Introduction to Programming with Data UF College of

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practices while quickly getting started with practical data evaluation tasks. like tabular reporting and data visualization techniques. Course Objectives,By the end of this course students will. Create Python scripts to fix a problem such as how to automatically. send emails how to generate CSV reports and how to scrape a. website for data, Evaluate data visualization techniques and determine best practices. for sharing data visually, Analyze data in tabular format using Pandas and NumPy. Assemble SQL queries for data extraction from a database. Identify statistical methods of data analysis and describe why they. are useful and significant, Use Jupyter Notebook to share Python skills and data analysis. techniques,Develop tests to evaluate Python functions.
Define Python data types and several methods available to each. Execute Python scripts via Jupyter Notebook and the shell or. command prompt, Examine text data using basic Natural Language Processing. techniques such as bag of words, Evaluate an API for use and execute commands against that API. using the Python requests library,Course Goal, Why is this course important Learning how to program is an important skill. for those who wish to harness data at their fingertips or manage others who. do so Python is a popular language for data science objectives and is easy. to learn Whether or not students plan on using their programming skills at. work or even becoming a data scientist it s essential to understand what is. both easy and hard to accomplish using programming if a person is to. oversee or interact with technical teams Finally being self sufficient and. able to answer their own data questions with some insight will help keep. students stay ahead of the curve and somewhat independent when. managing projects or fulfilling data related tasks. Expectations, In order to cover the wide variety of topics included in this course students. will need to apply themselves thoroughly to the coursework and bring a. willingness to try new things and a curiosity for data In this course. students will apply self guided learning techniques such as how to debug. without an expert by their side and how to use StackOverflow and group. chats to solve programming problems Throughout the course students will. be expected to ask questions and help others who are stuck These are. great skills beyond the scope of programming and will help students. succeed in most data analysis tasks they perform or advise in the. This course requires students to perform a pre class assessment and have. a laptop with an operating system which allows them to install applications. and programs i e Administrative access If you are running Windows you. will need Windows Vista or later If you are running OS X you will need. 10 8 or later Mountain Lion If you are running Linux please insure you. can install Python 3,Ownership Education, As graduate students you are not passive participants in this course This.
class allows you to not only take ownership of your educational experience. but to also provide your expertise and knowledge in helping your fellow. classmates The Canvas shell will have an open Q A thread where you. should pose questions to your classmates when you have a question as it. relates to an assignment or an issue that has come up at work Your. classmates along with your instructor will be able to respond to these. questions and provide feedback and help The same applies to the course. Slack team channels This open communication and accountability also. allows everyone to gain the same knowledge in one location rather than the. instructor responding back to just one student which limits the rest of the. class from gaining this knowledge, Required Text Data Wrangling with Python by Jacqueline Kazil and. Katharine Jarmul, Required Installations You will need to have Python and several other. libraries installed on your computer I will also provide a shared server for. some exercises i e quizzes and tests but it is highly recommended you. set up your local computer to run all programs for testing project work and. your own use If you have not used Python before I recommend following. the Python 3 6 X installation instructions here,MacOSX https www python org downloads mac osx. Windows https www python org downloads windows, Other Platforms https www python org download other. If you have never used Python for data science before I also ask that you. install anaconda https www continuum io downloads for managing. packages and different Python versions, To properly install Python 3 6 here are some outlines for each operating.
Windows Vista or later,Apple OS X 10 8 or later Mountain Lion. Linux Please ensure you can install via normal package manager or. If you run into trouble during any installation feel free to email however I. encourage you to first try searching and solving your problem Becoming. more familiar with the inner workings of your computer and how to fix. computer problems is a great first step in learning to program and a skill. you will hone throughout this course,Additional Readings. Listed in the course schedule and in each weekly module on Canvas. Prerequisite knowledge and skills, Students should have intermediate knowledge of how to install and debug. programs on their own computers We will be practicing these skills often. throughout the course so it s okay to be a bit slow at first I encourage. students to as soon as possible try walking through the Python and. anaconda installations covered in the Required Installations section This. will be good practice for getting to know a bit more about your computer. As part of the pre course training you should step through one of the. following introduction to Python video courses that are free online This will. help you get a jumpstart on the course and allow some things to be easier. repetition and review rather than immediately diving in with no background. If you have already programmed with Python before you can skip this. requirement, CodeAcademy https www codecademy com courses introduction. to python 6WeG3 0 1, DataCamp https www datacamp com courses intro to python for.
data science,Khan Academy, https www youtube com watch v husPzLE6sZc list PL36E7A2B7. In addition if you d like to learn a little more about the command line which. I have found incredibly useful for debugging and working better with my. computer and computer internals I recommend taking a look at these. Windows MS DOS,https www youtube com watch v MNwErTxfkUA. Windows PowerShell Windows 8 0, https www youtube com watch v IHrGresKu2w I only recommend. watching this if you already know DOS,Unix based Mac Linux. https www codecademy com en courses learn the command line. More in depth Unix based bash https github com Idnan bash. Teaching Philosophy, As a primarily self taught developer I strongly favor practice and project.
based learning for computer science Throughout this course we will touch. upon the deeper theories and academic approaches to data science and. computing however the course will have a strong emphasis on practical. use cases and projects I believe this allows you to quickly apply and excel. at programming to help you do your work while still allowing for questions. growth and curiosity towards the academic field This approach will be. reflected in our Weekly Readings which will blend the research in the field. with the daily applications,Instructional Methods, This course will involve several different instructional methods as a way to. address different learning styles and approaches If you find your learning. style is not adequately addressed please feel free to offer feedback via. email at any time The methods are as follows,Video lectures. Required readings,Online quizzes and tests,Discussion threads posting and voting. Asynchronous group discussions via Slack, Coding projects alone and in small assigned groups. Peer and self assessments and code reviews,Course Policies.
