Mathematics for Data Science. Part 2. Probability theory and mathematical statistics - course RUB 27,990. 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. Basic concepts of probability theory. Examples (4 ac. h.)
Module 2. Random events. Conditional probability. Bayes' formula. Independent tests (4 ac. h.)
Module 3. Discrete random variables. Law of probability distribution. Binomial distribution law. Poisson distribution (4 ac. h.)
Module 4. Descriptive statistics. Qualitative and quantitative characteristics of the population. Graphical presentation of data (4 ak. h.)
Module 5. Continuous random variables. Distribution function and probability density function. Uniform and normal distribution. Central limit theorem (4 ak. h.)
Module 6. Testing statistical hypotheses. P-values. Confidence intervals. (4 ac. h.)
Module 7. Relationship between quantities. Parametric and nonparametric correlation measures. Correlation analysis. (4 ac. h.)
Module 8. Multivariate statistical analysis. Linear regression (4 ac. h.)
Module 9. Analysis of variance. Logistic regression (4 ac. h.)
Module 10. Application of the studied sections of probability theory and mathematical statistics on a general example (Jupiter notebook). Project. (4 ac. h.)
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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