MET AD 599 A1:
Introduction to Python and SQL for Business Analytics
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, loopsLecture 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 SklearnLecture 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 functionsLecture 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:
|
Discussions in class and in the Blackboard
(i) Five biweekly Bb-based Discussion Forums (max. 4 pts/week)
(ii) In class discussions (max. 5 pts) |
20% |
|
Quizzes: 4 quizzes @ max. 5 pts/quiz |
|
|
Assignments: |
|
|
Four Individual Assignments (Due W4, W6, W8, W10)
Max. 7 pts each
|
28% |
|
Team Term Project, Phase 1 & Phase 2, Max 16 pts |
16% |
|
Final Report & Final Presentation, Max 10 pts |
10% |
| Class Participation | 6% |
|
TOTAL:
|
100% |
| Grade Converter Table | ||
|
% |
Points |
Grade |
|
96-100 |
4.00 |
A |
|
91-95.99 |
3.67 |
A- |
|
86-90.99 |
3.33 |
B+ |
|
81-85.99 |
3.00 |
B |
|
76-80.99 |
2.67 |
B- |
|
71-75.99 |
2.33 |
C+ |
|
66-70.99 |
2.00 |
C |
|
61-65.99 |
1.67 |
C- |
|
56-60.99 |
1.33 |
D+ |
|
51-55.99 |
1.0 |
D |
|
Under 50.99 |
0 |
F |
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 qualityoriented – 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
|
# |
Goals |
Category |
Compliance |
|
1 |
Critical and
Innovative
Thinking
|
Substantial |
This course involves the utilization of methodologies from 2 different programming to solve problems that are complex enough to require programming. Consequently, critical and innovative thinking are necessary to be successful. There islikely more than one solution to every problem, and discernment will be learned by critical thinking students. |
|
2 |
International
Perspective
|
Substantial |
International enterprises use the skills introduced and developed in this course, and the thinking skills will translate well as students are required to contextualize their problem solving abilities. |
|
3 |
Communication
Skills
|
Substantial |
Students will be required to present their findings in an organized and comprehensible way, which will require additional communication skills beyond the technical toolset introduced. |
|
4 |
Decision-making |
Substantial |
Approaching problems in this course will require strong decision-making skills for the students to complete the assignments. Methods and tools will be presented, but the student must identify how and why to apply them. |
|
5 |
Technical Tools and Techniques |
Substantial |
This course presents tools and techniques laid out in the module summary. These tools include procedural programming and interfacing with structured data via SQL. |
|
6 |
Research Skills and Scholarship |
Substantial |
A helpful skill for any programmer is the ability to troubleshoot and diagnose a problem in their program. The instructor will guide what a problem might be, but will also instruct the student as to available resources for debugging. |
|
7 |
Professional Ethics and Standards |
Substantial |
The importance of professional ethics and standards will be emphasized through the submission of individual assignments, with special emphasis on individual student contributions to programming projects. |
|
8 |
Creative and Effective Leaders |
Substantial |
Facing an unknown problem with a set of programming skills and critical thinking abilities will prepare students to lead in a modern analytics environment. |
Course Outline and Course Calendar
|
Week Date |
Modules & Topics |
Readings |
Discussion Board |
To Do: In-Class and Between Classes Exercises & Assignments & Quizzes & Consultations |
|
W1
01/24 – 01/30
Class 1 on 1/24
|
MODULE 1
Lecture 1: Introduction to the Python language, installation, first programs, workspace options, current applications |
Module 1 Lecture Notes; Lubanovic, Ch:1-3 Thomas H, Ch: 10,11 |
Bb Discussion Forum W#0: Introduce Yourself Discussion Board W1 & W2 |
Discussion Board W1 & W2: Please provide a 150-200 word answer to the following question, along with any references. Also, please post a response to at least two (2) of your classmates: Pick a type of data structure that we introduced in the class (e.g., stack, queue, linked list, hash table, tree, etc.) and discuss its practical use case and the problems it can solve. |
|
W2
01/31 – 02/6
Class 2 on 01/31
|
MODULE 1
Lecture 2: Script programming, data types, variables, mathematics, list operations, data structures (stack, queue, linked list, hash table, tree)
|
Module 1
Lecture Notes; Lubanovic, Ch:1-3 Thomas H, Ch: 10,11
|
Discussion Board W1 & W2, Continued |
Quiz 1: available between Feb-05 at 9 am ET and Feb-06 at 11:59 pm ET |
|
W3
02/7 –02/13
Class 3 on 02/7
|
MODULE 2
Lecture 3: Modules, flow control, conditional logic, text and strings, formatting and printing, loops
|
Module 2
Lecture Notes; Lubanovic, Ch: 4-5
|
Discussion Board W3 & W4 |
Discussion Boad W3 & W4: Please provide a 150-200 word answer to the following question, along with any references. Also, please post a response to at least two (2) of your classmates: I assume you have already been able to generate errors in your code as you're working to develop programs. For 1 of the errors you've encountered so far, search stackoverflow.com for a possible solution, and report on what you find. Be sure to highlight any extraneous information you encountered, that is, how did you discern what was the correct answer for your problem? In-Class Discussion: Preparation for Individual Assignment 1 (due W4/D7) |
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W4
02/14 – 02/21
Class 4 on 02/14
|
MODULE 2
Lecture 4: Functions, tuples, lists, and dictionaries
|
Module 2
Lecture Notes; Lubanovic, Ch: 4-5
|
Discussion Board W3 & W4, Continued |
Quiz 2: available between Feb-20 at 9 am ET and Feb-21 at 11:59 pm ET
Individual Assignment 1, due on Feb-21 at 11:59 pm ET
|
|
W5
2/22 – 2/27
Class on 2/22 Tue
|
MODULE 3
Lecture 5: Advanced topics for functions, Introduction to Algorithms, Common Algorithms, Realization of Data Structures
|
Module 3
Lecture Notes; Lubanovic, Ch:6-7 Thomas H, Ch: 1, 3, 6, 10, 11
|
Discussion Board W5 & W6 |
Discussion Board W5 &W6: Please provide a 150-200 word answer to the following question, along with any references. Also, please post a response to at least two (2) of your classmates: Algorithms can be very interesting. Please pick an algorithm that you find interesting and discuss how it works and the problems it can solve.
