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Brenda Gunderson,
Brady T. West,
and
Kerby Shedden
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/zb9474/inferential
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Syllabus
WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made.
On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. 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.
Read more
Syllabus
WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made.
On the Python side, we’ll review some high level concepts from the first course in this series, Python’s statistics landscape, and walk through intermediate level Python concepts. 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.
Read more
WEEK 2 - CONFIDENCE INTERVALS
In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
WEEK 3 - HYPOTHESIS TESTING
In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.
WEEK 4 - LEARNER APPLICATION
In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores basic principles of using data to make inferences
Focuses on interpreting inferential results appropriately
Teaches the construction of confidence intervals
Emphasizes categorical data as well as quantitative data
Instructs on conducting t-test and confidence intervals
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Save Inferential Statistical Analysis with Python to your list so you can find it easily later:
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Reviews summary
Statistical analysis and python fundamentals
Learners say this course is largely positive and has engaging assignments. It's designed for students with some statistical background or who have completed the previous courses in the specialization. Many reviewers noted that the instructors, especially Brady T. West, are highly rated for their clarity and enthusiasm. The course mainly focuses on confidence intervals and hypothesis testing in Python, with many real-world examples that help you understand the concepts in practice. However, students with no Python experience may find it challenging to keep up, as the course assumes basic familiarity with the language.
The course includes numerous real-world examples to help understand the concepts in practice.
"The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs, written assignment and quizzes."
Learners are satisfied with the variety of materials including lecture videos, readings, Jupyter notebooks, lab walkthroughs, written assignments, and quizzes.
"You will learn how to perform hypothesis tests for key areas such as"
"The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs, written assignment and quizzes."
Learners generally find the assignments engaging and helpful for practicing the concepts.
"Ample example python notebook files for students to reference"
"Challenging but achievable assignments"
The instructors, especially Brady T. West, are praised for their clarity, enthusiasm, and attention to detail.
"Brady T. West explains the concepts"
"Professor West is also making a great effort paying attention to details that every sound statistical analysis should follow but is often overlooked."
"The concepts gets etched in one's memory."
Students say the course requires a solid foundation in statistics and some experience with Python.
"A person without a background in python will struggle in this specialization because you need to have programing skill and experience"
"You need to have some prior experience with stats or a pre-college/college year 1 text book to accompany you if this is your first time learning stats."
The course assumes some familiarity with Python, which may be challenging for those with no programming experience.
"A person without a background in python will struggle in this specialization because you need to have programing skill and experience"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Inferential Statistical Analysis with Python with these
activities:
Develop study materials
Show steps
Solidify your understanding by creating your own study aids, such as flashcards, cheat sheets, or summaries.
Browse courses on
Statistics
Show steps
-
Identify key concepts and topics from the course.
-
Create study materials (e.g., flashcards, cheat sheets, summaries) based on your identified concepts.
-
Use your study materials to reinforce your understanding and prepare for assessments.
Practice using Python for statistical analysis
Show steps
Reinforce the statistical concepts covered in the course by applying them in a practical Python environment.
Browse courses on
Python
Show steps
-
Install Python and the necessary libraries (e.g., Statsmodels, Pandas, Seaborn).
-
Work through tutorials in the course environment to familiarize yourself with Python.
-
Practice using Python to analyze different types of data (e.g., categorical, quantitative).
-
Develop your own Python scripts for statistical analysis.
Participate in online discussion forums
Show steps
Engage with fellow learners to discuss statistical concepts, ask questions, and share insights.
Show steps
-
Join the course discussion forums.
-
Actively participate by posting questions, answering others, and engaging in discussions.
-
Share your understanding of statistical concepts and learn from others' perspectives.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Python libraries for statistical analysis
Show steps
Enhance your understanding of Python libraries by exploring their features and applications in statistical analysis.
Browse courses on
Python
Show steps
-
Identify additional Python libraries for statistical analysis (e.g., NumPy, Matplotlib).
-
Find and follow tutorials on how to use these libraries.
-
Apply your newfound knowledge in your Python scripts for statistical analysis.
Connect with professionals in the field
Show steps
Seek guidance and mentorship from experienced professionals to deepen your understanding and expand your network.
Show steps
-
Identify professionals in the field of statistics.
-
Reach out and connect with them via LinkedIn or email.
-
Request informational interviews or mentorship opportunities.
Create a statistical analysis report
Show steps
Demonstrate your understanding by analyzing real-world data, drawing conclusions, and presenting your findings effectively.
Browse courses on
Statistical Analysis
Show steps
-
Choose a dataset and research question.
-
Apply statistical techniques learned in the course to analyze the data.
