Students are introduced to popular Big Data tools such as the Hadoop framework and NoSQL databases. Students learn the basic concepts of MapReduce and Python scripting. Through various exercises, students explore widely used software for Big Data like Hive, Pig, and Spark. Prerequisite: Post-secondary education in a field related to computer science or mathematics is recommended. Alternatively, students would benefit from a basic understanding of programming concepts. Textbook Required.
Students are introduced to different scripting language tools such as SQL, NOSQL, Apache, Java and Python that support data analysis on large volumes of data. They also analyze the strengths and limitations of current tools used today. Students review and recommend which tools best support data analysis, data quality, problem solving, analysis, analytics and business decision-making for different functions and industries.
NOTE: This course has mandatory chats. No Textbook Required
Students learn to examine organizational goals and the value provided to a range of stakeholders by data analytics and big data systems and processes. They explore the concepts, and operating principles, techniques, and technology of the field. Students study how data analytics systems and skills, including planning, can inform business decision makers and meet their needs, and how predictive analytics, statistical analysis and modelling, visualization, business intelligence and decision support can play an important role in modern business success. Students also explore data management techniques including data cleansing and ETL (Extract, Transform, Load) systems.
NOTE: This course has mandatory chats. No textbook required.
Computer Requirements:Hardware; Intel I7 or AMD A10 processor or better with chipset that must support virtualization, 16 GB of RAM, 1 TB hard drive, Ethernet Network Card, Wireless Network Card, One USB 3.0 port (two preferred) Software; Windows 10 Professional Edition
Students are introduced to best practices, approaches, and tools for managing and delivering analytics, predictive analyses, ETL, and data projects. They will assess approaches around collaboration, estimation, scoping, planning, data cleaning, data migration, data quality, and risk mitigation. Students will recommend how to best communicate their assessments to business stakeholders. Textbook required.
This course provides a review of mathematical functions, algebraic equations, matrix operations and probability theory, followed by an introduction to the elementary principles of statistics. Students are also introduced to basic data manipulation and visualization using various software packages.