Enrichment classes are open to rising 1st-8th grade children and eligibility is based upon the grade a child is entering in Fall 2024. During the registration process families select one class per hour to create their child's enrichment class schedule for Session 2: July 1- July 12 (no camp on July 4).
The average class size is 10-14 campers with one teacher per class. A brief description of each class offered in Session 2 is included below.
Build the remainder of your child's summer camp schedule by reviewing enrichment class options for Session 1 (June 17-June 28) and Session 3 (July 15 - July 26).
Mad Science: READY, STEM, GO! (Session1)
In this program, budding engineers learn foundational science and find out how STEM is at work in the world around them! Campers learn about the animal kingdom, both on land and under the sea. They make mixtures and explore the science of motion and energy. Campers also discover the technology that powers our modern world. Classes include All About Animals, Energy Burst, Life in the Seas, Moving Motion, and more! Instructor: Mad Science
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Network ArchiTechsĀ
Campers will learn the fundamentals of how computers communicate with one another, as well as how data travels across the web. They will use a local area network that we will set up as a class to be able to learn these concepts in a hands-on capacity. Instructor: Danielius Rekasius
Survival Science
Emergencies can happen at any time! While the odds of being lost or stranded outdoors are pretty low, knowing what to do in an emergency is important. As survivalists, students learn exactly what to do in an emergency through the team-based study of real-world survival skills. From investigating the utility of tools to working through challenging situations, over this course, learners use ingenuity to gain crucial skills such as knot tying, water purification and compass navigation. Bringing STEM to the great outdoors, instructors love the hands-on, interactive and collaborative curriculum. With each thrilling activity, students gather their senses, put their new skills to the test and conquer every challenge Mother Nature throws their way! Instructor: Nexplore
Whether math is your passion or a stepping stone on your career path, ACC can help you Move Mountains toward your educational and career goals. We have dedicated faculty who are committed to a quality, student-centered, math education incorporating current trends and the latest technology. Get your Associate of Science Transfer degree in Mathematics full-time or part-time with convenient courses also offered nights and online and get your foundation to transfer to a 4-year college or university.
The purpose of a statewide articulation agreement is to identify the courses a student at a Colorado public community college must complete as part of an AA/AS degree to be guaranteed to be able to complete the designated baccalaureate degree program at public four-year colleges and universities as designated by the Colorado Department of Higher Education (CDHE) transfer agreement.
Please Note: If these credits are not required for the major at a receiving 4-year institution, they will be applied to the Bachelor's degree as elective credit towards graduation. Please check with the receiving institution to
determine in which way these courses will be applied.
* Note: CSU-Ft Collins requires a different computer science course than the community college course. Students should seek advising at CSU-Ft Collins for information on the appropriate computer science course to take.
Any additional courses taken in the discipline covered by this agreement might not count toward the requirements of the major at the receiving institution. Students are strongly encouraged to seek academic advising prior to registration regarding the acceptability of online science courses if they anticipate transferring to a 4-year institution or completing graduate work in the sciences or health professions. It should be noted that per Colorado Revised Statute, 23-1-125(1)(e), general education courses taken online are guaranteed to satisfy core course (gtPathways) requirements at all Colorado public institutions of higher education.
All courses required for this degree must be completed with a "C" or better to be considered for transfer to another Colorado institution. Transferability of courses to colleges or universities outside of Colorado is determined by the receiving institution.
ACC academic plans will help guide you through your chosen program. The academic plan will provide you with important information, like which semesters courses are offered if there are any prerequisites or co-requisites for courses, program outcomes, course sequence recommendations, notes for the program, graduation requirements, and other important information. These are updated annually, so we recommend downloading a copy of yours to keep on track to graduation.
Our Math Refreshers are a great opportunity to brush up on your math skills and prepare for your next math classes. ACC students can use the following steps to register for the Math Refresher Course.
