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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…

Applied Data Science with Python

Applied Data Science with Python

About This Specialization

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have basic a python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
Created by:
courses
5 courses
Follow the suggested order or choose your own.
projects
Projects
Designed to help you practice and apply the skills you learn.
certificates
Certificates
Highlight your new skills on your resume or LinkedIn.
Courses
Intermediate Specialization.
Some related experience required.

  1. COURSE 1

    Introduction to Data Science in Python

    Upcoming session: Jul 3 — Aug 7.
    Subtitles
    English

    About the 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.
    Show or hide details about course Introduction to Data Science in Python

    WEEK 1
    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.
    Video · Introduction to Specialization
    Reading · Syllabus
    Reading · Help us learn more about you!
    Video · Data Science
    Reading · 50 years of Data Science, David Donoho (optional)
    Video · The Coursera Jupyter Notebook System
    Other · Week 1 Lectures Jupyter Notebook
    Video · Python Functions
    Video · Python Types and Sequences
    Video · Python More on Strings
    Video · Python Demonstration: Reading and Writing CSV files
    Video · Python Dates and Times
    Video · Advanced Python Objects, map()
    Video · Advanced Python Lambda and List Comprehensions
    Video · Advanced Python Demonstration: The Numerical Python Library (NumPy)
    Quiz · Week One Quiz

    WEEK 2
    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.
    Video · Introduction
    Other · Week 2 Lectures Jupyter Notebook
    Video · The Series Data Structure
    Video · Querying a Series
    Video · The DataFrame Data Structure
    Video · DataFrame Indexing and Loading
    Video · Querying a DataFrame
    Video · Indexing Dataframes
    Video · Missing Values
    Other · The Ethics of Using Hacked Data
    Other · Assignment 2
    Programming Assignment · Assignment 2 Submission

    WEEK 3
    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.
    Other · Week 3 Lectures Jupyter Notebook
    Video · Merging Dataframes
    Video · Pandas Idioms
    Video · Group by
    Video · Scales
    Video · Pivot Tables
    Video · Date Functionality
    Other · Goodhart's Law
    Other · Assignment 3
    Programming Assignment · Assignment 3 Submission

    WEEK 4
    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.
    Other · Week 4 Lectures Jupyter Notebook
    Video · Introduction
    Video · Distributions
    Video · More Distributions
    Video · Hypothesis Testing in Python
    Other · The End of Theory
    Other · Science Isn't Broken: p-hacking activity
    Other · Assignment 4 - Project
    Programming Assignment · Assignment 4 Submission

  2. COURSE 2

    Applied Plotting, Charting & Data Representation in Python

    Current session: Jun 26 — Jul 31.
    Subtitles
    English

    About the Course

    This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will describe the gamut of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
    Show or hide details about course Applied Plotting, Charting & Data Representation in Python

    WEEK 1
    Module 1: Principles of Information Visualization
    In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.
    Video · Introduction
    Reading · Syllabus
    Reading · Help us learn more about you!
    Video · About the Professor: Christopher Brooks
    Video · Tools for Thinking about Design (Alberto Cairo)
    Other · Hands-on Visualization Wheel
    Video · Graphical heuristics: Data-ink ratio (Edward Tufte)
    Reading · Dark Horse Analytics (Optional)
    Video · Graphical heuristics: Chart junk (Edward Tufte)
    Reading · Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts
    Video · Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)
    Video · The Truthful Art (Alberto Cairo)
    Other · Must a visual be enlightening?
    Reading · Graphics Lies, Misleading Visuals
    Peer Review · Graphics Lies, Misleading Visuals

    WEEK 2
    Module 2: Basic Charting
    In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.
    Other · Module 2 Jupyter Notebook
    Video · Introduction
    Video · Matplotlib Architecture
    Reading · Matplotlib
    Reading · Ten Simple Rules for Better Figures
    Video · Basic Plotting with Matplotlib
    Video · Scatterplots
    Video · Line Plots
    Video · Bar Charts
    Video · Dejunkifying a Plot
    Other · Plotting Weather Patterns
    Peer Review · Plotting Weather Patterns

    WEEK 3
    Module 3: Charting Fundamentals
    In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.
    Other · Module 3 Jupyter Notebook
    Video · Subplots
    Video · Histograms
    Reading · Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional)
    Video · Box Plots
    Video · Heatmaps
    Video · Animation
    Video · Interactivity
    Other · Practice Assignment: Understanding Distributions Through Sampling
    Practice Peer Review · Practice Assignment: Understanding Distributions Through Sampling
    Other · Building a Custom Visualization
    Reading · Assignment Reading
    Peer Review · Building a Custom Visualization

    WEEK 4
    Module 4: Applied Visualizations
    In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.
    Other · Module 4 Jupyter Notebook
    Video · Plotting with Pandas
    Video · Seaborn
    Reading · Spurious Correlations
    Video · Becoming an Independent Data Scientist
    Other · Project Description
    Peer Review · Becoming an Independent Data Scientist

