535.609.81 - Topics in Data Analysis

Expanded Course Description

This course will provide a survey of techniques for the extraction of information from data generated experimentally and computationally. The approach will emphasize the theoretical foundation for each topic followed by applications of each technique to sampled experimental or computational data. The student will be provided with software to gain experience with each tool to allow the student to then quickly adapt to other implementations found in common data analysis packages. Topics include: uncertainty analysis, data fitting, feedforward neural networks, probability density functions, Fourier analysis and FFT procedures, spectral analysis, digital filtering and designer curves. Projects will require some programming experience and familiarity with tools such as Matlab, Excel, and use of the command window.

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. All modules run for a period of seven (7) days. Assignments are required for most modules. Some are due at the conclusion of each module, whereas others are two-week assignments. Two course projects are assigned and are two-week or three week extended assignments. Four discussion activities are assigned; these are two-week activities with a due date at the end of each two-week period. A two-week exam, which is similar to an extended assignment, is conducted during modules 9-10. You should regularly check the Calendar, Course Outline and Announcements for due dates.

Course Topics

Module 1 -- Introduction
Module 2 -- Linear Physical Systems
Module 3 -- Probability Density Functions – Part 1
Module 4 -- Probability Density Functions – Part 2
Module 5 -- Uncertainty Analysis
Module 6 -- Data Fitting – Part 1
Module 7 -- Data Fitting – Part 2
Module 8 -- Neural Networks – Part 1
Module 9 -- Neural Networks – Part 2
Module 10 -- Curve Design
Module 11 -- Spectral Analysis – Part 1
Module 12 -- Spectral Analysis – Part 2
Module 13 -- Digital Filtering – Part 1
Module 14 -- Digital Filtering – Part 2

Course Goals

Experimental and computational data abound in our everyday lives and in the conduct of research.  A wide variety of analysis techniques exist that can be employed to gain information from the data.  The student will learn the theory underlying a series of data analysis tools and will find analytical solutions to problems to reinforce the concepts.  The student will then apply the theory by using provided software and data sets to gain experience with their use.  The analysis will be reinforced through the completion of two course projects, multiple module assignments, discussion activities and an exam.  When complete, the student will have a thorough introduction to modern data analysis techniques.

Course Learning Outcomes (CLOs)

• Explain the differences between deterministic and random data.
• Use probability density functions, correlation functions and spectral density functions to analyze random data and define the nature of the underlying phenomena.
• Use uncertainty analysis to design experiments correctly.
• Use least squares curve fits for single input / single output problems to serve as models and interpolators.
• Compute surface fits and fits with a general functional form by extending the theory for two input (and higher) / single output problems.
• Develop nonlinear models for multiple input / multiple output problems using feedforward neural networks.
• Develop curves that preserve continuity, differentiability and curvature when joined with other functions.
• Design nonrecursive digital filters.

Textbooks

Bendat, J. S. and Piersol, A. G. (2010). Random Data: Analysis & Measurement Procedures (4th ed.). Hoboken, NJ: Wiley-Interscience, ISBN: 978-0-470-24877-5.

Other Materials & Online Resources

The student that plans to pursue Data Analysis further will find the following texts to be helpful.  These are NOT required for this course.

