data that meets the requirements and constraints of a project. Learners then extract data from different sources (a database, a website) using the appropriate techniques(SQL, web-scraping, and Application Programming Interface (API)).

Additional Performance Standards:
This course, along with Data Wrangling, make up the Data Acquisition and Wrangling competency; competency assessments in both courses must be successfully completed to be deemed competent.

In order to be successful in this course, learners must be competent in the Data Programming course outcomes.

A key role of an analyst is to present insights in a meaningful and compelling way so that stakeholders can fulfill business objectives. In this course, learners apply the principles of visual storytelling to design visualization elements, reports, and interfaces (e.g. dashboards) that meet stakeholder needs and support decision making.

Additional Performance Standards:
This course, along with Building and Presenting Data Visualizations, make up the Visualizing Data and Insights competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

This course is specifically focused towards supporting the mathematical principles required to apply the concepts of data analysis and big data analytics. Learners apply concepts such as probability, distributions, regression, topological analysis, and descriptive and inferential statistics to data-related contexts.

Data wrangling is an incredibly important step in a data analysis project. Once data has been collected, it must be prepared - reviewed, cleaned, structured, and enriched - prior to analysis. In this course learners profile a dataset, reshape the data structure, identify and clean data issues (such as missing data, duplicates, outliers, coding issues), and enrich data through augmentation, aggregation, or calculation. The result of data wrangling is a finalized dataset that is well documented and ready for analysis.

Additional Performance Standards:
This course, along with Data Acquisition, make up the Data Acquisition and Wrangling competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

The first step in exploratory data analysis (EDA) is pattern identification. In order to identify patterns within the data, learners utilize descriptive statistical methodology that is relevant and meaningful to the project goals. Learners use graphical analysis to explore relationships and correlations within the dataset. A variety of methods are used to handle missing data and outliers within the dataset. To categorize the data, learners build unsupervised learning models. Learners summarize the outcomes of their analysis from unsupervised learning models and use the outcomes to develop actionable business insights and recommend further analysis.

Additional Performance Standards:
This course, along with Data Analysis II, make up the Performing Data Analysis competency; competency assessments in both courses must be successfully completed to be deemed competent.
In order to be successful in this course, learners must be competent in the Math for Data Analytics and Data Programming course outcomes.

Using identified relationships within the data learners build simple predictive models based on regression and classification. Learners fit the data to the model, assess the model's performance and make adjustments the model's parameters. Learners summarize the outcome(s) of their analysis from predictive models and use the outcome(s) to develop actionable business insights. Learners assess further modelling opportunities and make recommendations for further data analysis.

Additional Performance Standards:
This course, along with Data Analysis, make up the Performing Data Analysis competency; competency assessments in both courses must be successfully completed to be deemed competent.

In this course, learners apply a range of techniques to realize any design vision. Learners integrate their technical and communication skills to build and refine simple and complex visualizations to optimize effectiveness. Finally, learners close off a project, ensuring that decision makers have what they need to make decisions.

Additional Performance Standards:
This course, along with Designing Effective Visualizations, make up the Visualizing Data and Insights competency; competency assessments in both courses must be successfully completed to be deemed competent.

Working alone or in a small team, students research, design, develop, and implement an applied big data analytics research project to satisfy a real organizational or community need. Students are expected to apply all of their knowledge and skills to produce a functioning prototype of their project idea.