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Jen Rose
and
Lisa Dierker
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Hypothesis Testing and ANOVA
This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.
Read more
Syllabus
Hypothesis Testing and ANOVA
This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Focuses on hypothesis testing, ANOVA, Chi-Square, and Pearson Correlation Analysis, which are fundamental statistical concepts
Teaches learners how to apply statistical tests to specific data and research questions, developing their analytical skills
Emphasizes the use of statistical software packages (SAS or Python), providing learners with practical experience
Provides opportunities for learners to share their progress and gain feedback from peers, fostering a collaborative learning environment
Involves learners actively in hypothesis testing and statistical analysis, promoting hands-on learning
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Reviews summary
Practical statistical data analysis skills
According to learners, this course provides a positive introduction to key data analysis techniques. Students found the coverage of statistical tests like ANOVA and Chi-Square to be well-explained, offering a solid foundation. The option to work with either SAS or Python is appreciated, with many praising the practical exercises and labs, particularly within the Python environment, as directly applicable to work. However, some found the pace to be fast and assignment instructions occasionally vague. There are also mentions that the SAS materials felt somewhat outdated compared to Python, and that obtaining timely instructor support could be difficult. Despite minor criticisms, the overall feedback highlights the course's value in demystifying concepts and providing practical skills.
Learn with SAS or Python options.
"I appreciated having the option to use SAS or Python, though the SAS materials felt a bit outdated..."
"The choice between SAS and Python is a plus, catering to different professional needs."
Explanations found easy to grasp.
"The explanations were clear, and the practical exercises using Python were very helpful."
"Fantastic introduction to data analysis... The lectures were well-structured, and the assignments reinforced the concepts perfectly."
"Solid course for beginners... The ANOVA and Chi-Square modules were particularly well explained."
"Highly recommend it. The way they explain hypothesis testing makes it easy to understand."
Hands-on practice reinforces learning.
"The practical exercises using Python were very helpful. It provided a solid foundation..."
"The hands-on tasks were useful, but sometimes the instructions were a bit vague..."
"...the assignments reinforced the concepts perfectly."
"The Python labs were excellent and directly applicable to my work."
"The hands-on labs are the best part. Very practical and relevant."
Coverage of statistical interactions limited.
"...the coverage of interactions felt superficial, and I had to seek external resources for a deeper understanding."
Getting help was reported as difficult.
"...getting timely support from the instructors was difficult."
"Support was minimal. Expected more depth for the price."
Some assignment instructions lack clarity.
"...sometimes the instructions were a bit vague, requiring extra research."
"...the instructions for the assignments weren't always clear."
"Some assignment instructions could be more detailed to avoid confusion."
SAS content/environment felt less modern.
"...the SAS materials felt a bit outdated compared to the Python ones."
"I used SAS, and while functional, the interface felt clunky compared to modern tools."
"I struggled with the SAS environment."
Moves fast, assumes some stats knowledge.
"...I found it moved quite fast. It assumes a basic understanding of statistics..."
"The material felt rushed, and the instructions for the assignments weren't always clear."
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 Data Analysis Tools with these
activities:
Review Basic Statistics
Show steps
Strengthen your foundation in statistics by reviewing basic concepts.
Browse courses on
Probability
Show steps
-
Review lecture notes or textbooks on basic statistics.
-
Solve practice problems to test your understanding.
-
Consult with a tutor or instructor if needed.
Organize Course Materials
Show steps
Stay organized and improve your learning by compiling and reviewing course materials.
Show steps
-
Gather all course materials, including lecture notes, assignments, and quizzes.
-
Organize the materials into a logical structure.
-
Review the materials regularly to reinforce your understanding.
Explore Statistical Software
Show steps
Become familiar with statistical software to enhance your data analysis capabilities.
Browse courses on
SAS
Show steps
-
Choose a statistical software package (e.g., SAS, Python).
-
Find tutorials on the software's website or other online resources.
-
Follow the tutorials to learn the basics of the software.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Form a Study Group
Show steps
Enhance your learning by collaborating with peers in a study group.
Show steps
-
Find a group of peers who are interested in forming a study group.
