Program Overview

The Associate of Applied Science in Data Analytics prepares students with the quantitative, technical, business, analytical, and communication competencies required to support data-driven decision making across modern organizations. The program develops practical competence in spreadsheet analytics, relational databases, SQL, Python, statistical analysis, data preparation, visualization, business intelligence, data governance, predictive analytics, and cloud-based analytical environments.

Students learn to transform raw data into usable information by collecting, validating, cleaning, integrating, querying, analyzing, visualizing, interpreting, and communicating data using appropriate analytical methods and technologies. Extensive laboratory instruction provides experience working with realistic business datasets and producing professional reports, dashboards, analytical models, and management presentations.

The curriculum emphasizes analytical reasoning, data quality, ethical data use, privacy, documentation, reproducibility, business understanding, and responsible interpretation of evidence.

Program Learning Outcomes

Graduates of the program will be able to apply fundamental data analytics concepts, quantitative reasoning, statistical methods, and professional practices to business and organizational data. They will use spreadsheets, relational databases, SQL, and Python to acquire, organize, clean, transform, analyze, and visualize data from approved sources. Graduates will be able to conduct exploratory and statistical analysis, develop business intelligence dashboards, create analytical data models, and apply appropriate visualization and reporting techniques to support decision making. They will apply data-quality, governance, privacy, security, ethical data-use, predictive analytics, forecasting, and introductory machine learning principles in analytical environments. Graduates will be able to translate business questions into measurable analytical requirements, interpret findings while recognizing limitations and uncertainty, and communicate results through professional reports, dashboards, executive summaries, and presentations. They will also collaborate effectively with business and technical stakeholders and complete an integrated enterprise data analytics project demonstrating practical competence across the major technical and professional areas of the degree.

What You’ll Learn

Data Analytics Foundations

Students will learn fundamental data analytics concepts and professional practices, data-driven decision making, data types and sources, data quality, analytical processes, business questions, metrics, key performance indicators, analytical reporting, and interpretation.

Spreadsheet Analytics & Business Data Modeling

Students will develop practical skills in spreadsheet organization and data cleaning, formulas, functions, lookups, conditional logic, pivot tables, pivot charts, KPI calculations, scenario analysis, spreadsheet dashboards, workbook auditing, accuracy, and documentation.

Databases & SQL

Students will learn relational database concepts, including tables, relationships, keys, constraints, and normalization. They will gain practical experience with database creation and data loading, SQL filtering, sorting, aggregation, joins, advanced SQL, subqueries, common table expressions, window functions, analytical views, and data modeling.

Python for Data Analytics

Students will learn Python programming fundamentals and work with Jupyter notebooks for data analysis. The program covers data import, DataFrame manipulation, data cleaning, aggregation, descriptive analysis, visualization, functions, debugging, documentation, and reusable analytical workflows.

Statistics & Quantitative Analysis

Students will study descriptive statistics, probability, distribution analysis, sampling, confidence intervals, hypothesis testing, correlation, regression, statistical interpretation, and their applications in business and organizational decision making.

Data Preparation & Exploratory Data Analysis

Students will develop skills in data acquisition and profiling, missing-value treatment, duplicate detection, data standardization and transformation, dataset merging and validation, exploratory visualization, data dictionaries, cleaning logs, and exploratory data analysis reporting.

Data Visualization & Business Intelligence

Students will learn data connections, analytical data modeling, KPI creation, dashboard development, interactive filtering, drill-down analysis, business intelligence reporting, data storytelling, and executive dashboard development.

Data Governance, Quality, Privacy & Security

Students will learn data governance and stewardship, data quality, metadata, data classification, access controls, privacy, responsible data use, data lineage, and governance practices that support effective and responsible data management.

Business Analytics & Performance Measurement

Students will apply analytics to financial, sales, marketing, customer, operations, workforce, and supply-chain data. They will develop skills in KPI development, performance measurement, variance analysis, business recommendations, and management reporting.

Predictive & Advanced Analytics

Students will learn predictive analytics and machine learning fundamentals, including regression and classification models, decision trees, model evaluation, time-series analysis, forecasting, segmentation, scenario analysis, and decision support.

Data Warehousing, ETL & Cloud Analytics

Students will learn data warehouses, data marts, dimensional modeling, star schemas, ETL/ELT processes, data pipelines, cloud storage, cloud-based analytics, pipeline monitoring, and analytical architecture.

Analytics Automation & Professional Data Practice

Students will develop practical skills in Python and SQL automation, data-validation scripts, workflow documentation, version-control exercises, quality-assurance review, analytics project planning, and professional portfolio development.

Enterprise Data Analytics Capstone

Students complete an end-to-end enterprise analytics project involving requirements analysis, data acquisition, data preparation, SQL and Python analysis, statistical analysis, exploratory analysis, predictive or forecasting analysis, KPI development, business intelligence dashboards, recommendations, technical documentation, executive reporting, portfolio development, and final presentation.

Career Opportunities

Graduates may pursue entry-level opportunities in data analytics, business intelligence, business analytics, reporting, data support, and related analytical environments. The program prepares students to apply spreadsheet, SQL, Python, statistical, visualization, business intelligence, predictive analytics, data governance, and cloud analytics skills within organizational settings. Graduates may also use these competencies for continued professional development, relevant industry certification preparation, and further education in data analytics, business analytics, information systems, data science, artificial intelligence, and related disciplines. Actual job titles, responsibilities, and employment requirements may vary depending on the employer, industry, and work setting.

Admission Requirements

Applicants should possess a High School Diploma or equivalent or meet the applicable admission requirements for associate-degree-level study.

Appropriate college-level reading and writing readiness and mathematics readiness are required as specified by the applicable courses.

International applicants may be required to submit additional documentation in accordance with institutional and regulatory requirements.

View Admission Requirements

Tuition & Study Options

Program Duration

2 Years

Study Modes

• Live Online
• On-Campus
• Hybrid Learning

Credential Awarded

Professional Diploma with Official Academic Transcript upon successful completion.

Tuition

Contact the Admissions Office for current tuition, flexible payment plans, scholarships, and available financing options.