Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4
u1s1，这门课的assignment还是有点难度的，特别是assigment4（哀怨），放给大家参考啦~ 有时间（需求）就把所有代码放到github上（好担心被河蟹啊） 先放下该课程相关链接： Coursera | Introduction to Data Science in Python（University of Michigan）| quiz答案 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment1 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment2 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment3 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4 嘿，顺便推广下自己的 博客 ，以后CSDN的文章都会放到自己的博客的。 犹豫了下，还是把assignment4放上来吧，现在网上几乎么得代码，做的我头皮发麻，呜呜呜。但是收获还是很多的，大家加油！
Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4
Assignment 4, description.
In this assignment you must read in a file of metropolitan regions and associated sports teams from assets/wikipedia_data.html and answer some questions about each metropolitan region. Each of these regions may have one or more teams from the “Big 4”: NFL (football, in assets/nfl.csv), MLB (baseball, in assets/mlb.csv), NBA (basketball, in assets/nba.csvor NHL (hockey, in assets/nhl.csv). Please keep in mind that all questions are from the perspective of the metropolitan region, and that this file is the “source of authority” for the location of a given sports team. Thus teams which are commonly known by a different area (e.g. “Oakland Raiders”) need to be mapped into the metropolitan region given (e.g. San Francisco Bay Area). This will require some human data understanding outside of the data you’ve been given (e.g. you will have to hand-code some names, and might need to google to find out where teams are)!
For each sport I would like you to answer the question: what is the win/loss ratio’s correlation with the population of the city it is in? Win/Loss ratio refers to the number of wins over the number of wins plus the number of losses. Remember that to calculate the correlation with pearsonr , so you are going to send in two ordered lists of values, the populations from the wikipedia_data.html file and the win/loss ratio for a given sport in the same order. Average the win/loss ratios for those cities which have multiple teams of a single sport. Each sport is worth an equal amount in this assignment (20%*4=80%) of the grade for this assignment. You should only use data from year 2018 for your analysis – this is important!
- Do not include data about the MLS or CFL in any of the work you are doing, we’re only interested in the Big 4 in this assignment.
- I highly suggest that you first tackle the four correlation questions in order, as they are all similar and worth the majority of grades for this assignment. This is by design!
- There may be more teams than the assert statements test, remember to collapse multiple teams in one city into a single value!
For this question, calculate the win/loss ratio’s correlation with the population of the city it is in for the NHL using 2018 data.
For this question, calculate the win/loss ratio’s correlation with the population of the city it is in for the NBA using 2018 data.
For this question, calculate the win/loss ratio’s correlation with the population of the city it is in for the MLB using 2018 data.
For this question, calculate the win/loss ratio’s correlation with the population of the city it is in for the NFL using 2018 data.
In this question I would like you to explore the hypothesis that given that an area has two sports teams in different sports, those teams will perform the same within their respective sports . How I would like to see this explored is with a series of paired t-tests (so use ttest_rel ) between all pairs of sports. Are there any sports where we can reject the null hypothesis? Again, average values where a sport has multiple teams in one region. Remember, you will only be including, for each sport, cities which have teams engaged in that sport, drop others as appropriate. This question is worth 20% of the grade for this assignment.
所有assignment就这样结束啦，希望大家有所收获~ 大家其他还有需要的就在评论留言哦 😃 欢迎讨论分享~
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Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment2
u1s1，这门课的assignment还是有点难度的，特别是assigment4（哀怨），放给大家参考啦~ 有时间（需求）就把所有代码放到github上（好担心被河蟹啊） 相关链接： Coursera | Introduction to Data Science in Python（University of Michigan）| Quiz Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment1 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment2 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment3 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4 CSDN链接： Coursera | Introduction to Data Science in Python（University of Michigan）| Quiz答案 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment1 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment2 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment3 Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4
Assignment2
For this assignment you’ll be looking at 2017 data on immunizations from the CDC. Your datafile for this assignment is in assets/NISPUF17.csv. A data users guide for this, which you’ll need to map the variables in the data to the questions being asked, is available at assets/NIS-PUF17-DUG.pdf. Note: you may have to go to your Jupyter tree (click on the Coursera image) and navigate to the assignment 2 assets folder to see this PDF file).
