All writing for this course must be original to this class this semester. Each student will produce a minimum of graded words for the course and must submit all major papers to Turnitin. Essays 1, 2, 3.
It teaches students to gather, select, and model large amounts of data. We consider the tremendous amount of data available volumehow to acquire it in a timely manner velocityand how to extract what is meaningful for a particular enterprise from a multitude of sources that may take many forms of images, media sources, social Critical issuesweek8, and the like variety.
This course also deals with how to store and protect both the data and the information generated from it. Although the course relies on well-known techniques and theory from mathematics, modeling, probability, statistics, and computer science to address these issues, we approach the course from a more practical, hands-on point of view where the student samples, explores, modifies, models, and assesses target data sets through the use of a comprehensive, workbench approach using data mining and data warehousing tools.
The large data sets considered are freely available from traditional data centers or network environments such as the US Census Bureau as well as from new storage technologies such as the cloud. The course also explores the technical and practical aspects of data science, the historical value of digital data, and its effects on Critical issuesweek8 implications for today's business world.
The professor will write scenarios so you can comment, critique, rip apart based on your comprehension of the material. The objective of these discussions is to manifest your understanding of the subject. Participation is what is important. If you agree or disagree with a particular statement or point of view, do not go silently in the night - argue your point but back it up with valid research.
Adams II, Faculty Email: Please do NOT email Dr. It is better to ask these questions in the discussion forum for the class, where others can benefit from the answer as well. Saturday 10 am to 11am EST or longer as needed by students!
The course will use a mixture of lecture notes, readings, labs, and homework to reinforce learning. How the class works The entire class is taught online through Canvas and Blackboard Collaboration which means you have flexibility in attending the class.
It is highly recommended that you attend the lectures and labs in order to interact with your Professor, TA, and fellow students. But we understand that most of you are working students and, at Harvard University, it is our goal to work with diverse schedules so you may continue your journey in higher learning.
Each week we list our assignments, homework, labs, and projects for you to stay focus. There is a weekly schedule so you will not be shocked by an upcoming due date. We have students from around the world in this class so we try to make our lectures and labs precise and clear.
There is a discussion forum where you can interact with your fellow students. We will answer your questions and post discussion topic to peak your interest in trends that are going on in the IT communities. We have tried to spread out the assignments, lectures, labs, and final project to ensure you have enough time to complete everything without stress.
The object of this course is to teach students the general concepts of Data Science and how to manage large data in a business environment.
Students will learn a historical approach in defining and understanding large data in the business world. Learning how to use statistical tools in manipulate Big Data. Learn critical programming skills with R Programming. Accessing, transforming and manipulating big data through real world case study.
Improve and clean data quality for reporting and analytics for decision makers. Learn the fundamentals of statistics and analytics. Exploring and visualizing data. Explore career opportunities in Data Science, Mining, and Warehousing.McMillan, K.
& Weyers, J. () How to Improve Your Critical Thinking & Reflective Skills (Smarter Study Skills), Harlow, Pearson. DMU Library Resource List. The list of learning resources can be accessed at the DMU Library, which has links to any additional electroinc resources associated with these books.
TECH DMU Library Resource List. Discussion postings will be evaluated for content, quality of writing, critical thinking and timeliness. Discussion postings that are not available for other students to read, consider, and offer a response, do not contribute to the course.
Gail Dines and Jean M.
Humez (). Gender, Race, and Class in Media: A Critical Reader. SAGE Publications, Inc (4th edition). Additional Readings: Included within each module Course structure This course is web-only.
A learning module has been set up for each section of the class. Each. Critical Issuesweek8. Critical Issues Paper: Week Eight Assignment CJS/ 09/18/ Critical Issues Paper: Week Eight Assignment As outlined in the course syllabus for week eight this paper is going to focus in on critical issues involved in policing.
List and describe three critical issues of the twenty-first-century in policing. In considering ethical leadership challenges discuss how problems associated with these issues might be resolved after reviewing the following three studies The challenges that face 21st Century Policing are numerous and varies based on the operational environment.
Law enforcement on all level of policing from. and critical thinking skills to their personal and professional decision-making processes -- skills essential for success in a learning-centered university and as engaged citizens in a global community.