-
Interpret your results and draw conclusions.
-
Create data visualizations to support your findings.
-
Write a comprehensive report summarizing your analysis and presenting your insights.
Contribute to open-source statistical projects
Show steps
Enhance your practical skills and contribute to the community by participating in open-source statistical projects.
Browse courses on
Statistics
Show steps
-
Identify open-source statistical projects (e.g., RStudio, SciPy).
-
Find ways to contribute to these projects (e.g., bug reporting, documentation).
-
Submit your contributions and actively engage with the project community.
Develop study materials
Show steps
Solidify your understanding by creating your own study aids, such as flashcards, cheat sheets, or summaries.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Identify key concepts and topics from the course.
- Create study materials (e.g., flashcards, cheat sheets, summaries) based on your identified concepts.
- Use your study materials to reinforce your understanding and prepare for assessments.
Practice using Python for statistical analysis
Show steps
Reinforce the statistical concepts covered in the course by applying them in a practical Python environment.
Browse courses on
Python
Show steps
Show steps
Show steps
- Install Python and the necessary libraries (e.g., Statsmodels, Pandas, Seaborn).
- Work through tutorials in the course environment to familiarize yourself with Python.
- Practice using Python to analyze different types of data (e.g., categorical, quantitative).
- Develop your own Python scripts for statistical analysis.
Participate in online discussion forums
Show steps
Engage with fellow learners to discuss statistical concepts, ask questions, and share insights.
Show steps
Show steps
Show steps
- Join the course discussion forums.
- Actively participate by posting questions, answering others, and engaging in discussions.
- Share your understanding of statistical concepts and learn from others' perspectives.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore Python libraries for statistical analysis
Show steps
Enhance your understanding of Python libraries by exploring their features and applications in statistical analysis.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify additional Python libraries for statistical analysis (e.g., NumPy, Matplotlib).
- Find and follow tutorials on how to use these libraries.
- Apply your newfound knowledge in your Python scripts for statistical analysis.
Connect with professionals in the field
Show steps
Seek guidance and mentorship from experienced professionals to deepen your understanding and expand your network.
Show steps
Show steps
Show steps
- Identify professionals in the field of statistics.
- Reach out and connect with them via LinkedIn or email.
- Request informational interviews or mentorship opportunities.
Create a statistical analysis report
Show steps
Demonstrate your understanding by analyzing real-world data, drawing conclusions, and presenting your findings effectively.
Browse courses on
Statistical Analysis
Show steps
Show steps
Show steps
- Choose a dataset and research question.
- Apply statistical techniques learned in the course to analyze the data.
- Interpret your results and draw conclusions.
- Create data visualizations to support your findings.
- Write a comprehensive report summarizing your analysis and presenting your insights.
Contribute to open-source statistical projects
Show steps
Enhance your practical skills and contribute to the community by participating in open-source statistical projects.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Identify open-source statistical projects (e.g., RStudio, SciPy).
- Find ways to contribute to these projects (e.g., bug reporting, documentation).
- Submit your contributions and actively engage with the project community.
Career center
Learners who complete Inferential Statistical Analysis with Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is someone who works with large amounts of data to extract meaningful insights. They use statistical techniques to analyze data and identify trends and patterns. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Data Analyst. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Data Analyst who wants to be able to make informed decisions based on data.
Statistician
Statistician
A Statistician is someone who collects, analyzes, interprets, and presents data. They use statistical techniques to draw conclusions about the world around them. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Statistician. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Statistician who wants to be able to make informed decisions based on data.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is someone who builds and deploys machine learning models. They use statistical techniques to analyze data and make predictions about the future. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Machine Learning Engineer. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Machine Learning Engineer who wants to be able to make informed decisions based on data.
Market Researcher
Market Researcher
A Market Researcher is someone who studies the market to understand the needs and wants of consumers. They use statistical techniques to analyze data and identify trends and patterns. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Market Researcher. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Market Researcher who wants to be able to make informed decisions based on data.
Financial Analyst
Financial Analyst
A Financial Analyst is someone who analyzes financial data to make investment recommendations. They use statistical techniques to analyze data and identify trends and patterns. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Financial Analyst. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Financial Analyst who wants to be able to make informed decisions based on data.
Data Scientist
Data Scientist
A Data Scientist is someone who uses data to solve problems. They use statistical techniques to analyze data and identify trends and patterns. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Data Scientist. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Data Scientist who wants to be able to make informed decisions based on data.
Business Analyst
Business Analyst
A Business Analyst is someone who analyzes business data to identify inefficiencies and opportunities. They use statistical techniques to analyze data and make recommendations for improvement. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Business Analyst. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Business Analyst who wants to be able to make informed decisions based on data.
