The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. Another goal is to improve the student’s practical skills of using linear algebra methods in machine learning and data analysis. You will learn the fundamentals of working with data in vector and matrix form, acquire skills for solving systems of linear algebraic equations and finding the basic matrix decompositions and general understanding of their applicability. This course is suitable for you if you are not an absolute beginner in Matrix Analysis or Linear Algebra (for example, have studied it a long time ago, but now want to take the first steps in the direction of those aspects of Linear Algebra that are used in Machine Learning). Certainly, if you are highly motivated in study of Linear Algebra for Data Sciences this course could be suitable for you as well. This Course is part of HSE University Master of Data Science degree program. Learn more about the admission into the program and how your Coursera work can be leveraged if accepted into the program here https://inlnk.ru/rj64e.

Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in R professional certificate. This course is directed at people with limited statistical background and no practical experience, who have to do data analysis, as well as those who are “out of practice”. While very practice oriented, it aims to give the students the understanding of why the method works (theory), how to implement it (programming using R) and when to apply it (and where to look if the particular method is not applicable in the specific situation).

Updated on March 2018 The highest rated Mental Math course. Joan B says "Very clear explanations and easy to understand... highly recommend this course... " Chhandak B says "Simple explanation with proper logic" Nir C says "Very helpful and fun" 'Mental Calculations Made Easy'. As the name suggests mental math calculations are made easy by attending this course.  The course is designed in such a way that it can be easily understood even by small children. This course is developed with an aim to encourage young minds to learn mathematics so that it will not be a burden for them. It will help children who get nightmares before attending Mathematics examination. They will be confident enough to attend exams without any difficulty and score good  marks. Chapters are included as videos. The duration of the course is approximately 2.5 hours. So if you want to excel in mental math I would recommend that this will be a great course for you. All the basic mental math operations(Addition,subtraction,multiplication and division) are covered in this course.Moreover the technique of calculating day from a date in any year is also explained in detail.The explanations are in detail. After attending this course you will be a mental math expert.

In this course, we go beyond the calculus textbook, working with practitioners in social, life and physical sciences to understand how calculus and mathematical models play a role in their work. Through a series of case studies, you’ll learn: How standardized test makers use functions to analyze the difficulty of test questions; How economists model interaction of price and demand using rates of change, in a historical case of subway ridership; How an x-ray is different from a CT-scan, and what this has to do with integrals; How biologists use differential equation models to predict when populations will experience dramatic changes, such as extinction or outbreaks; How the Lotka-Volterra predator-prey model was created to answer a biological puzzle; How statisticians use functions to model data, like income distributions, and how integrals measure chance; How Einstein’s Energy Equation, E=mc2 is an approximation to a more complicated equation. With real practitioners as your guide, you’ll explore these situations in a hands-on way: looking at data and graphs, writing equations, doing calculus computations, and making educated guesses and predictions. This course provides a unique supplement to a course in single-variable calculus. Key topics include application of derivatives, integrals and differential equations, mathematical models and parameters. This course is for anyone who has completed or is currently taking a single-variable calculus course (differential and integral), at the high school (AP or IB) or college/university level. You will need to be familiar with the basics of derivatives, integrals, and differential equations, as well as functions involving polynomials, exponentials, and logarithms. This is a course to learn applications of calculus to other fields, and NOT a course to learn the basics of calculus. Whether you’re a student who has just finished an introductory Calculus course or a teacher looking for more authentic examples for your classroom, there is something for you to learn here, and we hope you’ll join us!

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations. Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. These courses make up two Professional Certificates and are self-paced: Data Analysis for Life Sciences: PH525.1x: Statistics and R for the Life Sciences PH525.2x: Introduction to Linear Models and Matrix Algebra PH525.3x: Statistical Inference and Modeling for High-throughput Experiments PH525.4x: High-Dimensional Data Analysis Genomics Data Analysis: PH525.5x: Introduction to Bioconductor PH525.6x: Case Studies in Functional Genomics PH525.7x: Advanced Bioconductor This class was supported in part by NIH grant R25GM114818.

