STAT 207 Data Science Exploration Fall 2023 Welcome Course info is available through Canvas https canvas illinois edu courses 37967 Instructor Dr Julie Deeke Office 703 S Wright Street Room 44 Lecture Meetings Tuesday Thursday 12 30 1 50 pm 1024 Chemistry Annex Lab Instructors Meetings Email jdeeke illinois edu Pronouns she her Name Email Sections Meetings Location Prono uns Yuxuan Liu yuxuan15 he him DYA DYB W 9 10 20 11 12 20 209 Huff Hall Kaihong Zhang kaihong5 he him DYC DYD W 1 2 20 3 4 20 209 Huff Hall Office Hours A full set of office hours will be posted to Canvas A mix of virtual Zoom and in person office hours will be offered Learning Objectives Building on the foundation of STAT 107 we use Python Jupyter notebooks and GitHub to explore data science techniques statistical concepts and data analytic workflows combined with the statistical analysis of STAT 200 As we explore data science we Develop statistical thinking for understanding data selecting appropriate procedures for questions and writing conclusions Create and evaluate models that allow us to make predictions for new data and consider the different features to be balanced when making an optimal decision Interpret what statistical techniques imply about how variables are related to each other Apply our understandings from current data to make inferences about the underlying population Become proficient in Python coding for data management analytics and visualization Use GitHub repositories the industry standard for submitting code and reports Stat 207 Deeke F23 Syllabus Page 1 Course Description Explores the data science pipeline from hypothesis formulation to data collection and management to analysis and reporting Topics include data collection preprocessing and checking for missing data data summary and visualization random sampling estimating parameters uncertainty quantification hypothesis testing multiple linear and logistic regression modeling classification and machine learning approaches for high dimensional data analysis Students will learn how to implement the methods using Python programming and Git version control Prerequisites STAT 107 Data Science Discovery Course Materials 1 Calculator You can use any calculator for most of the course including your computer s calculator You will need a hand calculator not your phone for in person exams 2 Laptop computer You will need a laptop running Windows OS X or Linux that you can download and install software on Tablets Chromebooks and iPads are not supported 3 Canvas Site free online Canvas will be our learning management system LMS this semester Julie will post announcements assignments files links to tools and resources and grades on Canvas 4 Data Science Software python free download We will be using the data science software python accessed through jupyter notebooks for this course We ll use miniconda for jupyter which is available as a free download through https docs conda io en latest miniconda html Full download instructions can be found on Canvas 5 GitHub free download online We will be using the version control software git and the accompanying online service GitHub to download and submit lab assignments Git needs to be installed on your computer and a personal repository needs to be created within our GitHub Enterprise site Instructions on downloading git and creating your personal repository can be found on Canvas Our GitHub Enterprise site can be accessed through https github com illinois cs coursework after set up 6 Campuswire free online Campuswire is an online crowdsourcing discussion board You ll be able to ask and answer questions communicate with your classmates and talk with your course instructors through this platform This is free if you are ever asked to pay for this service let Julie know Our campuswire site is https campuswire com p G5A6B368B with access code 2450 7 Audience response system either iclicker or DSclicker free 16 On the first day of class we will select an audience response system based on group consensus The choices are iclicker paid or piloting a DSclicker program 8 Optional Course Texts free online If you d like to read more about the topics in this course we recommend a J VanderPlas 2016 Python Data Science Handbook https jakevdp github io PythonDataScienceHandbook b Diez Cetinkaya Rundel and Barr 2015 OpenIntro Statistics https www openintro org book os Stat 207 Deeke F23 Syllabus Page 2 Guide to Course Success I Julie am here as a resource to you and your learning I want everyone to succeed in the course and I am dedicated to providing you with the necessary resources and tools needed I can only respond and provide this support if I know what you need so please do let me know how I can help you in the course whether big or small Here are a couple of tips for this semester Communicate with your instructional team This is my biggest piece of advice Let us know what we can do for you how we can adjust things to help you succeed in the course and any accommodations we can make to support your learning Whether temporary or permanent don t hesitate to reach out Attend Lectures Regularly Lectures will be interactive and a place for you to make connections with the course material Lectures will be recorded for you to review any content that you missed or want to rewatch Start Assignments Early We ll be working with technology a lot in this course and technology has a way of breaking at the last minute It s a good idea to build in some cushion time for yourself by starting assignments early Drop by Office Hours Everyone gets confused or needs help sometimes If you are unsure about a concept can t figure out a solution to a problem or are just stuck you re not alone Stop by office hours we ll have a mix of in person and online office hours for you We ll be waiting for you to drop by If you can t make office hours you can always post to campuswire too Create a Consistent Weekly Schedule Creating and keeping a consistent weekly schedule will help you by forming a routine and by keeping track of course assignments Embrace Difficulty and Practice Did you know that you learn more when you face a challenge struggle with it and figure it out compared to when someone tells you the answer I encourage you to embrace desirable difficulties those instances where learning really happens At the same time don t struggle on your own for hours If you can t figure out how to solve a problem after a reasonable effort ask someone a