What is the difference between 101 and 102 classes

A remark that doesn't warrant a full answer: some schools, such as community colleges in Texas, use a four-digit system, i. As an example, MATH is a junior level, three-credit course. When I was at Univ. Graduates only courses actually means you have to get permission if you're an undergraduate started at However, I just looked and they don't use the same numbering system now. But Harvard still has a similar numbering system in place. For example, Math 55 at Harvard would probably not be considered an introductory course!

Higher 10x courses would be for slightly more advanced content or slight variations, e. Show 1 more comment. Active Oldest Votes. Improve this answer. Mark Meckes Mark Meckes 2, 16 16 silver badges 19 19 bronze badges. Sometimes, "" is a course intended for non-specialists of the subject. For students planning to proceed in Psych, there would be a or others. At one large University, as I recall, Physics was a science credit for Arts students, and there were other 10x courses specifically intended for majors in Engineering, Medicine, Pharmacy, and others--each separate, I think, so as to be scheduled compatibly with the courses of those specialties.

Actual Physics majors took Physics And at the low end of things, 0-level courses are typically remedial, non-credit courses covering things the student didn't learn in high school but should have, eg. Mark That is not a "typical" system. Some schools use numbers below that way, but many don't.

Again, while the first digit often has significance, which numbers mean what varies a lot. Sometimes, existed in the past, but got split or combined with another course, or various other things, and the number never got reused.

And to this answer's point about gaps, there were no other level CS courses — Izkata. Add a comment. Based on my experiences on a few schools, here are the consistent patterns I'm aware of: Course numbers are typically three digit numbers The first digit does typically indicate the level of the course, with 1XX courses being lower level than 2XX courses and so on, but the significance of the first digit can vary wildly 4XX courses could be undergrad courses or upper level grad courses at different schools, for instance.

Some of the principles that lead to choosing specific numbers are: Sometimes consecutive courses do get numbered consecutively, so and might form a related sequence. Conversely, my experience is that when courses don't form a natural sequence, they rarely but not never get consecutive numbers, to avoid confusion: there are always many gaps in the numbering system.

Sometimes the second digit has significance - it might be that courses whose second number is a 4, regardless of level, are all inorganic chemisty, so is the first inorganic chemistry course while is the number of a graduate seminar in inorganic chemistry. Often when a course is removed or dramatically changed, its number will be retired for a time: it would be confusing if meant very different things for people graduating from the same school in the same year because they took the course in two different years.

Henry Henry Another principle that is sometimes used: odd numbers for the fall semester courses, even numbers for the spring semester courses.

Re 4XX courses, what I've seen is that the same course will be open to grad or undergrads. Undergrads sign up for 4XX, grads for 6XX - same course, same instructor, perhaps a few extra assignments required of 6XX. Then 7XX courses are generally grad-level only. MichaelSeifert Or vice versa. I've seen the evens for sone semester and odds for another pretty frequently, but I haven't seen one be more common than the other.

I've never been at a school which routinely used 7XX numbers, but I can certainly believe they exist. The last thing you'll read about a course is its description. A course description is a general explanation of its topics and teaching methodology. So even if a course you are considering has the same title as a course at your target school, be sure to examine the descriptions of each for similar terms and topics to get a feel for how well-aligned the content really is.

You also know more about college course codes than you ever wanted to. How do you start actually taking affordable transfer credit? However, if you think you're ready to go it alone, here are some other resources that may help you along the way:. Transfer credit series pt. A former student counselor and Accelerated Pathways student, Abigail is now a writer and Accelerated Pathways Content Manger who's passionate about empowering others to achieve their goals.

How College Course Codes Work Colleges use course codes to describe and organize their courses in a way that can be easily understood by both colleges and students if said students have translation guides, that is.

Course Prefix The first part of a college course code is simple: a series of letters indicating the course's general subject. Abigail Endsley A former student counselor and Accelerated Pathways student, Abigail is now a writer and Accelerated Pathways Content Manger who's passionate about empowering others to achieve their goals. Tips for career counselors, Ph. Opinions on Inside Higher Ed.

Spring Forward. Resisting the Panopticon. Veterans Day, November Friday Fragments. Learning Innovation. In-Between Times. Higher Ed Gamma. Can These Colleges Be Saved? Tackling Transfer. Policy Education and Public Advocacy. The course will introduce data analysis from the Bayesian perspective to undergraduate students.

We will cover important concepts in Bayesian probability modeling as well as estimation using both optimization and simulation-based strategies. Key topics covered in the course include hierarchical models, mixture models, hidden Markov models and Markov Chain Monte Carlo. This course is the usual entry point in the actuarial science program. It is required for students who plan to concentrate or minor in actuarial science.

It can also be taken by others interested in the mathematics of personal finance and the use of mortality tables. For future actuaries, it provides the necessary knowledge of compound interest and its applications, and basic life contingencies definition to be used throughout their studies. Non-actuaries will be introduced to practical applications of finance mathematics, such as loan amortization and bond pricing, and premium calculation of typical life insurance contracts.

Main topics include annuities, loans and bonds; basic principles of life contingencies and determination of annuity and insurance benefits and premiums. This specialized course is usually only taken by Wharton students who plan to concentrate in actuarial science and Penn students who plan to minor in actuarial mathematics. It provides a comprehensive analysis of advanced life contingencies problems such as reserving, multiple life functions, multiple decrement theory with application to the valuation of pension plans.

This course covers models for insurer's losses, and applications of Markov chains. Poisson processes, including extensions such as non-homogeneous, compound, and mixed Poisson processes are studied in detail.

The compound model is then used to establish the distribution of losses. An extensive section on Markov chains provides the theory to forecast future states of the process, as well as numerous applications of Markov chains to insurance, finance, and genetics.

The course is abundantly illustrated by examples from the insurance and finance literature. While most of the students taking the course are future actuaries, other students interested in applications of statistics may discover in class many fascinating applications of stochastic processes and Markov chains.

This course will introduce a high-level programming language, called R, that is widely used for statistical data analysis. Using R, we will study and practice the following methodologies: data cleaning, feature extraction; web scrubbing, text analysis; data visualization; fitting statistical models; simulation of probability distributions and statistical models; statistical inference methods that use simulations bootstrap, permutation tests.

Prerequisite: Waiving the Statistics Core completely if prerequisites are not met. Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression.

Text mining especially through PCA is another topic of the course. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class.

Function estimation and data exploration using extensions of regression analysis: smoothers, semiparametric and nonparametric regression, and supervised machine learning. Conceptual foundations are addressed as well as hands-on use for data analysis.

This course will cover the design and analysis of sample surveys. Topics include simple sampling, stratified sampling, cluster sampling, graphics, regression analysis using complex surveys and methods for handling nonresponse bias. This course will expose students to the theoretical and empirical "building blocks" that will allow them to construct, estimate, and interpret powerful models of consumer behavior.

Over the years, researchers and practitioners have used these models for a wide variety of applications, such as new product sales, forecasting, analyses of media usage, and targeted marketing programs. Other disciplines have seen equally broad utilization of these techniques.