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The course emphasizes implementing mathematical concepts through Python, covering functions of several variables, matrix algebra, and introductory probability topics, including conditional probability and Bayes’ theorem. It includes practice via weekly homework, two midterm exams on Feb 23 and Apr 13, and a final exam scheduled for May 2, with detailed grading and course objectives.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/dat-300-mathematical-tools-for-data-science-course-syllabus-spring-2023/292077/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/dat-300-mathematical-tools-for-data-science-course-syllabus-spring-2023/292077.png","ImageObject",300,407,{"name":92,"@type":93},"Paura","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What topics are covered in DAT 300?","Question",{"text":113,"@type":114},"The course covers mathematical foundations for data science, including functions of several variables, matrix algebra, and introductory probability notions such as conditional probability and Bayes’ theorem. Students also learn related random variable concepts and implement simulations in Python.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How are grades determined for the course?",{"text":118,"@type":114},"Grades are based on homework (30%), Exam 1 (20%), Exam 2 (20%), and the final exam (30%). Letter grades follow a defined score-to-letter mapping table.",{"name":120,"@type":111,"acceptedAnswer":121},"When are the exams and are there make-up policies?",{"text":122,"@type":114},"Two midterms are tentatively scheduled for February 23 and April 13. The final exam is scheduled for Thursday, May 02 at 9:50–11:40 AM in the classroom, and there is no make-up final exam.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},292077,1789647997,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144},13056712833777,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","1/4/23, 9:22 PM syllabus-MathTools. utf8  \nDAT 300 \\#32520  \nMathematical Tools for Data Science Spring 2023, TTh 10:30-11:45 AM, WXLRA 103  \nInstructor: Dr. Xiaoqian Gong  \nOffice: WXLRA 645  \nOffice Hours: MW 3:00-4:00pm  \nCourse Description  \nPhone: (480) 543-9911  \nE-mail: [xgong14@asu.edu](xgong14@asu.edu)  \n([mailto:xgong14@asu.edu](mailto:xgong14@asu.edu))  \nWebsite: [https://xiaoqiangong.weebly.com/](https://xiaoqiangong.weebly.com/)  \n([https://xiaoqiangong.weebly.com/](https://xiaoqiangong.weebly.com/))  \nThis course covers the core mathematical topics that underpin data science as well as the key algorithms used for modern data analysis and their implementation using Python programming language. We discuss functions of several variables, matrix algebra and have an introduction to various notions and topics in probability.  \nTextbook  \nNo particular textbook required; notes ([https://math.la.asu.edu/~samara/MathTools-lectures/index.html) will](https://math.la.asu.edu/~samara/MathTools-lectures/index.html) will)[ ](https://math.la.asu.edu/~samara/MathTools-lectures/index.html) will)[be provided. We will use parts of material from several textbooks such as](be provided. We will use parts of material from several textbooks such as)  \n[-](-Calculus: Early Transcendentals)[Calculus: Early Transcendentals](-Calculus: Early Transcendentals), 8th edition, by James Stewart  \n-A First Course in Probability, 9th edition, by Sheldon Ross  \n-Linear Algebra with Applications , 9th edition, by Steven Leon  \n-Python for Data Analysis, 2nd edition, by Wes McKinney  \nPrerequisites  \nMAT 266 or 271 with C or better; MAT 343 with C or better; Credit is allowed for only DAT 300 or STP 494 (Mathematical Tools for Data Science) OR Visiting University Student  \nGrading Scheme  \n\n| Assignment | Points |\n| --- | --- |\n| Homework | 30% |\n| Exam 1 (Feb 23) | 20% |\n| Exam 2 (Apr 13) | 20% |\n| Final Exam | 30% |\n| Total | 100% |\n\n\n| 100-97: A+ | 89-87: B+ | 79-77: C+ | 69-60: D |\n| --- | --- | --- | --- |\n| 96-93: A | 86-83: B | 76-70: C | 0-59: E |\n| 92-90: A- | 82-80: B- |  |  |\n\nHomework  \nThere will be ≈ 7 HW assignments. You will have about a week for each homework, so please plan accordingly. Leaving the town for couple of days (such as for going to a wedding, funeral, conference, court, etc. ) is NOT an excuse for not finishing the homework on time.  \nfile:///C:/Users/janie/OneDrive/Desktop/teaching/Spring 2023/DAT 300/Syllabus/Syllabus-20230105T040637Z-001/Syllabus/syllabus-MathTools. html 1/5  \n1/4/23, 9:22 PM syllabus-MathTools. utf8  \nExams  \nThere will be two midterms/exams in the semester, tentatively scheduled for February 23 and April 13. They test students’ understanding of basic notions and concepts.  \nFinal Exam  \nThe final exam is scheduled for Thursday, May 02 at 9:50-11:40 AM , in our classroom. NOTE: There will be NO make-up final exam (neither before nor after this time), so please plan accordingly! Do not arrange your trip/flight to leave the town before the final!  \nObjectives  \nBy the end of the course students should be able to: understand the notion of gradient; understand gradient descent algorithm and Newton-Raphson’s method and be able to implement them using Python; be comfortable with matrix arithmetic; understand the notion of probability, conditional probability, knowhow to implement Bayes theorem; be familiar with basic random variables, discrete and continuous;  \nunderstand definitions of independent events and independent random variables; understand the notions of probability mass function, density and cumulative distribution function; be familiar with joint distribution; be able to perform various simulations in Python.  \nSoftware  \nWe will be using programming language Python within web app Jupyter Notebook and IDE Spyder. You can install them by installing Anaconda for free (see instructions in the course material) .  \nYou can also use computers in ASU’s libraries or computer labs:  \n∙ SoMSS computer labs","cbCairvVV4jbDOdb","https://ap.wps.com/l/cbCairvVV4jbDOdb","pdf",598925,"English","# Course Description\n## Grading Scheme\n## Homework and Exams\n## Final Exam\n## Learning Objectives\n## Software and Resources\n## Disability Accommodations\n## Cell Phones and Electronic Devices\n## Academic Honesty","[{\"question\":\"What topics are covered in DAT 300?\",\"answer\":\"The course covers mathematical foundations for data science, including functions of several variables, matrix algebra, and introductory probability notions such as conditional probability and Bayes’ theorem. Students also learn related random variable concepts and implement simulations in Python.\"},{\"question\":\"How are grades determined for the course?\",\"answer\":\"Grades are based on homework (30%), Exam 1 (20%), Exam 2 (20%), and the final exam (30%). Letter grades follow a defined score-to-letter mapping table.\"},{\"question\":\"When are the exams and are there make-up policies?\",\"answer\":\"Two midterms are tentatively scheduled for February 23 and April 13. The final exam is scheduled for Thursday, May 02 at 9:50–11:40 AM in the classroom, and there is no make-up final exam.\"}]","DAT 300 - Mathematical Tools for Data Science - Course Syllabus - Spring 2023 | PDF",13]