Actuary
Actuary
An Actuary is someone who uses mathematics and statistics to assess risk and uncertainty. They use statistical techniques to analyze data and make predictions about the future. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Actuary. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Actuary who wants to be able to make informed decisions based on data.
Health Economist
Health Economist
A Health Economist is someone who uses economics to analyze health care issues. They use statistical techniques to analyze data and identify trends and patterns in health care data. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Health Economist. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Health Economist who wants to be able to make informed decisions based on data.
Software Engineer
Software Engineer
A Software Engineer is someone who designs, develops, and maintains software systems. They use statistical techniques to analyze data and improve the performance of software systems. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Software Engineer. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Software Engineer who wants to be able to make informed decisions based on data.
Data Engineer
Data Engineer
A Data Engineer is someone who designs, builds, and maintains data pipelines. They use statistical techniques to analyze data and improve the performance of data pipelines. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Data Engineer. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Data Engineer who wants to be able to make informed decisions based on data.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is someone who uses mathematics and statistics to analyze financial data. They use statistical techniques to identify trends and patterns in financial data and make predictions about the future. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Quantitative Analyst. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Quantitative Analyst who wants to be able to make informed decisions based on data.
Epidemiologist
Epidemiologist
An Epidemiologist is someone who studies the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. They use statistical techniques to analyze data and identify trends and patterns in health data. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Epidemiologist. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Epidemiologist who wants to be able to make informed decisions based on data.
Biostatistician
Biostatistician
A Biostatistician is someone who uses statistics to analyze biological data. They use statistical techniques to identify trends and patterns in biological data and make predictions about the future. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Biostatistician. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Biostatistician who wants to be able to make informed decisions based on data.
Research Scientist
Research Scientist
A Research Scientist is someone who conducts research in a particular field of science. They use statistical techniques to analyze data and make discoveries about the world around them. The Inferential Statistical Analysis with Python course can help you develop the skills you need to be a successful Research Scientist. This course will teach you how to use Python to analyze data, construct confidence intervals, and test hypotheses. These skills are essential for any Research Scientist who wants to be able to make informed decisions based on data.
For more career information including salaries, visit:
OpenCourser.com/course/zb9474/inferential
Reading list
We've selected 14 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Inferential Statistical Analysis with Python.
Provides a comprehensive introduction to statistical inference, covering both frequentist and Bayesian approaches. It valuable reference for students and researchers in statistics and related fields.
Provides a thorough introduction to the theory of statistics, covering topics such as probability, sampling, estimation, and hypothesis testing. It valuable reference for students and researchers in statistics and related fields.
Provides a comprehensive introduction to mathematical statistics, covering topics such as probability, random variables, sampling, estimation, and hypothesis testing. It valuable reference for students and researchers in statistics and related fields.
Provides a comprehensive introduction to Bayesian data analysis, covering topics such as Bayesian inference, model checking, and predictive modeling. It valuable reference for students and researchers in statistics and related fields.
Save
Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable reference for students and researchers in deep learning and related fields.
Provides a comprehensive introduction to Python for data analysis, covering topics such as data manipulation, data visualization, and machine learning. It valuable reference for students and researchers in data analysis and related fields.
Save
Provides a comprehensive introduction to R for data science, covering topics such as data manipulation, data visualization, and machine learning. It valuable reference for students and researchers in data science and related fields.
Provides a comprehensive introduction to data science, covering topics such as data manipulation, data visualization, and machine learning. It valuable reference for students and researchers in data science and related fields.
Provides a comprehensive introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It valuable reference for students and researchers in machine learning and related fields.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for students and researchers in statistical learning and related fields.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for students and researchers in statistical learning and related fields.
Provides a comprehensive introduction to machine learning with R, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable reference for students and researchers in machine learning and related fields.
Provides a comprehensive introduction to deep learning with Python, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable reference for students and researchers in deep learning and related fields.
Provides a comprehensive introduction to natural language processing with Python, covering topics such as text classification, text summarization, and text generation. It valuable reference for students and researchers in natural language processing and related fields.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zb9474/inferential
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Effort
4 weeks of study, 4-6 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructors
Brenda Gunderson
Brady T. West
Kerby Shedden
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Statistics with Python. It's comprised of three courses:
- Inferential Statistical Analysis with Python (viewing)
- Fitting Statistical Models to Data with Python
- Understanding and Visualizing Data with Python
Good to know
Explores basic principles of using data to make inferences
Focuses on interpreting inferential results appropriately
Teaches the construction of confidence intervals
Emphasizes categorical data as well as quantitative data
Instructs on conducting t-test and confidence intervals
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