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Please refer to the blackboard for important dates and deadlines.)\n\nWeek 1\nAugust 26 (No class)\nTopics: Course introduction \n\nWeek 2\nSeptember 2 (Labor Day - No class)\nTopics: Overview to BI/BA\n\nWeek 3\nSeptember 9 (Monday 10-11:15am, BH 260)\nTopics: Business data management\nOnline activities: Microsoft Access \n\nWeek 4\nSeptember 16 (Monday 10-11:15am, BH 260)\nTopics: Introduction to SQL and Teradata\nOnline activities: Teradata SQL\n\nWeek 5\nSeptember 23 (Monday 10-11:15am, BH 260)\nTopics: More on Teradata SQL, Data warehousing\nOnline activities: Teradata OLAP\nHomework 1\n\nWeek 6\nSeptember 30 (Monday 10-11:15am, BH 260)\nTopics: OLAP tools\nOnline activities: Power Query\n\nWeek 7\nOctober 7 (Monday 10-11:15am, BH 260)\nTopics: Dimensional modeling\nOnline activities: Power Pivot Part 1\n\nWeek 8\nOctober 14 (Monday 10-11:15am, BH 260)\nTopics: Statistical modeling for descriptive analytics\nOnline activities: Power Pivot Part 2\nHomework 2\n \nWeek 9\nMIDTERM EXAM online test: October 21 (Monday 10-11:15am)\nMIDTERM EXAM software project: Due by Friday, October 25, 11:59pm\n\nWeek 10\nOctober 28 (Monday 10-11:15am, BH 260)\nTopics: Business reporting\nOnline activities: Power Pivot Part 3\n\nWeek 11\nNovember 4 (Monday 10-11:15am, BH 260)\nTopics: Data visualization\nOnline activities: Tableau part 1\n\nWeek 12\nNovember 11 (Memorial Day - No class)\nOnline activities: Tableau part 2\nHomework 3\n\nWeek 13\nNovember 18 (Monday 10-11:15am, BH 260)\nTopics: Data visualization\nOnline activities: Tableau part 3\n\nWeek 14\nNovember 25 (Monday 10-11:15am, BH 260)\nTopics: Data mining part 1\nThanksgiving break\n\nWeek 15\nDecember 2 (Monday 10-11:15am, BH 260)\nTopics: Data mining part 2\nOnline activities: Power BI \nHomework 4\n\nWeek 16\nDecember 9 (Monday 10-11:15am, BH 260)\nTopics: TBD\nFINAL EXAM software project due by Sunday, December 15, 11:59pm\n\nWeek 17\nFINAL EXAM online test: Tuesday, December 17, 1:00-3:00pm", "start_char_idx": 0, "end_char_idx": 2018, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "562e066e-7e64-4246-8182-f69c1abdf00e": {"__data__": {"id_": "562e066e-7e64-4246-8182-f69c1abdf00e", "embedding": null, "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {"1": {"node_id": "829cdfc3-c863-4265-855a-70e38136acf0", "node_type": "4", "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "hash": "aab5e81d4994c69cc2108aeb7b7bc29618fd67f22806f1469591d36c86f78165", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "fdf53254-96d3-4dc2-a05f-175872608808", "node_type": "1", "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24schedule.txt", "file_name": "mis340f24schedule.txt", "file_type": "text/plain", "file_size": 2112, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "hash": "7feea00a7c8c5ddee2507a0709e4561d51f044b7a74ebc0d4528e33f610c81a9", "class_name": "RelatedNodeInfo"}, "3": {"node_id": "708a31d9-4a93-47ca-b3fb-f8906448c861", "node_type": "1", "metadata": {}, "hash": "ab71629f481344ab2d840d176867d4f2fa39729424a9781198e5b1acaf167476", "class_name": "RelatedNodeInfo"}}, "hash": "4bf13ae16db9ec9e637f864ef21ecdfb7fbd6cd1c0d4f0f1fc7a8c5082c547c7", "text": "MIS 340: Applied Business Intelligence\nFall 2024, Section 1 (25491) (3 Credits)\nDepartment of Accounting and Information Systems\nNorth Dakota State University\n\nInstructor:\t\tProfessor Limin Zhang\nContact:\t\tEmail: limin.zhang@ndsu.edu\nOffice hours: Wednesday 11:30am-12:30pm, or by appointment\nOffice hour location: Barry Hall 206 \nClass Time/Place:\tMonday 10:00-11:15am, Barry Hall 260\nClass Website:\tNDSU blackboard (https://blackboard.ndus.edu/) \n\nRequired Textbook\nSharda, R., Delen, D., & Turban, E. (2023). Business Intelligence, Analytics, Data Science, and AI (5th ed.). Pearson. ISBN-13: 978-0137931286.\n\nRequired Technology\nThe following software systems will be used in the class:\n* Microsoft Excel 2021 with PowerPivot and Statistical Toolpak add-ins. You can download and install Office 365 ProPlus (free for all NDSU students) https://kb.ndsu.edu/page.php?id=100617. However, the PowerPivot add-ins are only available for Windows version of Excel.\n* Microsoft Power BI (online version is available for all NDSU students; desktop version can be downloaded from: https://powerbi.microsoft.com/en-us/downloads/ for Windows PC only).