Write a function called proportion_of_education which returns the proportion of children in the dataset who had a mother with the education levels equal to less than high school (<12), high school (12), more than high school but not a college graduate (>12) and college degree.
This function should return a dictionary in the form of (use the correct numbers, do not round numbers):
Let’s explore the relationship between being fed breastmilk as a child and getting a seasonal influenza vaccine from a healthcare provider. Return a tuple of the average number of influenza vaccines for those children we know received breastmilk as a child and those who know did not.
This function should return a tuple in the form (use the correct numbers:
This function should return a dictionary in the form of (use the correct numbers):
Note: To aid in verification, the chickenpox_by_sex()['female'] value the autograder is looking for starts with the digits 0.0077 .
A correlation is a statistical relationship between two variables. If we wanted to know if vaccines work, we might look at the correlation between the use of the vaccine and whether it results in prevention of the infection or disease [1]. In this question, you are to see if there is a correlation between having had the chicken pox and the number of chickenpox vaccine doses given (varicella).
Some notes on interpreting the answer. The had_chickenpox_column is either 1 (for yes) or 2 (for no), and the num_chickenpox_vaccine_column is the number of doses a child has been given of the varicella vaccine. A positive correlation (e.g., corr > 0 ) means that an increase in had_chickenpox_column (which means more no’s) would also increase the values of num_chickenpox_vaccine_column (which means more doses of vaccine). If there is a negative correlation (e.g., corr < 0 ), it indicates that having had chickenpox is related to an increase in the number of vaccine doses.
Also, pval is the probability that we observe a correlation between had_chickenpox_column and num_chickenpox_vaccine_column which is greater than or equal to a particular value occurred by chance. A small pval means that the observed correlation is highly unlikely to occur by chance. In this case, pval should be very small (will end in e-18 indicating a very small number).
[1] This isn’t really the full picture, since we are not looking at when the dose was given. It’s possible that children had chickenpox and then their parents went to get them the vaccine. Does this dataset have the data we would need to investigate the timing of the dose?
大家其他还有需要的就在评论留言哦 :) 欢迎讨论分享~
- 本文作者： 买猫咪的小鱼干
- 本文链接： https://ycchen00.github.io/2021/08/18/Coursera/Intro2DS/Assignment2/
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Applied Data Science with Python Specialization
Gain new insights into your data . Learn to apply data science methods and techniques, and acquire analysis skills.
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Conduct an inferential statistical analysis
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Introduction to data science in python.
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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.
Applied Plotting, Charting & Data Representation in Python
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 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 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.
Applied Machine Learning in Python
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.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.
Applied Text Mining in Python
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.
Instructors
Christopher Brooks
Kevyn Collins-Thompson
V. G. Vinod Vydiswaran
Daniel Romero
University of Michigan
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Introduction to Data Science in Python
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但是收获还是很多的，大家加油！ Coursera | Introduction to Data Science in Python（University of Michigan）| Assignment4. Assignment 4.
u1s1，这门课的assignment还是有点难度的，特别是assigment4（哀怨），放给大家参考啦~ 有时间（需求）就把所有代码放到github上（好担心被河蟹啊）
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.
problem to submit assignment 3 in course "introduction to data science in python". each time it gives different error.. last error -.
Provider. Coursera Help · Pricing. Free Online Course (Audit) · Languages. English · Certificate. Paid Certificate Available · Duration & workload. 4 weeks long, 35
Jupyter: Intro to Data Science - Assignment 2, Part 2 Details. Grant Long. CUNY City College. NYC Tech-in-Residence Corps.