Introduction to the mathematical concept of networks, and to two important optimization problems on networks: the transshipment problem and the shortest path problem. Short introduction to the modeling power of discrete optimization, with reference to classical problems. Introduction to the branch and bound algorithm, and the concept of cuts.

Differential equations are the mathematical language we use to describe the world around us. Most phenomena can be modeled not by single differential equations, but by systems of interacting differential equations. These systems may consist of many equations. In this course, we will learn how to use linear algebra to solve systems of more than 2 differential equations. We will also learn to use MATLAB to assist us. We will use systems of equations and matrices to explore: The original page ranking systems used by Google, Balancing chemical reaction equations, Tuned mass dampers and other coupled oscillators, Threeor more species competing for resources in an ecosystem, The trajectory of a rider on a zipline. The five modules in this seriesare being offered as an XSeries on edX. Please visit the Differential EquationsXSeries Program Page to learn more and to enroll in the modules. *Zipline photo by teanitiki on Flickr (CC BY-SA 2.0)

More than 2000 years ago, long before rockets were launched into orbit or explorers sailed around the globe, a Greek mathematician measured the size of the Earth using nothing more than a few facts about lines, angles, and circles. This course will start at the very beginnings of geometry, answering questions like "How big is an angle?" and "What are parallel lines?" and proceed up through advanced theorems and proofs about 2D and 3D shapes. Along the way, you'll learn a few different ways to find the area of a triangle, you'll discover a shortcut for counting the number of stones in the Great Pyramid of Giza, and you'll even come up with your own estimate for the size of the Earth. In this course, you'll be able to choose your own path within each lesson, and you can jump between lessons to quickly review earlier material. GeometryX covers a standard curriculum in high school geometry, and CCSS (common core) alignment is indicated where applicable. Learn more about our High School and AP* Exam Preparation Courses This course was funded in part by the Wertheimer Fund.

Preparing for the AP Calculus AB exam requires a deep understanding of many different topics in calculus as well as an understanding of the AP exam and the types of questions it asks. This course is Part 1 of our XSeries: AP Calculus AB and it is designed to prepare you for the AP exam. In Part 2, you will use and apply the meaning and interpretations of derivatives from Part 1 to the integral, antiderivatives and differential equations. You will learn some applications of integrals including finding volumes of solids and solids of revolution, volumes with known cross sections and applications to Velocity-Time graphs. We will close with an introduction to differential equations and see how they are used. As you work through this course, you will find lecture videos taught by expert AP calculus teachers, practice multiple choice questions and free response questions that are similar to what you will encounter on the AP exam and tutorial videos that show you step-by-step how to solve problems. By the end of the course, you should be ready to take on the AP exam!

Vector Calculus for Engineers covers both basic theory and applications. In the first week we learn about scalar and vector fields, in the second week about differentiating fields, in the third week about multidimensional integration and curvilinear coordinate systems. The fourth week covers line and surface integrals, and the fifth week covers the fundamental theorems of vector calculus, including the gradient theorem, the divergence theorem and Stokes’ theorem. These theorems are needed in core engineering subjects such as Electromagnetism and Fluid Mechanics. Instead of Vector Calculus, some universities might call this course Multivariable or Multivariate Calculus or Calculus 3. Two semesters of single variable calculus (differentiation and integration) are a prerequisite. The course is organized into 53 short lecture videos, with a few problems to solve following each video. And after each substantial topic, there is a short practice quiz. Solutions to the problems and practice quizzes can be found in instructor-provided lecture notes. There are a total of five weeks to the course, and at the end of each week there is an assessed quiz. Download the lecture notes: http://www.math.ust.hk/~machas/vector-calculus-for-engineers.pdf Watch the promotional video: https://youtu.be/qUseabHb6Vk