Description

Python is a modern, high-level programming language. One of the most popular programming languages, its use has steadily increased across many industries. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure.

Structure Querying Language (SQL) is the most common language globally for interacting with relational databases. Employers have indicated that knowledge of SQL is one of the essential skills for new graduates entering the workforce. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data.

This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics.

Course Developers

Professor Ben Harris ([email protected] ), Initial Content Developer, 2021

Professor Hanbo Yu ([email protected] ), Content Revisions, 2022

Prerequisites

.     Instructor-Led or Self-Paced Laboratory AD PY100

.     Self-Paced Laboratory AD200, Unit 6

Student Competencies

.     Primary computer use in Windows or Macintosh

.     Foundations in statistics and mathematics for applications in management

Text and Materials

Required Textbooks:

Introducing Python: Modern Computing in Simple Packages  , 2nd edition. Bill Lubanovic, O'Reilly Media. ISBN-

13: 978-1492051367, ISBN-10: 1492051365

Learning SQL: Generate, Manipulate, and Retrieve Data,  3rd Edition, 3rd Edition, Alan Beaulieu, O'Reilly Media. ISBN-13: 978-1492057611, ISBN-10: 1492057614

Optional Textbooks:

The elements of Statistical Learning: Data Mining, Inference, and prediction. Hastie, T., Tibshirani, R., & Friedman, J. H. (2017). Springer. ISBN-13: 9780387952840

Introduction to algorithms. MIT Press (3rd edition),al,Cormen - Thomas H. - Leiserson - Charles E. - Rivest - Ronald L. - Stein - Clifford. et. (2009). ISBN-13: 9788120340077

Learning Python, 5th Edition, Mark Lutz. O'Reilly Media. ISBN-13: 978-1449355739, ISBN-10: 1449355730

SQL Cookbook: Query Solutions and Techniques for Database Developers  , Anthony Molinaro, O'Reilly Media. ISBN-13: 978-0596009762, ISBN-10: 9780596009762

Blackboard

This course will use a Blackboard site. To access the course website, go to http://learn.bu.edu/ select Spring 2022 and AD599 A1.

Students are required to have a BU ID and password to log in. If you do not have a BU ID yet, note that this takes some time to start this process well before class starts.

Course Overview and Structure

Modules and Lectures

Module 1: Introduction to Python and Data Structures

Lecture 1: Introduction to the Python language, installation, first programs, workspace options, current applications.

Lecture 2: Script programming, data types, variables, mathematics, list operations, data structures (stack, queue, linked list, hash table, tree).

Module 2: Modules, loops and functions

Lecture 3: Modules, flow control, conditional logic, text and strings, formatting and printing, loops

Lecture 4: Functions, tuples, lists, and dictionaries

Module 3: Introduction to common algorithms and realization of data structures

Lecture 5: Advanced topics for functions, Introduction to Algorithms, Common Algorithms, Realization of Data Structures

Lecture 6: Numpy/Scipy (functions and capabilities), Pandas.

Module 4: Data science package for Python and Regressions

Lecture 7: Statsmodel and Sklearn

Lecture 8: Regressions, Cross-Validation, and Model Selection

Module 5: SQL Introduction – basic and intermediate syntax and practical SQL

Lecture 9: Database and SQL Introduction, basic syntax and environment, Creating and Populating Databases, basic queries and filtering

Lecture 10: Intermediate queries and subqueries, sets and joins, Grouping and Aggregation, Views

Module 6: Python and SQL integration

Lecture 11: CTE and Window functions

Lecture 12: Integration of Python and SQL

Each module adds a methodological aspect from its respective language focus. Each module will include one assignment and one discussion forum post.

Students will have to submit a final individual project in week 13. The final exam is in-person presentation.

Learning Goals

After successfully completing this course, students will understand the syntax and usage of Python and SQL programming languages. They will be able to address programming problems using coding and prototyping methods introduced and practiced throughout the course.

Students will be able to develop programs from scratch to solve or automate problems commonly encountered in analytics. While specific analytics methods are taught in other courses in the Administrative Sciences department, AD599 will provide the methodological foundation by which students can quickly adapt to new techniques and languages.

Grading Structure

This course is organized for around 14 weeks. The material is presented in the section Course Calendar of the syllabus. On Day 1 (the day when we will have class) we will have an in-class session. Day 7 is the last day of the week prior to the next class. Please adhere to the due dates posted on the course website. Late work is not accepted.

Your performance in the course will be graded in the following areas:

Additional details for each grading component are provided below.

In-Class Discussions and Contribution to the Blackboard Biweekly Discussion Forums

We at Boston University recognize and reward excellence. Excellence is uncommon, even rare. Your grade, then, will reflect the standards of excellence set by Boston University, in which only genuinely outstanding work will receive the highest score.

