Upcoming Paid Online Courses (4)
This course is about modeling and how computer models can support managerial decision making. A model is a simplified representation of a real situation and modeling is the process of developing, analyzing and interpreting a model in order to help make better decisions. Models can be invaluable tools in managing and understanding the complexity and risk inherent in many business problems. As a result, models have become an increasingly important part of business at all levels from daily operations to strategic decision making.
This course will help learners become intelligent users and consumers of these models. To this end, we will cover the basic elements of modeling – how to formulate a model and how to use and interpret the information a model produces. The course emphasizes “learning by doing” so that students will be expected to formulate, solve, and interpret a number of different optimization and simulation models using Excel spreadsheets. An important theme in the course is to understand the appropriate use of models in business and the potential pitfalls from using models incorrectly or inappropriately.
The course has two distinct parts:
- The first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.
- In the second half, we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.
In this introductory course, you will learn programming with Java in an easy and interactive way.
You will learn about fundamental data structures, such as lists, stacks, queues and trees, and presents algorithms for inserting, deleting, searching and sorting information on these data structures in an efficient way.
Emphasis is put on immediate feedback and on having a fun experience. Programming knowledge is not only useful to be able to program today’s devices such as computers and smartphones. It also opens the door to computational thinking, i.e. the application of computing techniques to every-day processes.
This course is designed taking into account the subset and recommendations of the College Board in order to prepare learners for the Advanced Placement (AP) Computer Science A exam.
How did Newton describe the orbits of the planets? To do this, he created calculus. But he used a different coordinate system more appropriate for planetary motion. We will learn to shift our perspective to do calculus with parameterized curves and polar coordinates. And then we will dive deep into exploring the infinite to gain a deeper understanding and powerful descriptions of functions.
How does a computer make accurate computations? Absolute precision does not exist in the real world, and computers cannot handle infinitesimals or infinity. Fortunately, just as we approximate numbers using the decimal system, we can approximate functions using series of much simpler functions. These approximations provide a powerful framework for scientific computing and still give highly accurate results. They allow us to solve all sorts of engineering problems based on models of our world represented in the language of calculus.
- Changing Perspectives
- Parametric Equations
- Polar Coordinates
- Series and Polynomial Approximations
- Series and Convergence
- Taylor Series and Power Series
This course, in combination with Parts 1 and 2, covers the AP* Calculus BC curriculum.
This course was funded in part by the Wertheimer Fund.
*Advanced Placement and AP are registered trademarks of the College Board, which was not involved in the production of, and does not endorse, these offerings.
In data science, data is called “big” if it cannot fit into the memory of a single standard laptop or workstation.
The analysis of big datasets requires using a cluster of tens, hundreds or thousands of computers. Effectively using such clusters requires the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and corresponding computational models, such as Hadoop, MapReduce and Spark.
In this course, part of the Data Science MicroMasters program, you will learn what the bottlenecks are in massive parallel computation and how to use spark to minimize these bottlenecks.
You will learn how to perform supervised an unsupervised machine learning on massive datasets using the Machine Learning Library (MLlib).
In this course, as in the other ones in this MicroMasters program, you will gain hands-on experience using PySpark within the Jupyter notebooks environment.
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