Prepare to confidently address the IT challenges of today—and tomorrow. Build next-level skills and forge a rewarding future in an ever-changing field.
In this specialization, you will develop the knowledge and competence to turn big data into actionable intelligence. Designed to cultivate your analytical skills, Walden’s Big Data Analytics specialization will introduce you to the intricate world of data mining, including data auditing, aggregation, validation, and reconciliation. By learning how to turn big value data into business intelligence, you will acquire the ability to drive enterprise productivity and increase operational efficiencies.
Students interested in this specialization should have knowledge of Java, SQL, and data processing/warehousing principles and applications. A student with a bachelor’s degree in Computer Science, Engineering, Finance, Math (Discrete Mathematics, Statistics) or similar field grounded in quantitative skills is required. Students who have completed a bachelor’s degree outside of those listed above and at a least one of the following certifications are also eligible:
This sequence represents the minimum time to completion. Time to completion will vary by student, depending on individual progress and credits transferred, if applicable. For a personalized estimate of your time to completion, call an enrollment specialist at 855-646-5286.
|Course Code||ITEC 6111||Course||Information Technology in the Organization||Credits||(3 sem. cr.)|
|Course Code||ITEC 6115||Course||Computer Networking and Operating Systems||Credits||(3 sem. cr.)|
|Course Code||ITEC 6145||Course||Enterprise Database Design||Credits||(3 sem. cr.)|
|Course Code||ITEC 6030||Course||Principles of Programming||Credits||(3 sem. cr.)|
Through a review of modern computer systems and the social and economic issues related to their use, students in this course are introduced to the conceptual foundations for designing, developing, and deploying large-scale management information systems. Students investigate the role of information technology in an organization—particularly the collection, storage, and distribution of information for operations, planning, and decision making.
Within this course, students can learn the concepts of computer operating systems, including the main functions, similarities, and differences. Students can explore a variety of topics, including configuration, file systems, security, administration, interfacing, multitasking, and performance analysis. In addition, they can further their understanding of computers through the study of computer networks by learning key networking concepts, components, and the design of information and communication infrastructure solutions.
In this course, students discuss the design, implementation, and operation of databases using a principal relational database management system (DBMS). Many fundamental topics are covered in this course including: data modeling using entity-relationship diagrams; data storage, manipulation, and queries using structured query language (SQL); functional dependencies, normalization concepts, data warehouse architectures, data warehouse modeling, and data analytics.
The discipline of software development demands a variety of skills. Students in this course assess the fundamental practices and principles of designing and constructing object-oriented programs. They engage in substantial hands-on practice, reinforcing algorithmic thinking, logical design, precise coding, and careful attention to quality.
|Course Code||ITEC 6655||Course||Data Warehousing and Business Intelligence||Credits||(3 sem. cr.)|
|Course Code||ITEC 6661||Course||Business Analytics and Data Mining||Credits||(3 sem. cr.)|
|Course Code||ITEC 6160||Course||Enterprise Systems Architecture||Credits||(3 sem. cr.)|
|Course Code||ITEC 6401||Course||Statistical Concepts for Big Data||Credits||(3 sem. cr.)|
|Course Code||ITEC 6675||Course||Introduction to Big Data Analytics||Credits||(3 sem. cr.)|
|Course Code||ITEC 6685||Course||Data Visualization||Credits||(3 sem. cr.)|
Students learn key approaches to the integration of enterprise-wide information to support business strategy and decision making. They cover issues in data acquisition, storage, retrieval, and analysis in this course. Topics include data warehouses; data marts; dashboards, key performance indicators, and scorecards; online analytical processing; and data visualization.
Students learn and apply techniques for inference and discovery in large data sets. Topics include statistical inference, exploratory data analysis, data mining, text mining, and machine learning for predictive modeling.
Large-scale enterprise systems often rely on architectural frameworks that define their main components as well as the interactions among these components. Students in this course survey the principal design strategies and tools for constructing the modern information system. They identify common vendor and open-source components, illustrating how these elements can create and integrate robust web- and cloud-based services and applications.
Statistical analysis supports quality management, drives decision making, enables forecasting and prediction, and provides a means for understanding many aspects of our world. Data is everywhere in today's integrated technological society, and statistical analysis provides the means to access and interpret data. Students in this course are introduced to statistics focused on working with complex data sets and analyzing big data. Students synthesize theory with practical applications to learn the fundamentals of statistical reasoning, descriptive statistics, visual data display, regression, hypothesis testing, research design, anomaly detection, and advanced analysis practices. They have the opportunity to practice using a statistical software package to solve statistical problems. Students use a publicly available big data set to formulate their own study and complete a statistical analysis.
The amount of data available to organizations to help them create a competitive advantage is growing exponentially. These data sets are so large and complex that traditional data modeling and data analysis processes are inadequate. In this course, students are guided through basic approaches to querying and exploring data using higher level tools built on top of a Hadoop Platform. Students will walk through query interfaces, environments, and the canonical situations for tools like HBASE, HIVE, Pig, as well as more open source tools like HUE.
Big Data normally refers to petabytes (1000 terabytes) or exabytes (one billion gigabytes) of unstructured data. This amount of data requires new methods to analyze, visualize, and present these data in a way that yields insight and understanding. Students in this course are introduced to elementary graphics programming, focusing primarily on two-dimensional vector graphics and the programming platforms for graphics. This infrastructure will also include lessons on the human side of visualization, studying human perception and cognition to gain a better understanding of the target of the data visualization.