  3. COURSE 3

    Applied Machine Learning in Python

    Upcoming session: Jul 3 — Aug 7.
    Subtitles
    English

    About the Course

    This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
    Show or hide details about course Applied Machine Learning in Python

    WEEK 1
    Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
    This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
    Reading · Course Syllabus
    Reading · Help us learn more about you!
    Video · Introduction
    Video · Key Concepts in Machine Learning
    Video · Python Tools for Machine Learning
    Other · Module 1 Notebook
    Video · An Example Machine Learning Problem
    Video · Examining the Data
    Video · K-Nearest Neighbors Classification
    Reading · Zachary Lipton: The Foundations of Algorithmic Bias (optional)
    Quiz · Module 1 Quiz
    Other · Assignment 1
    Programming Assignment · Assignment 1 Submission

    WEEK 2
    Module 2: Supervised Machine Learning - Part 1
    This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
    Other · Module 2 Notebook
    Video · Introduction to Supervised Machine Learning
    Video · Overfitting and Underfitting
    Video · Supervised Learning: Datasets
    Video · K-Nearest Neighbors: Classification and Regression
    Video · Linear Regression: Least-Squares
    Video · Linear Regression: Ridge, Lasso, and Polynomial Regression
    Video · Logistic Regression
    Video · Linear Classifiers: Support Vector Machines
    Video · Multi-Class Classification
    Video · Kernelized Support Vector Machines
    Video · Cross-Validation
    Video · Decision Trees
    Reading · A Few Useful Things to Know about Machine Learning
    Reading · Ed Yong: Genetic Test for Autism Refuted (optional)
    Quiz · Module 2 Quiz
    Other · Classifier Visualization Playspace
    Other · Assignment 2
    Programming Assignment · Assignment 2 Submission

    WEEK 3
    Module 3: Evaluation
    This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
    Other · Module 3 Notebook
    Video · Model Evaluation & Selection
    Video · Confusion Matrices & Basic Evaluation Metrics
    Video · Classifier Decision Functions
    Video · Precision-recall and ROC curves
    Video · Multi-Class Evaluation
    Video · Regression Evaluation
    Reading · Practical Guide to Controlled Experiments on the Web (optional)
    Video · Model Selection: Optimizing Classifiers for Different Evaluation Metrics
    Quiz · Module 3 Quiz
    Other · Assignment 3
    Programming Assignment · Assignment 3 Submission

    WEEK 4
    Module 4: Supervised Machine Learning - Part 2
    This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
    Other · Module 4 Notebook
    Video · Naive Bayes Classifiers
    Video · Random Forests
    Video · Gradient Boosted Decision Trees
    Video · Neural Networks
    Reading · Neural Networks Made Easy (optional)
    Reading · Play with Neural Networks: TensorFlow Playground (optional)
    Video · Deep Learning (Optional)
    Reading · Deep Learning in a Nutshell: Core Concepts (optional)
    Reading · Assisting Pathologists in Detecting Cancer with Deep Learning (optional)
    Video · Data Leakage
    Reading · The Treachery of Leakage (optional)
    Reading · Leakage in Data Mining: Formulation, Detection, and Avoidance (optional)
    Reading · Data Leakage Example: The ICML 2013 Whale Challenge (optional)
    Reading · Rules of Machine Learning: Best Practices for ML Engineering (optional)
    Quiz · Module 4 Quiz
    Other · Assignment 4
    Programming Assignment · Assignment 4 Submission
    Other · Unsupervised Learning Notebook
    Video · Introduction
    Video · Dimensionality Reduction and Manifold Learning
    Video · Clustering
    Reading · How to Use t-SNE Effectively
    Reading · How Machines Make Sense of Big Data: an Introduction to Clustering Algorithms
    Video · Conclusion

  4. COURSE 4

    Applied Text Mining in Python

    Starts July 24, 2017
    Subtitles
    English

    About the Course

    This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
  5. COURSE 5

    Applied Social Network Analysis in Python

    Starts August 2017
    Subtitles
    English

    About the Course

    This course will introduce the learner to network modelling through the networkx toolset. Used to model knowledge graphs and physical and virtual networks, the lens will be social network analysis. The course begins with an understanding of what network modelling is (graph theory) and motivations for why we might model phenomena as networks. The second week introduces the networkx library and discusses how to build and visualize networks. The third week will describe metrics as they relate to the networks and demonstrate how these metrics can be applied to graph structures. The final week will explore the social networking analysis workflow, from problem identification through to generation of insight. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Creators

Michigan’s academic vigor offers excellence across disciplines and around the globe. The University is recognized as a leader in higher education due to the outstanding quality of its 19 schools and colleges, internationally recognized faculty, and departments with 250 degree programs.

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.

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