Coursera specialization is a series of courses that help you master a particular skill. You can immediately sign up for specialization or look at the courses it consists of and choose the one you want to start with. By subscribing to a course that is part of the specialization, you automatically subscribe to the entire specialization. You can complete just one course, and then pause your studies or cancel your subscription at any time. Track your courses and progress in the student’s dashboard.

When you complete all machine learning courses and complete a hands-on project, you will receive a certificate that you can share with potential employers and colleagues.

## Mathematics and Python for data analysis

Data analysis and machine learning rely heavily on results from mathematical analysis, linear algebra, optimization methods, and probability theory. Without a fundamental knowledge of these sciences, it is impossible to understand how data analysis methods work. The goal of this course is to form such a foundation. We dispense with complex formulas and proofs and emphasize the interpretation and understanding of the meaning of mathematical concepts and objects. To successfully apply data analysis methods, you need to be able to program. The actual standard for this these days is Python. In this course, we offer to get acquainted with its syntax, as well as learn how to work with its main libraries useful for data analysis, for example, NumPy, SciPy, Matplotlib, and Pandas. The course videos are developed in Python 2. Assignments and notebooks adapted to Python 3.

## Search for structure in data

In machine learning, there are problems where you need to study the data structure, find hidden relationships and patterns in them. For example, we may need to describe each client of the bank using fewer variables – for this, we can use dimensional reduction methods based on matrix decompositions. Such methods try to form new features based on old ones, storing as much information as possible in the data. Another example is the problem of thematic modeling, in which for a set of texts you need to build a model that explains the process of forming these texts from a small number of topics.

In this course, you will learn about data clustering algorithms, with which, for example, you can search for groups of similar mobile operator clients. You will learn how to build matrix decompositions and solve the problem of thematic modeling, reduce the dimension of data, look for anomalies and visualize multidimensional data.

## Conclusion of data

Does knowledge of data analysis methods affect wages? Does the bank credit rating system work? Is the new banner better than the old one? To answer such questions, you need to collect data. The data almost always contain noise, so the statements that can be made on their basis are not always true, but only with a certain probability. You will learn everything you need to successfully turn data into conclusions – organizing experiments, A / B testing, universal methods for evaluating parameters and testing hypotheses, correlations, and cause-effect relationships. The course videos are developed in Python 2. Assignments and notebooks adapted to Python 3.