Data (DATA)

DATA 510  Introduction to Data Analytics  (4 Credits)  

Data analytics is defined as a scientific process that produces actionable insights. Students will be introduced to the concepts of data analysis, what the role of a data analyst will do, and the tools that are used to perform daily functions. This course will cover data analytics and data governance where students will learn about the fundamentals of data gathering, data mining, and how the decision-making process can be affected. This course also addresses the skills that are required to effectively communicate data to co-workers, leadership, and stakeholders.

Prerequisite(s): Excel proficiency is expected prior to enrollment in this course. Students should consider completing CMPL 502 Excel if they have not completed an Excel course in transfer.

View Course Outcomes:

  1. Define data analytics, data analysis, and data governance.
  2. Identify and demonstrate different data analytics techniques which may include data mining, data wrangling, data analysis, and data transforming.
  3. Explain the difference between reporting and analysis.
  4. Recognize the value of conducting data analysis to help in the decision-making process.

DATA 520  Data Mining, Cleaning, and Visualization  (4 Credits)  

This course will cover data mining, cleaning, and visualization preparation, including what data mining is and how it pertains to data analytics. Data cleaning and preparation for data analysis will also be covered. Students will have the opportunity to learn about data visualizations, which includes data modeling, mapping data attributes to graphical attributes, and using data visualization tools.

Prerequisite(s): DATA 510 Introduction to Data Analytics

View Course Outcomes:

  1. Define data mining and how it can be used in data analytics.
  2. Identify the data that are needed for data analysis.
  3. Prepare data for analysis using tools and techniques.
  4. Present data visuals for the analysis.
  5. Recognize effective and ineffective data visualizations.

DATA 610  Data Analytics and Technologies  (4 Credits)  

Students will have the opportunity to explore contemporary systems and technologies impacting the field of data analytics, including the cloud, AI, and machine learning. This course will also explore areas of technology that provide opportunities for future professional specialization, such as emerging Big Data technologies that support the work of data analysts, and the role of Information Technology (IT).

Prerequisite(s): DATA 520 Data Mining, Cleaning, and Visualization

View Course Outcomes:

  1. Compare different tools and technologies in industry that can be used in data analysis.
  2. Identify the characteristics of datasets for various applications.
  3. Apply scaling up machine learning techniques, machine learning libraries, mathematical/ statistical tools, and associated computing techniques and technologies.
  4. Implement various ways of selecting suitable model parameters for machine learning techniques.
  5. Examine the role of IT personnel with data analysis.

DATA 620  Data Analytics in Business Intelligence  (4 Credits)  

This course will examine the role of data analysis through the lens of multiple business disciplines such as business, health care, and marketing. Students will have the opportunity to explore key areas in the analytical process, including how data are created, stored, and accessed. The course covers how businesses and organizations work with data to create environments in which analytics can drive effective and efficient decision making.

Prerequisite(s): DATA 510 Introduction to Data Analytics

View Course Outcomes:

  1. Categorize risk measurement, value, and segmentation of the population within the context of a chosen industry.
  2. Use data analytics to construct and interpret data models, predictive modeling, and prototypes needed to gain stakeholder support or achieve business objectives.
  3. Identify the limitations and challenges of reusing data within a data model.
  4. Examine the internal and external business factors, such as economics and marketing, that impact organizational operations and objectives.
  5. Discuss the different elements of data governance and their applications.

DATA 630  Advanced Data Analytics  (4 Credits)  

Students will have the opportunity to explore more advanced data analytics methods such as collaborating on hypothesis testing and performing root cause analysis and practice presenting visualizations of data analysis that highlight the insights gained from analysis. The handling of imperfect data will also be covered.

Prerequisite(s): DATA 610 Data Analytics and Technologies and DATA 620 Data Analytics in Business Intelligence

View Course Outcomes:

  1. Identify the foundations of applied probability and statistics and their relevance in day-to-day data analysis, computing needs, and data storage.
  2. Apply various data visualization techniques using real-world data sets and analyze graphs and charts.
  3. Demonstrate the use of web analytics and metrics, procuring and processing unstructured text/data, and the ability to investigate hidden patterns.
  4. Implement machine-learning algorithms, data-mining techniques, and their pertinence in real-world applications.
  5. Apply data analytics techniques, skills, and critical thinking and identify the possibilities and limitations of their applications.