Cs1052
.pdf
School
Boston University**We aren't endorsed by this school
Course
CAS CS CS105
Subject
Computer Science
Date
Dec 22, 2024
Pages
4
Uploaded by CoachRedPandaPerson1239
CS 105: Introduction to Databases and Data MiningFall Semester 2024Instructor:Andrew WoodEmail:aewood@bu.eduLecture:Mon/Wed/Fri 11:15-12:05pm ESTCAS B12Lab1:Wed 1:25-2:15pm ESTCAS 218Lab2:Wed 2:30-3:20pm ESTFLR 152Lab3:Wed 3:35-4:25pm ESTCGS 323Lab4:Wed 4:40-5:30pm ESTCAS 204ATeaching Staff:•Anwesha Sasha (TF) —anwesha@bu.edu•Lamar AlSubhi (TA) —LamarMA@bu.edu•Javeria Jalil (CA) —javeria2@bu.eduOffice Hours:•Andrew: TBD•Anwesha: TBD•Lamar: TBD•Javeria: TBDMain References:This is a restricted list of various interesting and useful books that will be touchedduring the course. You need to consult them occasionally.•The official Python guide which can be foundhere.•Python practice problems can be foundhere,here, andhere.•Relational Algebra practice problems can be foundhere,here, andhere.•SQL practice problems can be foundhere,here, andhere.Course DescriptionDatabases and other collections of data are everywhere.Retailers use data aboutcustomers and their purchases to make decisions that increase profits. Researchers analyze genomic datato find treatments for diseases. Policymakers analyze socioeconomic data to gain insights that guide theirdecisions.Online music and video services perform data mining to deliver customized recommendations.How does all this work? CS 105 examines how data is organized, processed and displayed. Topics includerelational databases and the SQL query language, the writing of simple programs to process data, theprinciples of data visualization, and data-mining techniques for discovering patterns in data. At the end ofthe course, students apply the topics they have learned to a collection of data that interests them. CarriesMCS divisional credit in CAS. This course fulfills a single unit in the following BU Hub areas: QuantitativeReasoning II, Creativity/Innovation, and Critical Thinking.page 1 of4
Course NameSeptember 4, 2024Department InformationThis course fulfills the following HUB units:Quantitative Reasoning IILearning Outcome 1: Students will frame and solve complex problems using quantitative tools, such asanalytical, statistical, or computational methods.Learning Outcome 2: Students will apply quantitative tools in diverse settings to answer discipline-specificquestions or to engage societal questions and debates.Learning Outcome 3: Students will formulate, and test an argument by marshaling and analyzing quanti-tative evidence.Learning Outcome 4: Students will communicate quantitative information symbolically, visually, numeri-cally, or verbally.Creativity/InnovationLearning Outcome 1: Students will demonstrate understanding of creativity as a learnable, iterative processof imagining new possibilities that involves risk-taking, use of multiple strategies, and reconceiving inresponse to feedback, and will be able to identify individual and institutional factors that promoteand inhibit creativity.Learning Outcome 2: Students will be able to exercise their own potential for engaging in creative activityby conceiving and executing original work either alone or as part of a team.Critical ThinkingLearning Outcome 1: Students will be able to identify key elements of critical thinking, such as habits ofdistinguishing deductive from inductive modes of inference, recognizing common logical fallacies andcognitive biases, translating ordinary language into formal argument, distinguishing empirical claimsabout matters of fact from normative or evaluative judgments, and recognizing the ways in whichemotional responses can affect reasoning processes.Learning Outcome 2: Drawing on skills developed in class, students will be able to evaluate the validity ofarguments, including their own.page 2 of4
Course NameSeptember 4, 2024Course InformationCourse BreakdownThis course is split into three sections.In the first section, we will learn how toprogram in the Python programming language.The second section will focus on databases: particularlyrelational algebra dnd SQL. The last third of the course will cover data science/mining techniques at a highlevel and is also where we will apply our programming and database skills from the prior sections.This course will havewrittenas well aslabassignments. There will be in at mosttenwritten assignments,each release on a Monday and due that Friday by 11:59pm. They will ask you conceptual questions, vocabu-lary, and interacting with and analyzing programs. Each written assignment will be worth 50 points, and youwill submittypedsolutions (in pdf format) on gradescope which will be graded by hand by the teaching staff.There will beeightlab assignments this semester, each one week long. Lab assignments will be released ona Monday and will be due Friday of the same week at 11:59pm EST. Lab assignments will ask you to writecode as well as use/read about code other people have written (that we will use in the future). Each labassignment will be worth 50 points, and you will submitcodefiles on gradescope which will be graded byautograders.NOTE: the grade the autograder gives you is your grade for the lab assignment.Please make sure to check that you are submitting the correct file(s) with the correct name(s)!This course will have a final project, worth 200 points. We recommend students form groups of at most six.More information on this to come.This coursewillhave two midterms: one after the Python programming section of the course, and anotherafter the databases section. Additionally, this coursewillalso have a (cumulative) final exam. Each midtermwill be worth 100 points, and the final will be worth approximately 300 points.Course GradesTo calculate your final grade, 20% will come from your homework grade, 20% from yourmidterms (10% each), 20% from labs, 20% from the final project, and 20% from the final. There will beplenty of extra credit opportunities in lecture, homework, and labs. Including extra credit, it is possible toearn more than 100% in this course! Grades will be assigned using the following table:letter gradepercent thresholdA93%A-90%B+87%B83%B-80%C+77%C73%C-70%D60%F<60%page 3 of4
Course NameSeptember 4, 2024Late PolicyIn general, I am very lenient regarding late work or extensions. Please try to stay incontactwith me if you need extra time, its ok!I want each and every one of you to have a pleasant, low stressexperience in this course. I find that minimal stress is important to the learning process, and I want eachof you to learn from this course rather than stress about points. That being said, unless I hear from you,I will not be accepting assignmentstwodays after they are due. The first day after an assignment is duewill result in a 10% penalty. The next day, there is a 25% penalty (the penalties donotstack).ExpectationsWe encourage students to work collaboratively in this course, but will not condone cheatingas defined in the BU handbook. While we encourage students to discuss problems with each other and theteaching staff, students must submit their own work and may not use virtual agents such as ChatGPT,Google CoPilot, etc. for assistance with any assignments or exams.As mentioned previously, we recommend students form teams of at most six to work on the final project.This is so that we can fit presentations into the last two days of class.For this reason, it is extremelyunlikely that groups of less than 6 members will be permitted, but please ask first!GradescopePlease submit your homework solutions to gradescope.Please add yourself to the coursegradescope with the following code:R7DX6Rpage 4 of4