• Bendat, J. S. (1998). Nonlinear System Techniques and Applications (2nd ed.). Hoboken, NJ: Wiley-Interscience, ISBN: 978-0-471-16576-7.  This is the companion volume to the course textbook that is oriented toward nonlinear applications.
• Coleman, H. W. and Steele, W. G. (2009). Experimentation, Validation and Uncertainty Analysis for Engineers (3rd ed.). New York, NY: John Wiley and Sons, ISBN: 0-470-16888-9.  An excellent text on the topic of uncertainty analysis.
• Draper, N.R. and Smith, H. (1998). Applied Regression Analysis (3rd ed.). New York, NY: John Wiley & Sons, ISBN: 0-471-17082-8, pp. 15-148.  Everything and anything on regression analysis is found in this text.
• Haykin, S. O. (2010). Neural Networks and Learning Machines. Upper Saddle River, Nj: Pearson Prentice-Hall, ISBN: 978-8-131-76377-3.  An excellent text on neural networks.
• James, J.F. (2011). A Student's Guide to Fourier Transforms (3rd ed.). New York, NY: Cambridge University Press, ISBN: 978-0-521-17683-5.  An excellent text oriented toward the student.
• Papoulis, A. (2002). Probability, Random Variables and Stochastic Processes (4th ed.). New York, NY: McGraw-Hill Book Co., ISBN: 978-0-071-22661-5.  A classic and somewhat more difficult text, but worth it.
• Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (1992). Numerical Recipes in Fortran, (2nd ed.). New York, NY: Cambridge University Press, ISBN: 0-521-43064-X.  Forget the fact that code examples are provided in Fortran.  This is a delightful book on numerical methods that has applications for data analysis and should be on everyone’s bookshelf!
• Ross, S. (2012). A First Course in Probability, (9th ed.). London, UK: Pearson Education Limited, ISBN: 978-0-321-79477-2.  A standard for the understanding of probability.
• Stearns, S. D. and Hush, D. R. (2011) Digital Signal Processing with Examples in MATLAB, (2nd ed.). Boca Raton, FL: CRC Press.  ISBN: 978-1-439-83782-5.  This is a classical text that has been updated to include examples in MATLAB.

Required Software

The use of a spreadsheet program is required.  All other software will be provided in the form of Fortran code and executables and MATLAB m-files.  MATLAB is required if the student wishes to use the m-files.  However, Fortran executables that require no additional software component may also be used.  In addition, some assignments require plots.  In some cases this can be accomplished by simply using a spreadsheet program that comes pre-installed on most computers.  The student may use any plotting software with which he/she is comfortable.  MATLAB can also be used to produce the plots.

Student Coursework Requirements

Each module is expected to require approximately 7-10 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the text, the lecture notes and reviewing supplied references (approximately 3 hours per week); viewing video lectures and movies (approximately 1 hour per week); and solving problem assignments (approximately 3-4 hours per week).  In addition, discussion activities are assigned during certain two-week periods and typically require 1-3 hours over the two-week period.  The two course projects may be expected to require 5 hours each over the course of several weeks.  The times vary of course with the skill level possessed by the student.

This course will consist of four basic student requirements:

1. Module Assignments (40% of Final Grade Calculation)

Most modules will require graded assignments consisting of problem sets that students will complete individually. The weekly assignments will consist of problems to be solved exercising concepts from the text or the instructor's notes. Additionally, the assignments will require the execution of programs for the analysis of data with comments on the results.  The problems will either be similar to example problems solved within each module or will be review problems exercising prerequisite concepts.

The problems will vary in difficulty; usually there will be one or two problems in each Assignment that are more challenging than the others. A maximum of 3 points per problem can be earned, with partial credit given as defined in the Module Assignments Rubric document.  You may use your text, instructor notes and any other reference material deemed necessary.

All assignments are due on the last day of the week in which the module is assigned unless the assignment spans multiple weeks.  In the latter case it will be due on the last day of the module in which it is to end.  Consult the Course Outline, the Calendar and the Announcements for details. Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructor).

At the end of the semester, the points earned from all of the Module Assignment problems will be summed to yield a single grade that will represent the Module Assignment portion of your total grade for the course. The grade ranges listed below are approximate and should be used for guidance. However, additional consideration is given at the end of the semester to students that routinely attempt and make progress on the more difficult problems, as these problems are intended to separate the ‘A’ students from the others.

• 96 - 86 pts: A
• 85 - 77 pts: B
• 76 - 67 pts: C
• 66 - 58 pts: D
• 57 - 0 pts: F

The Module Assignment portion of the total course grade is the largest single contribution because it permits students to demonstrate their mastery of the concepts. Therefore, the Instructor is looking for consistent and dedicated attention to the problems every week. The Instructor will accept and answer questions (by email or during office hours) on the approach to any of the problems. Problem sets will be graded and returned in a timely manner so that students may monitor their progress throughout the course. Solutions to each Module Assignment will be provided following submission.

