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Mihaela Danci
Mastering distributions and aggregations is essential for effective exploration, summarization, and analysis of datasets.
This course will teach you how to analyze distributions and implement advanced data summarization techniques.
Read more
Mastering distributions and aggregations is essential for effective exploration, summarization, and analysis of datasets. This course will teach you how to analyze distributions and implement advanced data summarization techniques.
Read more
Mastering distributions and aggregations is essential for effective exploration, summarization, and analysis of datasets. This course will teach you how to analyze distributions and implement advanced data summarization techniques.
Distributions and aggregations are essential tools in data science enabling the exploration, summarization, and in-depth analysis of data to derive actionable insights.
In this course, Data Science with Python: Distributions and Aggregations in Data, you’ll gain the ability to explore complex datasets and extract meaningful information.
First, you’ll explore univariate and bivariate distributions using graphical techniques, with an emphasis on understanding the principles of correlation and causation.
Next, you’ll discover how to perform advanced data manipulation techniques, including grouping and aggregation.
Finally, you’ll learn how to reshape data using crosstabs and pivot tables for data summarization and analysis.
When you’re finished with this course, you’ll have the skills and knowledge of distributions and aggregations needed to explore, summarize, and analyze data with Python.
Register for this course and see more details by visiting:
OpenCourser.com/course/f73x60/data
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What's inside
Syllabus
Course Overview
Analyzing Distributions
Exploring Data Summarization Techniques
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Students seeking advancement with Python advanced distribution and aggregation analysis
Those who wish to explore data deeply, summarize data effectively, and analyze datasets in-depth
Designed for those looking to build their expertise in data analysis using Python programming language
Teaches critical Python techniques to analyze distributions and implement advanced data summarization methods
Taught by Mihaela Danci, recognized for her work in data science
Involves hands-on exploration of complex datasets to extract valuable insights
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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 Science with Python: Distributions and Aggregations in Data with these
activities:
Organize and review course materials
Show steps
Stay organized and engaged by compiling and reviewing your notes, assignments, quizzes, and exams. This will help you reinforce what you learn throughout the course.
Show steps
-
Gather all your course materials, including notes, slides, handouts, and assignments.
-
Organize the materials by topic or chapter.
-
Review the materials regularly to refresh your memory.
Review statistics concepts
Show steps
Review the basic concepts of statistics, such as mean, median, mode, and standard deviation to strengthen your foundation for data analysis.
Browse courses on
Statistics
Show steps
-
Read through your old notes or textbooks on statistics.
-
Go through practice problems and exercises to test your understanding.
Read 'Data Analysis with Python and Pandas'
Show steps
Supplement your learning by reading a book that covers the practical aspects of data analysis with Python. This will enhance your understanding of distributions and aggregations.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
-
Read the book and make notes.
-
Try out the examples and exercises provided in the book.
-
Discuss the concepts with peers or online communities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Python visualizations
Show steps
Follow tutorials on Python libraries such as Matplotlib and Seaborn to gain hands-on experience in creating visualizations for data exploration and analysis.
Browse courses on
Data Visualization
Show steps
-
Find tutorials on creating visualizations using Python libraries.
-
Follow the steps in the tutorials to create different types of visualizations.
-
Experiment with different visualization techniques.
Calculate distributions and summary statistics
Show steps
Complete practice drills to master calculating different types of distributions (e.g., normal distribution, binomial distribution) and summary statistics (e.g., mean, median, standard deviation).
Browse courses on
Distributions
Show steps
-
Find practice problems or exercises on calculating distributions and summary statistics.
-
Solve the problems and check your answers.
-
Identify patterns and insights from the results.
Collaborate on a data analysis project
Show steps
Work with peers on a data analysis project to apply your skills, share knowledge, and receive feedback. This will enhance your understanding and retention of course concepts.
Browse courses on
Data Analysis
Show steps
-
Find a group of peers to work with.
-
Choose a dataset and define the project goals.
-
Divide the work among the group members.
-
Discuss your findings and provide feedback to each other.
-
Present your final results to the class.
Analyze and visualize a real-world dataset
Show steps
Apply your skills to analyze and visualize a real-world dataset. This project will provide practical experience and reinforce your understanding of distributions and aggregations.
Browse courses on
Data Analysis
Show steps
-
Find a publicly available dataset that interests you.
-
Clean and prepare the data for analysis.
-
Explore and analyze the data using the concepts covered in the course.
-
Create visualizations to illustrate your findings.
-
Write a report summarizing your analysis and insights.
Tutor other students in data analysis
Show steps
Reinforce your understanding by mentoring other students. By explaining concepts and answering questions, you will deepen your own knowledge and develop effective communication skills.
Browse courses on
Mentoring
Show steps
-
Identify students who need help with data analysis.
-
Review the course material to refresh your understanding.
-
Prepare lesson plans or materials to guide your tutoring sessions.
-
Meet with students regularly to provide support and guidance.
-
Reflect on your experiences and make adjustments to your approach as needed.
