Statistics with python university of michigan github

Statistics with python university of michigan github

This course is part of the Statistics with Python Specialization. In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data.

Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries.

Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera. 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.

In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.

In the second week of this course, we will be looking at graphical and numerical interpretations for one variable univariate data.

In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design.

Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.

Very helpful course for newcomer in data science studies. Great in clearing fundamentals for descriptive statistics, use of python to get these insights,plotting. Overall provide good learning curve. Great course to learn the basics!We use optional third-party analytics cookies to understand how you use GitHub. Learn more. You can always update your selection by clicking Cookie Preferences at the bottom of the page.

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Understanding and Visualizing Data with Python

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Instantly share code, notes, and snippets. Code Revisions 9 Forks 1. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. Assignment 4 - Hypothesis Testing. A recession is defined as starting with two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth. A recession bottom is the quarter within a recession which had the lowest GDP. A university town is a city which has a high percentage of university students compared to the total population of the city.

Hypothesis: University towns have their mean housing prices less effected by recessions. Run a t-test to compare the ratio of the mean price of houses in university towns the quarter before the recession starts compared to the recession bottom.

For this assignment, only look at GDP data from the first quarter of onward. For "State", removing characters from "[" to the end. For "RegionName", when applicable, removing every character from " " to the end. ExcelFile 'gdplev. This dataframe should be a dataframe with columns for q1 through q3, and should have a multi-index in the shape of ["State","RegionName"]. Note: Quarters are defined in the assignment description, they are not arbitrary three month periods.

The resulting dataframe should have 67 columns, and 10, rows. Then runs a ttest comparing the university town values to the non-university towns values, return whether the alternative hypothesis that the two groups are the same is true or not as well as the p-value of the confidence. The variable p should be equal to the exact p value returned from scipy.

The value for better should be either "university town" or "non-university town" depending on which has a lower mean price ratio which is equivilent to a reduced market loss. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

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statistics with python university of michigan github

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Course 1 - Understanding and Visualizing data with Python. View code. Releases No releases published.

statistics with python university of michigan github

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Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This repository will contain assignments and other self study material pertaining to the University of Michigan's Applied Data Science with Python Specialiation, which consists of the following courses:.

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We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 10 commits. Failed to load latest commit information. Interactive distributions. Project Figure. View code. Applied-Data-Science-with-Python-Specialization This repository will contain assignments and other self study material pertaining to the University of Michigan's Applied Data Science with Python Specialiation, which consists of the following courses: Introduction to Data Science in Python.

Applied Machine Learning in Python. Applied Text Mining in Python. About Applied Data Science with Python Specialization: University of Michigan Topics coursera coursera-machine-learning coursera-data-science coursera-assignment machine-learning data-science university-of-michigan coursera-python coursera-specialization.

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Save preferences.This module provides functions for calculating mathematical statistics of numeric Real -valued data. The module is not intended to be a competitor to third-party libraries such as NumPySciPyor proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab.

It is aimed at the level of graphing and scientific calculators. Unless explicitly noted, these functions support intfloatDecimal and Fraction. Behaviour with other types whether in the numeric tower or not is currently unsupported. Collections with a mix of types are also undefined and implementation-dependent. These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.

Note: The functions do not require the data given to them to be sorted. However, for reading convenience, most of the examples show sorted sequences.

university-of-michigan

The arithmetic mean is the sum of the data divided by the number of data points. It is a measure of the central location of the data. If data is empty, StatisticsError will be raised.

statistics with python university of michigan github

The mean is strongly affected by outliers and is not a robust estimator for central location: the mean is not necessarily a typical example of the data points. For more robust measures of central location, see median and mode. The sample mean gives an unbiased estimate of the true population mean, so that when taken on average over all the possible samples, mean sample converges on the true mean of the entire population. This runs faster than the mean function and it always returns a float. The data may be a sequence or iterable.

If the input dataset is empty, raises a StatisticsError. The geometric mean indicates the central tendency or typical value of the data using the product of the values as opposed to the arithmetic mean which uses their sum. Raises a StatisticsError if the input dataset is empty, if it contains a zero, or if it contains a negative value.

The harmonic mean, sometimes called the subcontrary mean, is the reciprocal of the arithmetic mean of the reciprocals of the data. If one of the values is zero, the result will be zero. The harmonic mean is a type of average, a measure of the central location of the data. It is often appropriate when averaging rates or ratios, for example speeds.

What is the average speed? StatisticsError is raised if data is empty, or any element is less than zero. The current algorithm has an early-out when it encounters a zero in the input. This means that the subsequent inputs are not tested for validity. This behavior may change in the future. If data is empty, StatisticsError is raised. The median is a robust measure of central location and is less affected by the presence of outliers.

When the number of data points is odd, the middle data point is returned:. When the number of data points is even, the median is interpolated by taking the average of the two middle values:. Return the low median of numeric data. The low median is always a member of the data set. When the number of data points is odd, the middle value is returned. When it is even, the smaller of the two middle values is returned. Use the low median when your data are discrete and you prefer the median to be an actual data point rather than interpolated.

statistics with python university of michigan github

Return the high median of data. The high median is always a member of the data set.Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills. This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.

The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library.

Applied Data Science with Python Specialization

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 be a tutorial 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 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 through a tutorial. 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. 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 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 textcleaning 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. 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.A repository for a course on the Pytho Specilization by Coursera py4e.

All the assignments of the Python for everybody specialization by University Of Michigan. A repository to store publicly available datasets about the University of Michigan, related to academics, administration, student life, financials, and more.

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