-
Set regular meeting times and a study schedule.
-
Review course material together, discuss concepts, and work on assignments.
Practice Statistical Tests
Show steps
Reinforce your understanding of statistical tests by completing practice problems.
Browse courses on
Hypothesis Testing
Show steps
-
Review the lecture material on the statistical test you want to practice.
-
Find practice problems online or in textbooks.
-
Solve the practice problems and check your answers.
Conduct a Research Project
Show steps
Develop and test your own research question using the statistical methods learned in this course.
Browse courses on
Hypothesis Testing
Show steps
-
Identify a research question relevant to your interests and the course material.
-
Design a study to test your research question.
-
Collect data using appropriate methods.
-
Analyze your data using the statistical tests learned in this course.
-
Write a report summarizing your findings.
Create a Presentation on Statistical Analysis
Show steps
Showcase your knowledge of statistical analysis by creating a presentation that explains a statistical concept or method.
Browse courses on
Data Visualization
Show steps
-
Choose a statistical concept or method to present.
-
Research the topic and gather relevant information.
-
Create a presentation that includes clear explanations, examples, and visuals.
-
Practice your presentation and deliver it to a group.
Create a Statistical Model
Show steps
Deepen your understanding of statistical modeling by creating your own statistical model.
Browse courses on
Regression Analysis
Show steps
-
Identify a problem that can be solved using statistical modeling.
-
Collect and prepare data.
-
Choose an appropriate statistical model.
-
Build and train the model.
-
Evaluate the performance of the model.
Review Basic Statistics
Show steps
Strengthen your foundation in statistics by reviewing basic concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on basic statistics.
- Solve practice problems to test your understanding.
- Consult with a tutor or instructor if needed.
Organize Course Materials
Show steps
Stay organized and improve your learning by compiling and reviewing course materials.
Show steps
Show steps
Show steps
- Gather all course materials, including lecture notes, assignments, and quizzes.
- Organize the materials into a logical structure.
- Review the materials regularly to reinforce your understanding.
Explore Statistical Software
Show steps
Become familiar with statistical software to enhance your data analysis capabilities.
Browse courses on
SAS
Show steps
Show steps
Show steps
- Choose a statistical software package (e.g., SAS, Python).
- Find tutorials on the software's website or other online resources.
- Follow the tutorials to learn the basics of the software.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Form a Study Group
Show steps
Enhance your learning by collaborating with peers in a study group.
Show steps
Show steps
Show steps
- Find a group of peers who are interested in forming a study group.
- Set regular meeting times and a study schedule.
- Review course material together, discuss concepts, and work on assignments.
Practice Statistical Tests
Show steps
Reinforce your understanding of statistical tests by completing practice problems.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Review the lecture material on the statistical test you want to practice.
- Find practice problems online or in textbooks.
- Solve the practice problems and check your answers.
Conduct a Research Project
Show steps
Develop and test your own research question using the statistical methods learned in this course.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Identify a research question relevant to your interests and the course material.
- Design a study to test your research question.
- Collect data using appropriate methods.
- Analyze your data using the statistical tests learned in this course.
- Write a report summarizing your findings.
Create a Presentation on Statistical Analysis
Show steps
Showcase your knowledge of statistical analysis by creating a presentation that explains a statistical concept or method.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a statistical concept or method to present.
- Research the topic and gather relevant information.
- Create a presentation that includes clear explanations, examples, and visuals.
- Practice your presentation and deliver it to a group.
Create a Statistical Model
Show steps
Deepen your understanding of statistical modeling by creating your own statistical model.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Identify a problem that can be solved using statistical modeling.
- Collect and prepare data.
- Choose an appropriate statistical model.
- Build and train the model.
- Evaluate the performance of the model.
