Build a Foundation for your Data Science Skills. Master a wide range of math underlying Data Science and learn how to apply it in practice

4.3

星

405 个评分

CalculusProbabilityDiscrete MathematicsLinear Algebra

32,681 次近期查看

Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.

Each course of the specialisation ends with a project that gives an opportunity to see how the material of the course is used in Data Science. Each project is directed at solving practical problem in Data Science. In particular, in your projects you will analyse social graphs, predict estate prices and uncover hidden relations in the data.

可分享的证书

完成后获得证书

100% 在线课程

立即开始，按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

初级

无需相关领域的预备知识无需相关经验。

完成时间大约为6 个月

建议 4 小时/周

英语（English）

字幕：英语（English）

可分享的证书

完成后获得证书

100% 在线课程

立即开始，按照自己的计划学习。

灵活的计划

设置并保持灵活的截止日期。

初级

无需相关领域的预备知识无需相关经验。

完成时间大约为6 个月

建议 4 小时/周

英语（English）

字幕：英语（English）

4.6

星

196 个评分

•

44 条评论

The main goal of this course is to introduce topics in Discrete Mathematics relevant to Data Analysis.

We will start with a brief introduction to combinatorics, the branch of mathematics that studies how to count. Basics of this topic are critical for anyone working in Data Analysis or Computer Science. We will illustrate new knowledge, for example, by counting the number of features in data or by estimating the time required for a Python program to run. Next, we will apply our knowledge in combinatorics to study basic Probability Theory. Probability is everywhere in Data Analysis and we will study it in much more details later. Our goals for probability section in this course will be to give initial flavor of this field. Finally, we will study the combinatorial structure that is the most relevant for Data Analysis, namely graphs. Graphs can be found everywhere around us and we will provide you with numerous examples. We will mainly concentrate in this course on the graphs of social networks. We will provide you with relevant notions from the graph theory, illustrate them on the graphs of social networks and will study their basic properties. In the end of the course we will have a project related to social network graphs. As prerequisites we assume only basic math (e.g., we expect you to know what is a square or how to add fractions), basic programming in Python (functions, loops, recursion), common sense and curiosity. Our intended audience are all people that work or plan to work in Data Analysis, starting from motivated high school students.

3.8

星

62 个评分

•

16 条评论

Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses.

Course starts with a basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus sequentially building up a base for the basic optimisation. To provide an understanding of the practical skills set being taught, the course introduces the final programming project considering the usage of optimisation routine in machine learning. Additional materials provided during the course include interactive plots in GeoGebra environment used during lectures, bonus reading materials with more general methods and more complicated basis for discussed themes.

4.2

星

45 个评分

•

6 条评论

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.

4.7

星

102 个评分

•

23 条评论

Exploration of Data Science requires certain background in probability and statistics. This course introduces you to the necessary sections of probability theory and statistics, guiding you from the very basics all way up to the level required for jump starting your ascent in Data Science.

The core concept of the course is random variable — i.e. variable whose values are determined by random experiment. Random variables are used as a model for data generation processes we want to study. Properties of the data are deeply linked to the corresponding properties of random variables, such as expected value, variance and correlations. Dependencies between random variables are crucial factor that allows us to predict unknown quantities based on known values, which forms the basis of supervised machine learning. We begin with the notion of independent events and conditional probability, then introduce two main classes of random variables: discrete and continuous and study their properties. Finally, we learn different types of data and their connection with random variables. While introducing you to the theory, we'll pay special attention to practical aspects for working with probabilities, sampling, data analysis, and data visualization in Python. This course requires basic knowledge in Discrete mathematics (combinatorics) and calculus (derivatives, integrals).

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communicamathematics, engineering, and more.

Learn more on www.hse.ru

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

Can I take the course for free?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

How long does it take to complete the Specialization?

Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-8 months.

What background knowledge is necessary?

As prerequisites we assume precollege level math, basic programming in python (functions, loops, recursion) and common sense. Our intended audience are all people that work or plan to work in Data Science.

Do I need to take the courses in a specific order?

We recommend taking the courses in the order presented, as each subsequent course uses some knowledge from previous courses.

Will I earn university credit for completing the Specialization?

Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. In the case of this particular Specialization the credit will be accepted by this masters program: https://www.coursera.org/degrees/master-of-data-science-hse

What will I be able to do upon completing the Specialization?

You will be able to understand mathematics behind Data Science. This will boost your skills in Data Analysis.

还有其他问题吗？请访问 学生帮助中心。