Tell me the price as well as the exact date
fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 1 /10 Course Description Incomplete - Preview use only Title: BIG DATA AND ANALYTICS Code: ITECH1103 School / Faculty: School of Science, Engineering and Information Technology Teaching Period: 2019/07 Author: Kylie Turville Program Level: AQF Level of Program 5 6 7 8 9 10 Level Introductory Intermediate Advanced Pre-requisites: Nil Co-requisites: Nil Exclusions: Nil Credit Points: 15.00 ASCED Code: 020303 Description of the course for handbook entry: This course is concerned with modern Business Intelligence often known as “Big Data Analytics”. This course provides fundamental concepts related to broad categories of applications, technologies, architectures and processes of gathering, storing, accessing, and analyzing operational data to provide business users with timely competitive information and knowledge to facilitate in-depth comprehension for efficient and strategic decision making. Students will develop comprehension of the challenges organisations are facing for managing “Big Data” and the technological solutions deployed for that. This course will incorporate traditional IB concepts but add elements such as predictive analytics, data mining, and operations research/management science frameworks and tools. Grade Scheme: Graded (HD, D, C, etc.) Placement Component: No Supplementary Assessment: Yes Where supplementary assessment is available a student must have failed overall in the course but gained a final mark of 45 per cent or above and submitted all major assessment tasks. fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 2 /10 Course Description Incomplete - Preview use only ITECH1103 BIG DATA AND ANALYTICS Organisation: Delivery Mode: Regular semester Staff: Type Name Room Telephone Email Lecturer Md Monir Hossain 02 9269 6915
[email protected] Timetable: Type Day Time Room Staff / Comment Lecture 1 Friday 8:30am to 10:30am 408B Md Monir Hossain Laboratory 1 Friday 10:30am to12:30pm 306 Md Monir Hossain Additional consultation time can be booked by contacting the staff member concerned directly. Learning Outcomes: Knowledge: K1. Design a relational database for a provided scenario utilising tools and techniques including ER diagrams and relation models K2. Analyze both structured (i.e RDBMS, SQL) and unstructured data process (i.e R and Hadoop); K3. Examine different Big Data Analytics techniques, tools, applications and implementation Methodologies; K4. Examine various concepts and techniques related to stream mining, real-time analytics and internet of things (IOT); K5. Debate the issues, techniques and usefulness of data, web and text mining; K6. Discuss the importance, techniques and tools for data visualization. Skills: S1. Demonstrate skills in designing and building a database application using a commercially available Database management systems S2. Analyse and visualise a real-life data sets using a cutting edge analytical tools S3. Analyze and evaluate the effectiveness of structured and non-structured data processes; S4. Investigate and critique various Big Data Analytics methods, techniques and templates; S5. Research emerging trends and future issues facing data, web and text mining; S6. Distinguish the IT governance need for Big Data, distributed systems and Hadoop. Application of knowledge and skills: A1. Communicate professionally to present a coordinated, coherent and independent exposition of knowledge and ideas in dealing with Big Data Analytics; A2. Analyze and audit Big Data Analytics implementation that incorporates modern challenges such as social and digital media; fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 3 /10 Course Description Incomplete - Preview use only ITECH1103 BIG DATA AND ANALYTICS A3. Utilize analytical tools to gather, store, clean, assess and analyze business data for operational and strategic decision making. Values and Graduate Attributes: Values: V1. Value the need for and complexity of mining, visualizing and management of data from diverse sources in various structures; V2. Appreciate the importance and benefits of Big Data Analytics techniques in today's business world. Graduate Attributes: FedUni graduate attributes statement. To have graduates with knowledge, skills and competence that enable them to stand out as critical, creative and enquiring learners who are capable, flexible and work ready, and responsible, ethical and engaged citizens. Attribute Brief Description Focus Knowledge, skills and competence Students will be given the knowledge of big data analytics tools and management techniques available today and their future trends. Medium Critical, creative and enquiring learners Students will participate in self-directed learning environment to develop their theoretical and practical expertise in the field of data analytics. Medium Capable, flexible and work ready Students will utilise data mining, visualization and management tools in big data platform currently used in industry. Medium Responsible, ethical and engaged citizens Student will learn technologies that will make small to large businesses and organizations more efficient and responsive to social needs and clients' feedback. Low Content: Scope: Topics may include: Entity Relationship Diagram Big Data concepts, applications and tools; Structured data processing such as RDBMS, SQL; Non-structured data process; Data Analytics in a Big Data, distributed systems, R over Hadoop; Data, web and text mining; fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 4 /10 Course Description Incomplete - Preview use only ITECH1103 BIG DATA AND ANALYTICS Stream mining, real time analytics and IoT (Internet of Things); Data visualization; Big Data applications. Sequence: The following is an approximate guide to the sequence of topics in this