Career center
Learners who complete Data Analysis Tools will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data. They use their findings to help businesses make informed decisions. This course can help you in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Market Researcher
Market Researcher
Market Researchers gather and interpret data about consumers and markets. They use their findings to help businesses develop new products and marketing campaigns. This course can help you in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop recommendations. This course can help you in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They work in a variety of industries, including finance, insurance, and healthcare. This course can help you in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use their findings to help businesses and organizations make informed decisions. This course can help you in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They work with stakeholders to gather requirements, analyze data, and develop solutions. This course can help you may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models. They work with data scientists to gather data, train models, and evaluate results. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users to gather requirements, design systems, and write code. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. They work with clients to gather information, analyze data, and develop investment strategies. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical models to solve business problems. They work with businesses to improve efficiency, reduce costs, and make better decisions. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. They work with businesses to develop strategies to mitigate risks and protect their assets. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze medical studies. They work with researchers to develop new treatments and improve patient care. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Epidemiologist
Epidemiologist
Epidemiologists study the causes and distribution of diseases. They use statistical methods to identify risk factors and develop prevention strategies. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
Survey Researcher
Survey Researcher
Survey Researchers design, conduct, and analyze surveys. They work with businesses and organizations to collect data about their customers, employees, or other stakeholders. This course may be useful in this role by providing you with the skills you need to: test hypotheses about your data, apply the appropriate statistical tests to your specific data and question, and use statistical software packages (SAS or Python).
For more career information including salaries, visit:
OpenCourser.com/course/am6h86/data
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
Data Analysis Tools.
More advanced text that provides a comprehensive overview of statistical methods. It covers a wide range of topics, including hypothesis testing, ANOVA, regression analysis, and non-parametric tests. This great resource for those who want to learn more about statistics, or for those who need a reference book for their research.
Classic text that provides a comprehensive overview of statistical methods for the social sciences. It covers a wide range of topics, including hypothesis testing, ANOVA, regression analysis, and non-parametric tests. This book great resource for those who want to learn more about statistics, or for those who need a reference book for their research.
Save
Provides a comprehensive overview of econometrics. It covers a wide range of topics, including regression analysis, time series analysis, and forecasting. It takes an applied approach, using real-world examples to illustrate the concepts discussed.
Provides an overview of modern statistical learning methods. It covers a wide range of topics, including linear models, support vector machines, and tree-based methods. The book is written in a clear and accessible style, making it a good resource for researchers and students.
More specialized text that focuses on the application of statistics to financial engineering. It covers topics such as time series analysis, risk management, and portfolio optimization. This book great resource for those who want to learn about the use of statistics in the financial industry.
Introduces regression and multilevel/hierarchical models through hands-on examples. It uses the statistical software R to demonstrate the methods discussed.
Save
Focuses on applying data mining techniques to real-world problems. It covers topics such as data collection and preparation, exploratory data analysis, and model selection. The book provides a good blend of theory and practice, making it a valuable resource for both researchers and practitioners.
Provides a detailed overview of Bayesian data analysis. It covers a wide range of topics, including Bayesian inference, model selection, and Markov chain Monte Carlo methods.
Provides an introduction to the theory of generalized linear models (GLMs). GLMs are an important class of statistical models used in many applications. The book includes several examples and exercises, making it a great resource for researchers and students.
Covers commonly used statistical models for categorical and limited dependent variables such as logistic regression, ordered logit, and Poisson regression. It discusses both frequentist and Bayesian approaches along with how to interpret and present results.
Provides a detailed overview of time series analysis. It covers a wide range of topics, including stationarity, autocorrelation, and forecasting. The book takes a more technical approach than some of the other books on this list, but it provides a comprehensive understanding of time series analysis.
Provides a comprehensive overview of pattern recognition and machine learning. It begins with a review of basic concepts and then covers a range of topics, including linear and nonlinear models, statistical decision theory, andBayesian inference. The book is written clearly and provides plenty of examples and exercises.
For those using Python, this book covers the basics of Python and its powerful data analysis capabilities. In particular, it covers the use of the Pandas and NumPy libraries for data wrangling and analysis.
For those using R, this book covers the fundamentals of R and describes a variety of advanced functionality including a section on applying R to data analysis and graphics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/am6h86/data
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Rating
4.5
Effort
10 hours to complete
Via
Coursera
Institution
Wesleyan University
Instructors
Jen Rose
Lisa Dierker
Language
English
Transcripts
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This course belongs to a
collection
called
Data Analysis and Interpretation. It's comprised of five courses:
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