2. Course Projects (Completed Individually) (25% of Final Grade Calculation)

Two projects will be assigned to provide extended exercise in applying software to samples of experimental and computational data and then to analyze the results.  The software will be provided.  Guidance for each project may be found in both the lecture notes as well as video lectures.  Continuing instructor guidance and feedback will occur throughout the course during office hours.  The first project is due in Module 5, and the second project is due in Module 14.

For each project the grade will be assessed as follows.  The number of points earned in the project divided by the maximum number of points for the project will be converted to a percentage.  The grade ranges listed below are typical and should be used for guidance. However, additional consideration will be given to those students that produce more accurate computations or more meaningful plots beyond the minimum requirements.

• 90 - 100 pts: A
• 80 - 89 pts: B
• 70 - 79 pts: C
• 60 - 69 pts: D
• 59 - 0 pts: F

3. Exam (25% of Final Grade Calculation)

The two-week Exam is a longer version of a module assignment, and it represents 25% of your total grade. It will be started in Module 9 and will be due on Day 7 of Module 10.  You should plan to spend substantial time with this assessment and put forth your best effort. The problems are to be solved individually and should not be discussed with other students. The exam contains seven problems that total to a maximum of 100 points.  Partial credit may be earned on each problem as defined in the Course Exam Rubric document.

This exam is designed to test your mastery of the concepts presented over the first nine modules. I will plan office hours in Module 11 with the purpose of discussing the exam with the students.  You will have two weeks for completion. The assessment will be graded and returned along with instructor solutions.  You may use your text, instructor notes and any other reference material deemed necessary.

For the exam the grade will be assessed as follows.  The number of points earned in the exam divided by the maximum number of points for the exam will be converted to a percentage.  Percentages that fall within the following ranges will earn the corresponding letter grade.

• 90 - 100 pts: A
• 80 - 89 pts: B
• 70 - 79 pts: C
• 60 - 69 pts: D
• 59 - 0 pts: F

The Exam portion of the total course grade is a large contribution because it permits students to demonstrate their cumulative mastery of concepts presented during the first nine modules. The instructor will accept and answer questions (by email or during the office hours session planned when the exam is distributed) on the description of any of the problems to ensure that students understand what is being asked for. The exam will be graded and returned in a timely manner so that students may monitor their progress throughout the course. Solutions will be provided following submission.

4. Discussion Activities (10% of Final Grade Calculation)

There will be four Discussion Activities in the course. Refer to the specific module for instructions regarding each discussion activity. The class will be divided into groups: A, B, C, etc. Each discussion activity will last for two weeks, and each group will be responsible for: producing a group response for the activity during an initial period (Days 1-7), participating in the discussion during the middle period (Days 8-11), and then producing an updated final submission during the remaining time (Days 12-14). The total number of points to be earned for all discussion activities will be 100 points. Each individual can score up to 25 points for a given activity.

Details regarding the posting of information and the assignment of partial credit may be found in the Discussion Activity Rubric document.

The grade ranges listed below are typical and should be used for guidance for the assessment of the discussion portion of the total grade.

• 90 - 100 pts: A
• 80 - 89 pts: B
• 70 - 79 pts: C
• 60 - 69 pts: D
• 59 - 0 pts: F

The Instructor will provide occasional oversight and feedback on Discussions. The Instructor will provide feedback on the final solution submitted by each group.

Grading Policy

Assignments are due on the dates posted in your Canvas course site. You may check these due dates in the Course Calendar or in the Assignments in the corresponding modules. I will typically post grades one to two weeks after assignment due dates.

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.  A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.  A grade of C or lower should be considered unacceptable for graduate level work.

The Engineering for Professionals program uses the following +/- grading system.

100–98 = A+
97–94 = A
93–90 = A−
89–87 = B+
86–83 = B
82–80 = B−
79–77 = C+
76–73 = C
72–70 = C−
69–67 = D+
66–63 = D
<63 = F

Final grades will be determined by the following weighting:

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.