In-Class Discussion: Preparation for Individual Assignment 2 (due W6/D7)
|
|
W6
02/28 – 03/4
Class on 02/28
SPRING RECESS 03/5 – 03/13
|
MODULE 3
Lecture 6: Numpy/Scipy (functions and capabilities), Pandas.
|
Module 3
Lecture Notes; Lubanovic, Ch:6-7 Thomas H, Ch: 1, 3, 6, 10, 11
|
Discussion Board W5 & W6, Continued |
Quiz 3: available between March-3 at 9 am ET and March-4 at 11:59 pm ET Individual Assignment 2, due on March-4 at 11:59 pm ET |
|
W7
03/14–03/20
Class 7 on 3/14
|
MODULE 4
Lecture 7:Statsmodel and Sklearn
|
Module 4
Lecture Notes; Trevor Hastie, Ch: 3, 7, 15
|
Discussion Board W7 & W8 |
Discussion Board W7 & W8: Please provide a 150-200 word answer to the following question, along with any references. Also, please post a response to at least two (2) of your classmates: We have learned several regression models. Please pick one model and discuss its advantages and disadvantages over other models. |
|
W8
03/21 – 03/27
Class 8 on 03/21
|
MODULE 4
Lecture 8: Regressions, Cross-Validation, and Model Selection
|
Module 4
Lecture Notes; Trevor Hastie, Ch: 3, 7, 15
|
Discussion Board W7 & W8, Continued Preparation for Team Term Project: Team Building & Guidelines for the Preparation of Team Term Project Phase 1 & Phase 2 |
Quiz 4: available between March-26 at 9 am ET and March-27 at 11:59 pm ET Individual Assignment 3, due on March-27 at 11:59 pm ET |
|
W9
03/28 – 04/03
Class 9 on 03/28
|
MODULE 5
Lecture 9: Database and SQL Introduction, basic syntax and environment, Creating and Populating Databases, basic queries and filtering
|
Module 5
Lecture Notes; Alan Beaulieu, Ch: 1-11
|
Discussion Board W9 & W1 |
Discussion Board W9 & W10: Please provide a 150-200 word answer to the following question, along with any references. Also, please post a response to at least two (2) of your classmates: We have so many handy tools, such as Excel, Google sheets, etc., which are easy to use and do not require any coding/querying background knowledge. Please discuss the advantage (or disadvantages) of SQL to one of the tools. |
|
W10
04/04 – 04/10
Class 10 on 04/04
|
MODULE 5
Lecture 10: Intermediate queries and subqueries, sets and joins, Grouping and Aggregation, Views
|
Module 5
Lecture Notes; Alan Beaulieu, Ch: 1-11
|
Discussion Board W9 & W10, Continued |
Individual Assignment 4, due on Apr-10 at 11:59 pm ET |
|
W11
04/11 – 04/19
Class 11 on 04/11
|
MODULE 6
Lecture 11: CTE and Window functions
|
e-Reserve, Course Pack, L11 |
|
Team Assignment Project, Phase 1, due on Apr-19 at 11:59 pm ET |
|
W12
04/20 – 04/24
Class 12 on 04/20 Wed
|
MODULE 6
Lecture 12: Integration of Python and SQL
|
e-Reserve, Course Pack, L12 |
|
Team Assignment Project, Phase 2, due on Apr-24 at 11:59 pm ET |
|
W13
04/25 – 05/01
Class 13 on 04/25
|
Course Review and Preparation for the Submission of the Final Team Project |
Bb Discussion Forum
W#13 & W#14: AD899 - Takeaways & Lessons Learned
|
|
Final Team Project Report, due on May-01 at 11:59 pm ET |
|
W14
Class 14 on 05/02 -05/04
|
Preparation for the Final Presentations |
|
|
Final In-Class Team Presentations, Session 1 |
|
Final Exam 05/09 |
|
|
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Final In-Class Team Presentations, Session 2 |