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Laura Gemmell
In this 1-hour long project-based course, you will learn how to set up a Google Colab notebook, source data from the internet, load data into Python, merge two datasets, clean data, perform exploratory data analysis, carry out ANOVA and create boxplots. Throughout the course you will work on an Education dataset from World Bank. This will allow you to perform statistical analysis on your own datasets in Python. This project does not require any previous Python or coding experience, but it would be useful for learners to understand the statistical methods covered. The course includes data sourcing and cleaning which are invaluable...
Read more
In this 1-hour long project-based course, you will learn how to set up a Google Colab notebook, source data from the internet, load data into Python, merge two datasets, clean data, perform exploratory data analysis, carry out ANOVA and create boxplots. Throughout the course you will work on an Education dataset from World Bank. This will allow you to perform statistical analysis on your own datasets in Python. This project does not require any previous Python or coding experience, but it would be useful for learners to understand the statistical methods covered. The course includes data sourcing and cleaning which are invaluable...
Read more
In this 1-hour long project-based course, you will learn how to set up a Google Colab notebook, source data from the internet, load data into Python, merge two datasets, clean data, perform exploratory data analysis, carry out ANOVA and create boxplots. Throughout the course you will work on an Education dataset from World Bank. This will allow you to perform statistical analysis on your own datasets in Python. This project does not require any previous Python or coding experience, but it would be useful for learners to understand the statistical methods covered. The course includes data sourcing and cleaning which are invaluable real world skills, and focuses on visualizing your results which is needed as a large part of any analysis is the storytelling.
Register for this course and see more details by visiting:
OpenCourser.com/course/6f7zut/basic
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Know what's good ,
what to watch for , and
possible dealbreakers
Course is well-suited for beginners who want to enhance their data analysis and visualization skills with Python
Provides hands-on experience with data cleaning, analysis, and creating boxplots using Python
Teaches valuable real-world skills in data sourcing and cleaning
Emphasizes storytelling through data visualization, a vital skill for data analysts
Utilizes a dataset from the World Bank for practical applications of data analysis
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Reviews summary
Statistics and python
Basic Statistics in Python (ANOVA) is a 1-hour long project-based course that teaches you how to analyze data using Python. While the course is suitable for beginners, a basic understanding of Python is helpful. One of the standout features of the course is its focus on real-world skills such as data sourcing, cleaning, and visualization, which are essential for data analysis. The course also covers essential statistical methods, although some learners have expressed a desire for more detailed explanations of how to interpret the results from ANOVA.
Google Colab is a useful tool for the course.
"Google Colab was great"
Suitable for beginners with no prior Python or coding experience.
"This project does not require any previous Python or coding experience"
Course emphasizes valuable real-world skills like data sourcing, cleaning, and visualization.
"This project includes data sourcing and cleaning which are invaluable real world skills"
Some learners desire more detailed explanations of ANOVA interpretation.
"I'd like to have more explanation about how we could interpret the results"
Activities
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your course. Deepen your understanding
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it. Supplement your coursework and achieve mastery of the topics covered
in Basic Statistics in Python (ANOVA) with these
activities:
Join a study group
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Attend study group meetings regularly.
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Participate in discussions and ask questions.
Review the textbook
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Review the textbook to gain a better understanding of the course material.
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Introduction R Programming_Professional Level
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Read the book's introduction and first chapter.
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Identify the key concepts and terms.
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Summarize the main points of each chapter.
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Complete the practice exercises at the end of each chapter.
Follow online tutorials
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Follow online tutorials to learn how to use specific data analysis techniques.
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ANOVA
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Find online tutorials on ANOVA, boxplots, and data cleaning.
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Watch the tutorials and take notes.
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Practice using the techniques you learned on your own dataset.
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Complete the practice drills.
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Review your results and identify areas for improvement.
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Fix bugs or add new features.
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Submit a pull request.
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Create a data analysis portfolio to showcase your skills.
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Choose a project to work on.
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Analyze the data and draw conclusions.
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Create a presentation to showcase your findings.
Mentor other students
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Mentor other students to solidify your own understanding of the material and help others learn.
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Data Analysis
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Find opportunities to mentor other students.
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Provide guidance and support.
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Answer questions and provide feedback.
Join a study group
Show steps
Join a study group to learn from and collaborate with other students.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a study group that meets your needs.
- Attend study group meetings regularly.
- Participate in discussions and ask questions.
Review the textbook
Show steps
Review the textbook to gain a better understanding of the course material.
View
Introduction R Programming_Professional Level
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter.
- Identify the key concepts and terms.
- Summarize the main points of each chapter.
- Complete the practice exercises at the end of each chapter.
Follow online tutorials
Show steps
Follow online tutorials to learn how to use specific data analysis techniques.
Browse courses on
ANOVA
Show steps
Show steps
Show steps
- Find online tutorials on ANOVA, boxplots, and data cleaning.
- Watch the tutorials and take notes.
- Practice using the techniques you learned on your own dataset.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data analysis drills
Show steps
Practice data analysis drills to improve your skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find practice drills on data analysis.
