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JOHN CABOT UNIVERSITY

COURSE CODE: "EC/MKT 361"
COURSE NAME: "Applied Data Analytics"
SEMESTER & YEAR: Fall 2019
SYLLABUS

INSTRUCTOR: Alina Sorgner
EMAIL: [email protected]
HOURS: MW 1:30-2:45 PM
TOTAL NO. OF CONTACT HOURS: 45
CREDITS: 3
PREREQUISITES: Prerequisite: MA 208
OFFICE HOURS:

COURSE DESCRIPTION:
This course will examine current trends in data science, including those in big data analytics, and how it can be used to improve decision-making across different fields, such as business, economics, social and political sciences. We will investigate real-world examples and cases to place data science techniques in context and to develop data-analytic thinking. Students will be provided with a practical toolkit that will enable them to design and realize a data science project using statistical software.
SUMMARY OF COURSE CONTENT:
Course topics include: Data-analytic thinking and data-driven decision-making. Data science process. Data sources and types of data. Data management and data manipulation with statistical software (Stata). Exploratory data analysis and data visualization. Introduction to big data analytics. Classification and prediction problems. Basic machine-learning algorithms. Geo-mapping. Text mining.
LEARNING OUTCOMES:
1. Understand the fundamental concepts and applications of data science and apply this knowledge for data-driven decision-making.
2. Understand the current trends and applications of big data as well as limitations of big data analytics.
3. Use statistical software to work with different types of data.
4. Design and execute the complete data science process, including problem formulation, data collection, exploratory data analysis, data visualization, modeling, evaluation, and communicating results.
5. Develop teamwork skills and presentation skills.
TEXTBOOK:
Book TitleAuthorPublisherISBN numberLibrary Call NumberComments
Data Science for BusinessProvost, Foster and Tom FawcettO’Reilly: Sebastopol, CA978-1-449-36132-7  
REQUIRED RESERVED READING:
NONE

RECOMMENDED RESERVED READING:
NONE
GRADING POLICY
-ASSESSMENT METHODS:
AssignmentGuidelinesWeight
Attendance and class participationAttendance and active involvement in discussions and other class activities is essential.10%
Mid-term examThe mid-term exam evaluates a student’s understanding of concepts taught during the first half of the course.20%
Data Science ProjectDesigning, executing, and reporting on a data-science-oriented study. There will be two project presentations, intermediate and final (10% of the final grade for each presentation). Students will submit a final written project report (30% of the final grade). 50%
Final examThe final exam is comprehensive.20%

-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.

-ATTENDANCE REQUIREMENTS:
ATTENDANCE REQUIREMENTS AND EXAMINATION POLICY
You cannot make-up a major exam (midterm or final) without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed. The final exam period runs until ____________
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic dishonesty will receive a failing grade on the work in which the dishonesty occurred. In addition, acts of academic dishonesty, irrespective of the weight of the assignment, may result in the student receiving a failing grade in the course. Instances of academic dishonesty will be reported to the Dean of Academic Affairs. A student who is reported twice for academic dishonesty is subject to summary dismissal from the University. In such a case, the Academic Council will then make a recommendation to the President, who will make the final decision.
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap. Students with approved accommodations must inform their professors at the beginning of the term. Please see the website for the complete policy.

SCHEDULE

SessionSession FocusReading AssignmentOther AssignmentMeeting Place/Exam Dates
Week 1 & 2Introduction to Applied Data Analytics. Data-analytic thinking and data-driven decision making. Data science process. Data sources and types of data. Current trends and applications of big data.   
Week 3 & 4Data management, data cleaning and data mannipulation with statistical software (Stata). Writing and executing a program in statistical software.   
Week 5 & 6Exploratory data analysis. Data visualization techniques.   
Week 7 -10Introduction to Big Data analytics. Classification and prediction problems. Basic machine-learning algorithms. Classification trees, naive Bayes, linear regression, logistic regression, k-nearest neighbors, k-means.   
Week 11 & 12Geographic Information Systems (GIS). Geo-mapping.   
Week 13 & 14Text mining. Text as Data. Bag of words. N-grams. Mining the social web (e.g., Twitter).