\n* Microsoft Access 2021 (only available on Windows PC)\n* Tableau (Access instruction will be provided by the instructor)\n* Teradata ViewPoint or Teradata Studio (Access instruction will be provided by the instructor)\n\nNOTE: You can use the NDSU Virtual Open Lab to access campus software remotely. Please refer to the following website for instructions: https://kb.ndsu.edu/page.php?id=114270 \n\nCourse Description\nA hands-on look at Business Intelligence as applied to managerial decision making by exploring techniques for information creation including business analytics, data visualization, scorecards, dashboards and data mining.  \n\nNDSU College of Business Learning Goals:\n1. Our students will be effective communicators\n2. Our students will demonstrate effective critical thinking and decision making\n3. Our students will be knowledgeable about key business domains\n4. Our students will show sensitivity to ethics/ethical reasoning\n5. Our students will be effective collaborators\n6. Our students will be sensitive to issues of globalization\n\nMIS 340 contributes to these goals through its student learning outcomes:\n* Students will learn conceptual frameworks to understand the use of BI/DA in organizational decision-making settings.\n* Students will learn how to design data warehouses and data marts and use the extract, transform, load (ETL) process to populate the data warehouse with data from organizational transaction processing systems.\n* Students will be able to define data mining and understand the different methodologies associated with data mining.\n* Students will learn advanced software application hands-on skills on how to apply leading analytics tools (e.g., Teradata, Power Pivot, Tableau) to develop BI solutions.\n\nCourse Administration\nThis course is offered in a blended format with approximately 50% lecture sessions and 50% online offerings (see class schedule for details.) During both lecture and online sessions, a number of quizzes, video tutorials, hands-on exercises, reading assignment, and other activities will be given to improve the understanding of course material and encourage attendance. \n\nAssignments\nAll assignments and projects are due at the time and date specified in the instructions. 10% of the total possible points will be deducted each day for late submissions. Late submissions beyond 5 days (including weekends) will not be accepted. \n\nAttendance & Class Activities\nTo improve the understanding of course material and encourage class attendance, a number of class exercises, reading assignments, quizzes, and online activities will be given during the semester. There is no late submission or make-up for missed class activities except for excused absences supported by documentation.\n\nYou are a critical element in class sessions, which will usually be interactive. All students are encouraged to ask questions to clarify and expand the presented material. Each class will have important information, and it is your responsibility to get this information if you are unable to attend the class. There will be online videos materials and hands-on practice designed to supplement the textbooks. \n\nExams\nThe exams will cover all the topics that are covered in the class. Each exam may consist of two parts: (1) a closed-book online test and (2) a take-home software project. There will be no make-up exams, except for emergency situations or medical reasons supported by medical or other appropriate documentation. In case of emergency, please call or email the instructor as soon as possible.", "start_char_idx": 0, "end_char_idx": 4692, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}, "708a31d9-4a93-47ca-b3fb-f8906448c861": {"__data__": {"id_": "708a31d9-4a93-47ca-b3fb-f8906448c861", "embedding": null, "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "excluded_embed_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "excluded_llm_metadata_keys": ["file_name", "file_type", "file_size", "creation_date", "last_modified_date", "last_accessed_date"], "relationships": {"1": {"node_id": "829cdfc3-c863-4265-855a-70e38136acf0", "node_type": "4", "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "hash": "aab5e81d4994c69cc2108aeb7b7bc29618fd67f22806f1469591d36c86f78165", "class_name": "RelatedNodeInfo"}, "2": {"node_id": "562e066e-7e64-4246-8182-f69c1abdf00e", "node_type": "1", "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}, "hash": "4bf13ae16db9ec9e637f864ef21ecdfb7fbd6cd1c0d4f0f1fc7a8c5082c547c7", "class_name": "RelatedNodeInfo"}}, "hash": "ab71629f481344ab2d840d176867d4f2fa39729424a9781198e5b1acaf167476", "text": "There is no late submission or make-up for missed class activities except for excused absences supported by documentation.