Math and Python for Data Analysis

Description: The course is aimed at developing the skills and abilities to apply the methods of mathematical analysis, linear algebra, optimization methods, probability theory for data analysis problems, as well as to gain basic skills in working with special Python data analysis libraries.

Amount of credits: 6

Пререквизиты:

• Of Informatively-communication technologies

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• Acquire theoretical and practical skills in applying the methods of mathematical analysis, linear algebra, optimization methods, probability theory for data analysis problems, as well as to gain basic skills in working with special Python data analysis libraries.

Objective

• 1. Understanding and applying the basic mathematical concepts used for data analysis 2. application in practice of the methods of linear algebra, mathematical analysis, optimization and probability theory 3. Acquisition of practical skills in working with the main libraries that are used in practice for data analysis: NumPy, SciPy, Matplotlib and Pandas 4. obtaining basic practical skills in data analysis

Learning outcome: knowledge and understanding

• 1 knowledge of basic mathematical concepts used for data analysis 2. knowledge of NumPy, SciPy, Matplotlib and Pandas libraries

Learning outcome: applying knowledge and understanding

• 1. application of basic mathematical concepts used for data analysis 2. application in practice of the methods of linear algebra, mathematical analysis, optimization and probability theory 3. Acquisition of practical skills in working with the main data analysis library 4. Getting basic practical skills in data analysis in Python

Learning outcome: formation of judgments

• 1. forming judgments about the mathematical methods used for data analysis 2. formation of judgments about the methods of linear algebra, mathematical analysis, optimization and probability theory 3. Formation of a judgment on the practical application of the NumPy, SciPy, Matplotlib and Pandas libraries for data analysis

Learning outcome: communicative abilities

• 1. development and improvement of communicative abilities of students; 2. development of skills to participate in a constructive dialogue about the role and importance of artificial intelligence systems in the modern world, various areas in artificial intelligence systems

Learning outcome: learning skills or learning abilities

• 1. formation of skills in the field of artificial intelligence systems for the implementation of research work 2. development of skills in applying the methods of linear algebra, mathematical analysis, optimization and probability theory 3. the ability to contribute, within academic and professional contexts, to technological, social or cultural development in the interest of building a knowledge society

Teaching methods

1. lectures and online lectures, laboratory classes using slides and other multimedia tools.

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Введение в Python
• IPython: интерактивные вычисления и среда разработки
• Основы NumPy: массивы и векторные вычисления
• Структуры данных pandas
• Чтение и запись данных, форматы файлов
• Переформатирование данных: очистка, преобразование, слияние, изменение формы
• Построение графиков и визуализация
• Агрегирование данных и групповые операции
• Временные ряды
• Финансовые и экономические приложения
• Дополнительные сведения о библиотеке NumPy

Key reading

• Маккинли. Уэс Python и анализ данных / Уэс Маккинли ; пер. А. А. Слинкин. - Москва : ДМК Пресс, 2015. - 481 с. : ил.
• Максимова, О. Д. Математический анализ в примерах и задачах. Предел функции : учебное пособие для вузов / О. Д. Максимова. — 2-е изд., стер. — Москва : Издательство Юрайт, 2019. — 200 с.
• Марчук, Г.И. Геронтология in silico. Становление новой дисциплины. Математические модели, анализ данных и вычислительные эксперименты / Г.И. Марчук. - М.: Бином. Лаборатория знаний, 2017. - 929 c.
• Златопольский Д.М. Основы программирования на языке Python. – М.: ДМК Пресс, 2017. – 284 с.
• Мэтиз, Э. Изучаем PYTHON.Программирование игр, визуализация данных, веб-приложения / Э. Мэтиз. - СПб.: Питер, 2017. - 496 c.
• Федоров, Д. Ю. Программирование на языке высокого уровня Python : учебное пособие для прикладного бакалавриата / Д. Ю. Федоров. – 2-е изд., перераб. и доп. – Москва : Издательство Юрайт, 2019. – 161 с. – (Бакалавр. Прикладной курс).

Further reading

• Информационные технологии и вычислительные системы: Обработка информации и анализ данных. Программная инженерия. Математическое моделирование. Прикладные аспекты информатики / Под ред. С.В. Емельянова. - М.: Ленанд, 2015. - 104 c.
• Гашев С. Н. Математические методы в биологии: анализ биологических данных в системе Statistica. — М.: Юрайт. 2020. 208 с.
• Халафян А. А., Боровиков В. П., Калайдина Г. В. Теория вероятностей, математическая статистика и анализ данных. Основы теории и практика на компьютере. Statistica. Excel. Более 150 примеров решения задач. Учебное пособие. — М.: Ленанд. 2017. 320 с.
• Миркин Б. Г. Введение в анализ данных. — М.: Юрайт. 2020. 175 с.