- Complete the practice drills.
- Review your results and identify areas for improvement.
Contribute to open source projects
Show steps
Contribute to open source projects to gain experience and learn from others.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find open source projects to contribute to.
- Fix bugs or add new features.
- Submit a pull request.
Create a data analysis portfolio
Show steps
Create a data analysis portfolio to showcase your skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a project to work on.
- Analyze the data and draw conclusions.
- Create a presentation to showcase your findings.
Mentor other students
Show steps
Mentor other students to solidify your own understanding of the material and help others learn.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find opportunities to mentor other students.
- Provide guidance and support.
- Answer questions and provide feedback.
Career center
Learners who complete Basic Statistics in Python (ANOVA) will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists combine statistical, analytical, and programming skills to extract insights from data. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Data Scientists as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to model and interpret complex datasets, which is essential for success as a Data Scientist.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their processes and strategies. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Business Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze business data and make data-driven recommendations, which are crucial skills for Business Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of systems. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Operations Research Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze operational data and make data-driven recommendations for improvement, which are crucial skills for Operations Research Analysts.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help solve real-world problems across various industries. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Statisticians, as it covers fundamental statistical concepts, including ANOVA, which is widely used in statistical analysis. The hands-on exercises focused on data sourcing, cleaning, and visualization will provide valuable practical experience that is highly sought after by employers in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Software Engineers, as it provides a foundation in the statistical methods and data analysis techniques commonly used in software development. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze software data and identify areas for improvement, which are crucial skills for Software Engineers.
Data Analyst
Data Analyst
Data Analysts help businesses make better decisions by extracting and interpreting meaningful patterns from data. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Data Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to identify and explain trends and patterns in data, which are crucial skills for Data Analysts.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments and financial decisions. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Financial Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze financial data and make data-driven recommendations, which are crucial skills for Financial Analysts.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure and systems. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Data Engineers, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to understand data requirements and design data systems that meet those needs, which are crucial skills for Data Engineers.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help communicate insights and trends. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Data Visualization Specialists, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to understand data and create visualizations that effectively convey information, which are crucial skills for Data Visualization Specialists.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Quantitative Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to model and interpret financial data, which is essential for success as a Quantitative Analyst.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty in areas such as insurance, finance, and healthcare. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Actuaries, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze risk and uncertainty, which is essential for success as an Actuary.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of health and disease in populations. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Epidemiologists, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze health data and identify risk factors for disease, which are crucial skills for Epidemiologists.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Biostatisticians, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze biological and medical data, which are crucial skills for Biostatisticians.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand consumer behavior and trends. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Market Researchers, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze market data and make data-driven recommendations, which are crucial skills for Market Researchers.
Research Analyst
Research Analyst
Research Analysts gather and analyze data to provide insights and make recommendations for decision-making. This course in Basic Statistics in Python (ANOVA) may be useful for aspiring Research Analysts, as it provides a foundation in the statistical methods and data analysis techniques commonly used in the field. By learning how to perform ANOVA and create boxplots, you will be better equipped to analyze research data and draw meaningful conclusions, which are crucial skills for Research Analysts.
For more career information including salaries, visit:
OpenCourser.com/course/6f7zut/basic
Reading list
We've selected ten 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
Basic Statistics in Python (ANOVA).
Provides a comprehensive introduction to statistical learning methods, including ANOVA. It valuable resource for both beginners and experienced practitioners.
Provides a thorough treatment of linear statistical models, including ANOVA. It valuable resource for students and researchers in a variety of fields.
Provides a clear and concise introduction to statistical methods commonly used in psychology, including ANOVA. It valuable resource for students and researchers in psychology and related fields.
Provides a comprehensive introduction to data analysis using Python. It includes coverage of ANOVA and other statistical methods.
Save
Provides a comprehensive introduction to data science using R software. It includes coverage of ANOVA and other statistical methods.
Provides a comprehensive introduction to deep learning using Python. It includes coverage of ANOVA and other statistical methods.
Provides a comprehensive introduction to statistical methods commonly used in social sciences, including ANOVA. It valuable resource for students and researchers in social sciences.
Provides a comprehensive introduction to statistical methods commonly used in business and economics, including ANOVA. It valuable resource for students and researchers in business and economics.
Provides a comprehensive introduction to statistical methods commonly used in engineering and the sciences, including ANOVA. It valuable resource for students and researchers in engineering and the sciences.
Provides a comprehensive introduction to mathematical statistics, including ANOVA. It valuable resource for students and researchers in mathematics and statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6f7zut/basic
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Laura Gemmell
Good to know
Course is well-suited for beginners who want to enhance their data analysis and visualization skills with Python
Provides hands-on experience with data cleaning, analysis, and creating boxplots using Python
Teaches valuable real-world skills in data sourcing and cleaning
Emphasizes storytelling through data visualization, a vital skill for data analysts
Utilizes a dataset from the World Bank for practical applications of data analysis
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