This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
關於此 專項課程
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學習Specialization的學生是
- Data Scientists
- Data Analysts
- Biostatisticians
- Process Analysts
- Data Engineers
您將學到的內容有
Create and interpret data visualizations using the Python programming language and associated packages & libraries
Apply and interpret inferential procedures when analyzing real data
Apply statistical modeling techniques to data (ie. linear and logistic regression, linear models, multilevel models, Bayesian inference techniques)
Understand importance of connecting research questions to data analysis methods.
您將獲得的技能
Python ProgrammingData Visualization (DataViz)Statistical ModelStatistical inference methods
專項課程的運作方式
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實踐項目
每個專項課程都包括實踐項目。您需要成功完成這個（些）項目才能完成專項課程並獲得證書。如果專項課程中包括單獨的實踐項目課程，則需要在開始之前完成其他所有課程。
獲得證書
在結束每門課程並完成實踐項目之後，您會獲得一個證書，您可以向您的潛在雇主展示該證書並在您的職業社交網絡中分享。
此專項課程包含 3 門課程
課程1
Understanding and Visualizing Data with Python
4.6
265 個評分
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. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. 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.
課程2
Inferential Statistical Analysis with Python
4.3
114 個評分
In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately. At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
課程3
Fitting Statistical Models to Data with Python
4.4
73 個評分
In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.
講師
Brenda Gunderson
Lecturer IV and Research FellowDepartment of Statistics
Brady T. West
Research Associate ProfessorInstitute for Social Research
Kerby Shedden
ProfessorDepartment of Statistics
關於 密歇根大学
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我可以免费学习课程吗？
此课程是 100% 在线学习吗？是否需要现场参加课程？
此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。
完成专项课程需要多长时间？
This specialization is made up of three courses, each with four weeks/modules. Each week in a course requires a commitment of roughly 3-6 hours, which will vary by learner.
What background knowledge is necessary?
High school-level algebra is the only background knowledge mandatory for the first course in the series. A basic Python and/or coding background is recommended.
Do I need to take the courses in a specific order?
It is definitely recommended to take this specialization in order.
完成专项课程后我会获得大学学分吗？
You will not earn University credit for completing this specialization.
What will I be able to do upon completing the Specialization?
Upon completion of all courses in this specialization, you will have a solid grasp of statistical analysis and will be able to conduct analyses using the Python programming language. You'll be able to create data visualizations in Python, as well as interpret and explain them. You will be able to utilize data for estimation and assessing theories, interpretation of inferential results, and you will be able to apply more advanced statistical modeling procedures.
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