Mathematics for Data Science. Part 1. Mathematical analysis and linear algebra - course RUB 26,990. from Specialist, training 40 academic hours, date of May 15, 2023.
Miscellaneous / / December 03, 2023
Professional programming course teacher, certified developer Python Institute with general work experience in the IT field more than 20 years. Built IT systems in 4 companies from scratch. More than 5 years.
Vadim Viktorovich graduated from the Russian State University for the Humanities in 2000 with a specialization in Informatics and Computer Science. A true professional in administration matters DBMS, automation of company business processes (ERP, CRM etc.), creating test cases and training employees.
Able to motivate and captivate. He is demanding of his listeners, always ready to clarify difficult points. Extensive experience working on real projects allows him to pay attention to those details that are usually overlooked by novice developers.
Module 1. Introduction to Jupiter Notebook (Python) (8 ac. h.)
Module 2. Introduction to mathematical analysis (16 ac. h.)
- Basic concepts of mathematical analysis. Item.
- Set theory (Probability spaces. Discrete space of elementary outcomes. Probability on the number line and plane. Rule of addition and multiplication).
- Metric spaces (The concept of metric space. Definition of a normed space, the concept of a norm, difference from a metric, examples of normed spaces. The norm in optimization).
- Sequences. Theory of limits (Cauchy's definition. Peano's definition. Calculation of function limits. Asymptotic functions. Equivalent functions. Function complexity assessment).
- Differentiation (Differentiability of a function at a point. Partial derivatives and differentials of higher orders. Gradient. Hessian matrix. Derivative of a function of one variable. Derivative of a function of several variables).
- Extrema of functions of many variables (Definitions of local and global minimum points. Necessary and sufficient condition for extremum for convex functions. The concept of stationary points and - the difference in their definition from extremum points).
- Integral (Indefinite integral. Definite integral. Applications of a definite integral and approximate methods for its calculation. Improper integrals. Double integrals. Approximate methods of integration).
- Rows (Concepts of rows. Convergence of series).
- Application of the studied sections of mathematical analysis using a general example (Jupiter notebook). Project.
Module 3. Linear algebra (16 ac. h.)
- Linear space.
- Matrices and matrix operations.
- Linear transformations.
- Systems of linear equations.
- Singular decomposition of matrices.
- Application of the studied sections of linear algebra on a general example (Jupiter notebook). Project.
Data science includes a wide range of approaches and methods for collecting, processing, analyzing and visualizing data sets of any size. A separate practically important area of this science is working with big data using new principles mathematical and computational modeling, when classical methods stop working due to their impossibility scaling. This course is designed to help the student learn the basics of the subject area through formulation and solving typical problems that a data science researcher may encounter in his or her work. To teach the student to solve such problems, the authors of the course provide the student with the necessary theoretical minimum and show how to use the tool base in practice.
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You will refresh your knowledge of mathematics, learn basic formulas and functions, and understand the basics of machine training and you can start a career in Data Science - IT companies around the world are looking for such specialists.
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