Whether you need help with an assignment or a mathematical concept, our Math Support is free and open every semester to help you succeed. Get tutoring from our expert faculty and student tutors who have excelled in their classes. Can't make it to the Littleton Campus? We also have tutoring in Castle Rock and online through TutorMe.
This course is based on a book KD is writing, "Brain Computation: A Hands-on Guidebook" using Jupyter notebook with Python codes.
The course will be in a "flipped learning" style; each week, students read a draft chapter and experiment with sample codes before the class.
In the first class of the week, they present what they have learned and raise questions.
In the second class of the week, they 1) present a paper in the reference list, 2) solve exercise problem(s), 3) make a new exercise problem and solve it, or 4) propose revisions in the chapter.
Toward the end of the course, students work on individual or group projects by picking any of the methods introduced in the course and apply that to a problem of their interest.
Students are assumed to be familiar with Python, as covered in the Computational Methods course in Term 1, and basic statistics, as covered in the Statistical Tests and Statistical Modeling courses in Term 2.
This course develops advanced mathematical techniques for application in the natural sciences. Particular emphasis will be placed on analytical and numerical, exact and approximate methods, for calculation of physical quantities. Examples and applications will be drawn from a variety of fields. The course will stress calculational approaches rather than rigorous proofs. There will be a heavy emphasis on analytic calculation skills, which will be developed via problem sets.
The course is aimed at students interested in modeling systems characterized by stochastic dynamics in different branches of science. Goals of the course are: to understand the most common stochastic processes (Markov chains, Master equations, Langevin equations); to learn important applications of stochastic processes in physics, biology and neuroscience; to acquire knowledge of simple analytical techniques to understand stochastic processes, and to be able to simulate discrete and continuous stochastic processes on a computer.
1) Basic concepts of probability theory. Discrete and continuous distributions, main properties. Moments and generating functions. Random number generators.
2) Definition of a stochastic process and classification of stochastic processes. Markov chains. Concept of ergodicity. Branching processes and Wright-Fisher model in population genetics.
3) Master equations, main properties and techniques of solution. Gillespie algorithm. Stochastic chemical kinetics.
4) Fokker-Planck equations and Langevin equations. Main methods of solution. Simulation of Langevin equations. Colloidal particles in physics.
5) First passage-time problems. Concept of absorbing state and main methods of solution. First passage times in integrate-and-fire neurons.
6) Element of stochastic thermodynamics. Work, heat, and entropy production of a stochastic trajectory. Fluctuation relations, Crooks and Jarzynski relations.
A geometrically oriented introduction to the calculus of vector and tensor fields on three-dimensional Euclidean point space, with applications to the kinematics of point masses, rigid bodies, and deformable bodies. Aside from conventional approaches based on working with Cartesian and curvilinear components, coordinate-free treatments of differentiation and integration will be presented. Connections with the classical differential geometry of curves and surfaces in three-dimensional Euclidean point space will also be established and discussed.
Many physical processes exhibit some form of nonlinear wave phenomena. However diverse they are (e.g. from engineering to finance), however small they are (e.g. from atomic to cosmic scales), they all emerge from hyperbolic partial differential equations (PDEs). This course explores aspects of hyperbolic PDEs leading to the formation of shocks and solitary waves, with a strong emphasis on systems of balance laws (e.g. mass, momentum, energy) owing to their prevailing nature in Nature. In addition to presenting key theoretical concepts, the course is designed to offer computational strategies to explore the rich and fascinating world of nonlinear wave phenomena.
By the end of this course, participants dealing with wave-like phenomena in their research field of interest should be able to identify components that can trigger front-like structures (e.g. shocks, solitons) and be able to explore their motion numerically. Whilst the course is aimed at graduate students with an engineering/physics background, biologists interested in wave phenomena in biological systems (e.g. neurones, arteries, cells) are also welcome. However, it is assumed that participants have prior knowledge of maths for engineers and physicists. 152ee80cbc
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