Attendance Policy, Due to its online nature the course will not have in person meetings or. attendance in a classic sense Students are required to. Check Slack for regular updates at least twice a week. Check the course discussion board and participate in postings voting. and discussion threads at least once a week, View and read all required content by the due date. Send required projects in by their respective due dates. Respond to group coordination and emails in 24 hours or less. If a student fails to meet the above attendance requirements there will be a. reduction in the participation portion of the student s grade I encourage. students to install necessary applications such as Slack on their mobile. device or set up alerting to ensure you can promptly respond to fellow. students and instructor messages without long delays. Late Work and Make up Policy, Deadlines are critical to this class All work is due on or before the due. date Pre approved extensions for deadlines will only be permitted for. emergencies Minor inconveniences such as technical issues family. vacation or minor illness are not valid reasons for extensions With this in. mind there will be penalties for late work NO LATE ASSIGNMENTS WILL. BE ACCEPTED FOR FULL CREDIT without prior arrangements that are. acceptable to the instructor unless the lateness is due to an excused. absence such as illness or catastrophic emergency that can be. documented This is true for all assignments discussion boards papers. case studies etc Late penalties are as follows,Assignments one hour late 15 penalty. Assignments an hour late but 12 hours late 25 penalty. Assignments 12 hours late but 24 hours late 50 penalty. Assignments 24 hours late but 48 hours late 70 penalty. Assignments 48 hours late 0 points no credit or 100 penalty. If you have an emergency or pre approved schedule when you will be. unavailable to complete assignments such as offline or limited access to. internet you must let the instructor know at least 5 days in advance. Requirements for class attendance and make up exams assignments and. other work in this course are consistent with university policies that can be. found in the online catalogue at, https catalog ufl edu ugrad current regulations info attendance aspx.
Emergency and extenuating circumstances policy Students who face. emergencies such as a major personal medical issue a death in the. family serious illness of a family member or other situations beyond their. control should notify their instructors immediately. Students are also advised to contact the Dean of Students Office if they. would like more information on the medical withdrawal or drop process. https www dso ufl edu care medical withdrawal process. Students MUST inform their academic advisor before dropping a. course whether for medical or non medical reasons Your advisor will. assist with notifying professors and go over options for how to proceed with. their classes Your academic advisor is Tiffany Robbert and she may be. reached at trobbert jou ufl edu,Coursework, Most non coding coursework will be submitted via Canvas There are. several other services we will use throughout the course for submissions. Coding Projects Github,Code Reviews Github,Quizzes Tests Canvas Jupyter Server. The weekly coursework deadlines and where to submit work will be posted. to Canvas and updated as needed throughout the course. This class like others involves many deadlines Here is a reminder The. new lecture starts on Mondays, Code Practice Quiz 11 AM EST Wednesdays the week assigned. Course Discussions 11 AM EST Thursdays the week assigned. Assignments 11 AM EST Fridays the week assigned, Final Coding Project 11 AM EST last Wednesday of the semester. Your work will be evaluated according to this distribution There will be. opportunities in some of the assignments for extra credit Those. opportunities will only count for students with regular on time completion of. other assignments i e 75 of work turned in on time and complete. Reading Reactions Chat Participation 20,Quizzes 10.
Assignments 35,Final Project 35,The final grade will be awarded as follows. A 100 to 93,A 92 to 90,B 90 to 87,B 87 to 83,B 82 to 80. C 80 to 77,C 77 to 73,C 72 to 70,D 70 to 67,D 67 to 63. D 62 to 60, https catalog ufl edu ugrad current regulations info grades aspx. I will round grades to the nearest half percent meaning a 92 50 92 99 will. result in an A whereas a 92 00 92 49 would remain an A. Weekly Lectures, This course will have weekly video lectures shared via Canvas These.
videos will be a mixture of content produced by the instructor as well as. other free online videos that explain and demonstrate the course content. for the week I ask that you watch all videos and complete all reading. before attempting the quiz and assignment content even if the content is. review for you It s likely there are some tips and other pointers covered. that you will be assessed on at a future point, In addition to the video lectures there will be between 1 3 optional live. lectures where we will meet in a live setting This could be to share a. conference or meetup talk I find engaging or simply to have a group. discussion on several topics and a demonstration of a . Introduction to Programming with Data Fall 2017 Instructor Katharine Jarmul Email kjarmul jou ufl edu Twitter kjam About the Instructor Katharine Jarmul is a data scientist and educator based in Berlin Germany Originally from Los Angeles California she first began working with Python for data analysis in 2008 at the Washington Post Since then she has worked at large and small

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