I strongly encourage student participation and contribution during the lectures. Use this opportunity to

distinguish yourself from others and benefit from personalized studying, learning, and teaching experience.

Students are expected to have read the relevant chapters before each class and prepare for group discussions about the material in them.

Individual Assignments

Assignment  1:  Python  Jupyter  notebook  assignment, including  tuple,  list and dictionary operation and scripting

Assignment 2: Python Jupyter notebook assignment, including modules, flow control, conditional logic, text and strings, loops, functions

Assignment  3:  Python  Jupyter  notebook  assignment,  including  data  importation,  data  cleaning,  model building, model selection

Assignment 4: SQLite assignment, including filtering, subqueries, aggregation functions, join

Unless explicitly stated otherwise – any individual assignment is due at 11:59 pm ET on the last day of the lecture week. The general policy is that late assignments will not be accepted

Quizzes. There will be four quizzes probing the assigned reading and the content of weekly discussions.

Team Term Project explained

Phase 1: According to prompt requirements, develop an analysis plan, select the data needed, and develop three ideas for applying a database.

Phase 2:  Integrate Python and SQL to solve the problems again in Phase 1

Final Report: Summarize your results in a managerial report

Final   Presentation:   Summarize  your   methodologies,   thoughts,   analyzing   process,   results   in   a   final presentation

Requirements, Policies, and Standards

Grading Policy

Grade inflation is in the best interests of neither BU students nor the institution's reputation. Therefore, a grade of A or A will be given only to students who truly distinguish themselves in the course.

The Academic Policy Committee of Metropolitan College recommends the following guidelines for distinguishing grades:

.    A, A-                  20% of the class

.     B+, B, B-           80% of the class

.    Other                as merited

Student Preparation

Minimal preparation is reading the material, i.e., the chapter(s) we will go over in class, summarizing what it is about, the significant issues, and offering some recommendations.

Superior preparation involves being able to (i) summarize the situation or problem presented; (ii) recommend a solution to the discussed problem; (iii) support your recommendation with relevant details and analyses;

and (iv) discuss innovative solutions.

Excellent work will be rewarded with an A. This grade requires quality excellence in all aspects of the course: quizzes, discussions, projects, and exams. Grades will be curved.

Students will not be allowed to submit extra work beyond what is defined in the syllabus to improve their grades as this will be unfair to other students.

The Department uses the APA style to facilitate reading the paper and understanding references without

being cumbersome as some of the other styles (such as Chicago or MLA). Students can download the student style guide from the American Psychological Association website http://www.apastyle.org/elecref.html )

Papers are to be Research Papers. Remember that work that you use from other authors must be referenced. Since it is assumed that you are not an authority on the topic that you are writing, this paper is expected to    be an overview of many different sources of information. These must be contributed to the author using the   APA format. This is your paper and not the cut and paste of someone else's work. The Internet has led to a

false sense of what research is all about. An academically sound study differs from spending an afternoon surfing the Internet and then cutting from material available. Remember: (1) the Internet is not quality-

oriented – it does not know the difference between relevant to your study documents and data sources; (2) the Internet is not a sole source for your research. In particular, sources such as Wikipedia are often not

reviewed by third-party submissions by individual contributors. Thus, while many entries provide excellent information, some are fundamentally flawed or just plain wrong. Keep in mind that the Boston University   Library and your local, state, and the national US Library of Congress have extensive online services. Use    them.

Policies on Late Assignments

All assignments and assessments have due dates. These are the last dates that the stated material is due. I maintain the right to refuse or downgrade any materials presented after due dates. This is not a subject for discussion.

Students should develop a schedule to build the work around their personal needs and obligations. The

student should organize their time and work to turn in the assignment before the due date. To be clear, this means that the work will be accepted anytime up to that date but not after. Students should allow for contingencies and plan to hand in their work well before the last minute. That way, should some unforeseen problem arise, the timely presentation of work is not in jeopardy.

Requests for Extensions

Sometimes, unfortunate situations occur that make fulfilling requirements impossible. Any request for

extensions will be evaluated on a case-by-case basis. There is no guarantee that make-up will be permitted, and any request needs to be in writing, and written verification of the incident will be expected. If you are   unable to meet an assignment deadline for any reason, you should contact the instructor immediately and  preferably in advance.

Academic Integrity and Honesty

You are to complete any project or assignment on your own. The academic conduct policy is summarized below. For the full text of the academic conduct code, please go to:

www.bu.edu/met/metropolitan_college_people/student/resources/conduct/code.html

Any Plagiarism will be reported to the Dean and dealt with according to the Academic Conduct Code of Metropolitan College.

Satisfaction of Department-Wide Goals