course. Week(s) Topic(s) 1 Introduction to Database Management Systems 2-4 Entity Relationship Modelling, SQL - Data Manipulation Language 5-7 Big Data Concepts, Tools and Application; Data Mining, Text and Web Mining 8-11 Hadoop and MapReduce; IoT and Big Data Analytic; Big Data Governance; DataVisualisation Learning Tasks and Assessment: This course involves lectures, tutorials and Labs . Lectures will introduce students to the topics and resources, but students will be expected to explore the issues through their own reading and to share their knowledge and ideas through research, presentations, labs tasks and participation in class discussion. The labs are expected to expose students to database management, analysis and visualisation software, where students will demonstrate their understanding both in the mid semester test and the second assignment . Learning Outcomes Assessed Assessment Task Assessment Type K1-K6, S1, S2, S3, A1, A2 Illustrate skills in the analysis and practicalapplication of Big Data Analytics technologies. Tutorials/Assignment(s) K1-K5, S3, S4, S5, A2, A3 Participate in lectures and labs/tutorials, read and summarise theoretical and practical aspects of BDA. Oral / Written Examination The following tasks will be graded. Task Released Due Weighting Mid Semester Test Week 5 In timetabled laboratory (Week 5) 10.0% Data Analytics Presentation Week 4 In timetabled laboratory (Week 10) 10.0% Data Analytics Report Week 4 Fri, May 31, 2019 - 16:00 (Week 11) 20.0% Final Exam Exam period End of exam 60.0% fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 5 /10 Course Description Incomplete - Preview use only ITECH1103 BIG DATA AND ANALYTICS TASK 1 : Mid Semester Test Learning Outcomes Assessed: K3, A3 Purpose: determine progression of the student in the course as an indication of any intervention that may be required. Requirements: review the ï¬rst FOUR weeks` topics of material in preparation for an in-class test; held during the normal lab time. Assessed by: Lecturer/Tutor Submission: End of class; Feedback: marks in fdlMarks, individual feedback as required and overall feedback in class TASK 2 : Group Presentation Learning Outcomes Assessed: K4, K5, A2, V1, V2 Purpose: The purpose of the oral presentation is to provide an opportunity for students to present the results of DATA Analysis and to share this knowledge while practising their verbal communication skills. Requirements: Present findings from selected literature and data analysis held during the normal lab and tutorial time. Assessed by: Lecturer/Tutor Submission: Week 10 Feedback: marks in fdlMarks, individual feedback as required and overall feedback in class. TASK 3 : Data Analytic Report Learning Outcomes Assessed: K3, S2, A1, V1, V2 Purpose: The purpose of this task is to provide students with practical experience in working in teams to write a DATA ANALYTIC report to provide useful insights, pattern and trends in the chosen/given datasets. Requirements: review the labs and tutorial material to submit tutorials and labs questions; held during the normal lab and tutorial time. Assessed by: Lecturer/Tutor Submission via moodle: Week 11 Feedback: marks in fdlMarks, individual feedback as required and overall feedback in class or via Moodle. TASK 4: Final Exam Learning Outcomes Assessed: K3, K4, K5, S3, S4, S5, A2 Purpose: To determine whether students satisfactorily comprehend the content of the course and are able to apply the knowledge gained in diverse contexts. Written examination assessed by: Lecturer/Tutor; End of semester fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 6 /10 Course Description Incomplete - Preview use only ITECH1103 BIG DATA AND ANALYTICS Recommended time per learning activity: Students should be aware that a course’s class time is only a small component of their expected learning activities. Students are expected to spend approximately 150 hours (300 hours if 30 credit points) studying this course in order to have a reasonable opportunity to satisfactorily meet the learning outcomes. The following table is a suggested breakdown of this time on the learning activities and represents the recommended minimum for each of these activities. Learning Activity Description Hours Lecture 2 hours per week during teaching semesterof 12 weeks 24 Tutorial/Laboratory 2 hours per week during teaching semesterof 12 weeks 24 Reading and reviewing resources 1 hour per week during teaching semester of 12 weeks. Reading relevant materials from Text books, Journal Article ,Conference Paper and Professional Magazine 18 Assessment completion Mid Semester preparation ,Research and writing of assignment report, including preparation for group presentations. It should be done outside of class hours. 54 Preparation for ï¬nal exam Additional time preparing for ï¬nal exam, including review of past exam papers if available.Students are expected to spend additional hours to develop knowledge and skills for the ï¬nal exam 30 Total: 150 Submission and Return of Student Work: Marks and comments to the Mid-Semester Test will be available by the weekend of week 6. Marks and comments to the Data Analytical Report and the Presentation will be available by the weekend of week 12. Marks for the lab/tut portfolio tasks will be available by the weekend of the following fortnight. Final Exam: The final exam in this course will take place in the end of term exam period. It will be a 3 hour exam and students will NOT be permitted to take in any materials. Assessment Criteria: In order to receive a passing grade in this course, students must receive an overall passing mark in the combined result of all assessment tasks. Topics Assessed : All topics covered during this course are subject to assessment. `Turnitin` Submission: fdlGrades UD_ITECH1103_2019/07_2019-03-14 14:48:43 CRICOS Provider Number: 00103D 7 /10