\n\nYou are a critical element in class sessions, which will usually be interactive. All students are encouraged to ask questions to clarify and expand the presented material. Each class will have important information, and it is your responsibility to get this information if you are unable to attend the class. There will be online videos materials and hands-on practice designed to supplement the textbooks. \n\nExams\nThe exams will cover all the topics that are covered in the class. Each exam may consist of two parts: (1) a closed-book online test and (2) a take-home software project. There will be no make-up exams, except for emergency situations or medical reasons supported by medical or other appropriate documentation. In case of emergency, please call or email the instructor as soon as possible. \n\nGrading Policy\nThe course grade is composed of the following components and weights:\n\nEvaluation Items\n- Attendance, Quizzes & Class Activities: 150 Points\n- Assignments (4 x 50 points): 200 Points\n- Projects (2 x 100 points): 200 Points\n- Exams (2 x 150 points): 300 Points\nTotal: 850 points\n\nGrade Percentage Points\nA 90-100%\t765-850\nB 80-89.99%\t680-764\nC 70-79.99%\t595-679\nD 60-69.99%\t510-594\nE < 60%\t\t< 510\n\nDisputes with grading\nAll grade appeals should be made to the instructor in writing via email within 3 days of receiving back the graded assignment.\n\nAcademic Honesty Statement\nThe academic community is operated on the basis of honesty, integrity, and fair play. NDSU Policy 335: Code of Academic Responsibility and Conduct applies to cases in which cheating, plagiarism, or other academic misconduct have occurred in an instructional context. Students found guilty of academic misconduct are subject to penalties, up to and possibly including suspension and/or expulsion. Student academic misconduct records are maintained by the Office of Registration and Records. Informational resources about academic honesty for students and instructional staff members can be found at www.ndsu.edu/academichonesty.\n\nAmericans with Disabilities Act for Students with Special Needs\nAny students with disabilities or other special needs, who need special accommodations in this course, are invited to share these concerns or requests with the instructor and contact the Disability Services Office (www.ndsu.edu/disabilityservices) as soon as possible.\n\nFamily Educational Rights and Privacy Act (FERPA) Statement\nYour personally identifiable information and educational records as they relate to this course are subject to FERPA.\n\nThis syllabus is intended to give students guidance in what may be covered during the semester and will be followed as closely as possible. However, the professor reserves the right to modify, supplement, and make changes to any part of this syllabus as needed.", "start_char_idx": 3764, "end_char_idx": 6686, "text_template": "{metadata_str}\n\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\n", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"33b11659-042d-47b5-a8f2-f69fdcdf04d0": {"node_ids": ["fdf53254-96d3-4dc2-a05f-175872608808"], "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24schedule.txt", "file_name": "mis340f24schedule.txt", "file_type": "text/plain", "file_size": 2112, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}}, "829cdfc3-c863-4265-855a-70e38136acf0": {"node_ids": ["562e066e-7e64-4246-8182-f69c1abdf00e", "708a31d9-4a93-47ca-b3fb-f8906448c861"], "metadata": {"file_path": "C:\\Users\\Limin\\openai\\mis340\\data\\mis340f24syllabus.txt", "file_name": "mis340f24syllabus.txt", "file_type": "text/plain", "file_size": 6773, "creation_date": "2024-08-22", "last_modified_date": "2024-08-22", "last_accessed_date": "2024-08-22"}}}}