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Теория отраслевых рынков (Industrial Organization)

About this course: Курс посвящен факторам, влияющим на размер компаний и структуру рынка. Почему на одних рынках преобладают малые компании, а на другом крупные? Продавцы принимают решения стратегически, однако их стимулы в свою очередь зависят от структуры рынка и от предшествующих решений. Как разделить между зоной предопределенных и свободных решений? Например, сговор как модель ценового поведения – предопределен структурой рынка или служит результатом свободного волеизъявления? Способны ли укоренившиеся на рынке продавцы препятствовать входу новичков, защищая свою рыночную долю и свою прибыль? Каковы лучшие способы предотвращения ценовых сговоров продавцов? Нужно ли (или по крайней мере желательно) запрещать или ограничивать слияния между крупными продавцами? Есть ли необходимость для государственной политики налагать ограничения на условия договоров между производителем и дистрибьютором? Как в этих условиях должна быть организована государственная политика (применение антимоноп…

Introduction to Data Science in Python

Introduction to Data Science in Python

About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it,  manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Created by:   University of Michigan

How To PassPass all graded assignments to complete the course.
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Week 1
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.

11 videos2 readings
  1. Video: Introduction to Specialization
  2. Reading: Syllabus
  3. Video: Data Science
  4. Reading: 50 years of Data Science, David Donoho (optional)
  5. Video: The Coursera Jupyter Notebook System
  6. Notebook: Week 1 Lectures Jupyter Notebook
  7. Video: Python Functions
  8. Video: Python Types and Sequences
  9. Video: Python More on Strings
  10. Video: Python Demonstration: Reading and Writing CSV files
  11. Video: Python Dates and Times
  12. Video: Advanced Python Objects, map()
  13. Video: Advanced Python Lambda and List Comprehensions
  14. Video: Advanced Python Demonstration: The Numerical Python Library (NumPy)
Graded: Week One Quiz
Week 2
In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question.

8 videos
  1. Video: Introduction
  2. Notebook: Week 2 Lectures Jupyter Notebook
  3. Video: The Series Data Structure
  4. Video: Querying a Series
  5. Video: The DataFrame Data Structure
  6. Video: DataFrame Indexing and Loading
  7. Video: Querying a DataFrame
  8. Video: Indexing Dataframes
  9. Video: Missing Values
  10. Discussion Prompt: The Ethics of Using Hacked Data
  11. Notebook: Assignment 2
Graded: Assignment 2 Submission
Week 3
In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.

6 videos
  1. Notebook: Week 3 Lectures Jupyter Notebook
  2. Video: Merging Dataframes
  3. Video: Pandas Idioms
  4. Video: Group by
  5. Video: Scales
  6. Video: Pivot Tables
  7. Video: Date Functionality
  8. Discussion Prompt: Goodhart's Law
  9. Notebook: Assignment 3
Graded: Assignment 3 Submission
Week 4
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The majority of the week will be dedicated to your course project, where you'll engage in a real-world data cleaning activity and provide evidence for (or against!) a given hypothesis. This project is suitable for a data science portfolio, and will test your knowledge of cleaning, merging, manipulating, and test for significance in data. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.

4 videos
  1. Notebook: Week 4 Lectures Jupyter Notebook
  2. Video: Introduction
  3. Video: Distributions
  4. Video: More Distributions
  5. Video: Hypothesis Testing in Python
  6. Discussion Prompt: The End of Theory
  7. Discussion Prompt: Science Isn't Broken: p-hacking activity
  8. Notebook: Assignment 4 - Project
Graded: Assignment 4 Submission
How It Works
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
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