Organize and review course materials
Show steps
Stay organized and engaged by compiling and reviewing your notes, assignments, quizzes, and exams. This will help you reinforce what you learn throughout the course.
Show steps
Show steps
Show steps
- Gather all your course materials, including notes, slides, handouts, and assignments.
- Organize the materials by topic or chapter.
- Review the materials regularly to refresh your memory.
Review statistics concepts
Show steps
Review the basic concepts of statistics, such as mean, median, mode, and standard deviation to strengthen your foundation for data analysis.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Read through your old notes or textbooks on statistics.
- Go through practice problems and exercises to test your understanding.
Read 'Data Analysis with Python and Pandas'
Show steps
Supplement your learning by reading a book that covers the practical aspects of data analysis with Python. This will enhance your understanding of distributions and aggregations.
View
Python for Data Analysis: Data Wrangling with...
on Amazon
Show steps
Show steps
Show steps
- Read the book and make notes.
- Try out the examples and exercises provided in the book.
- Discuss the concepts with peers or online communities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Python visualizations
Show steps
Follow tutorials on Python libraries such as Matplotlib and Seaborn to gain hands-on experience in creating visualizations for data exploration and analysis.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find tutorials on creating visualizations using Python libraries.
- Follow the steps in the tutorials to create different types of visualizations.
- Experiment with different visualization techniques.
Calculate distributions and summary statistics
Show steps
Complete practice drills to master calculating different types of distributions (e.g., normal distribution, binomial distribution) and summary statistics (e.g., mean, median, standard deviation).
Browse courses on
Distributions
Show steps
Show steps
Show steps
- Find practice problems or exercises on calculating distributions and summary statistics.
- Solve the problems and check your answers.
- Identify patterns and insights from the results.
Collaborate on a data analysis project
Show steps
Work with peers on a data analysis project to apply your skills, share knowledge, and receive feedback. This will enhance your understanding and retention of course concepts.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a group of peers to work with.
- Choose a dataset and define the project goals.
- Divide the work among the group members.
- Discuss your findings and provide feedback to each other.
- Present your final results to the class.
Analyze and visualize a real-world dataset
Show steps
Apply your skills to analyze and visualize a real-world dataset. This project will provide practical experience and reinforce your understanding of distributions and aggregations.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a publicly available dataset that interests you.
- Clean and prepare the data for analysis.
- Explore and analyze the data using the concepts covered in the course.
- Create visualizations to illustrate your findings.
- Write a report summarizing your analysis and insights.
Tutor other students in data analysis
Show steps
Reinforce your understanding by mentoring other students. By explaining concepts and answering questions, you will deepen your own knowledge and develop effective communication skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify students who need help with data analysis.
- Review the course material to refresh your understanding.
- Prepare lesson plans or materials to guide your tutoring sessions.
- Meet with students regularly to provide support and guidance.
- Reflect on your experiences and make adjustments to your approach as needed.
Career center
Learners who complete Data Science with Python: Distributions and Aggregations in Data will develop knowledge and skills
that may be useful to these careers:
Research Analyst
Research Analyst
Research Analysts use their research skills and expertise to analyze data and trends that can drive business decisions. They collect and interpret data from a variety of sources to identify patterns and insights. The Data Science with Python: Distributions and Aggregations in Data course can help Research Analysts build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Research Analysts.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use statistical techniques to draw conclusions from data and communicate their findings to stakeholders. The Data Science with Python: Distributions and Aggregations in Data course can help Data Analysts build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use their expertise in data analysis, statistics, and machine learning to solve complex business problems. They develop and implement data-driven solutions to improve decision-making and drive business value. The Data Science with Python: Distributions and Aggregations in Data course can help Data Scientists build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Data Scientists.
Business Analyst
Business Analyst
Business Analysts use their analytical skills to identify and solve business problems. They work with stakeholders to gather requirements, analyze data, and develop solutions that improve business processes. The Data Science with Python: Distributions and Aggregations in Data course can help Business Analysts build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Business Analysts.
Statistician
Statistician
Statisticians use their expertise in statistical methods to analyze data and draw conclusions. They work in a variety of fields, including research, healthcare, and public policy. The Data Science with Python: Distributions and Aggregations in Data course can help Statisticians build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Statisticians.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their mathematical and statistical skills to analyze financial data and develop trading strategies. They work in the financial industry, helping investment firms make informed decisions. The Data Science with Python: Distributions and Aggregations in Data course can help Quantitative Analysts build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Quantitative Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their analytical skills to solve complex problems in a variety of industries, including manufacturing, transportation, and healthcare. They develop and implement mathematical models to optimize processes and improve efficiency. The Data Science with Python: Distributions and Aggregations in Data course can help Operations Research Analysts build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Operations Research Analysts.
Market Researcher
Market Researcher
Market Researchers use their analytical skills to understand consumer behavior and market trends. They conduct surveys, analyze data, and develop marketing strategies to help businesses reach their target audience. The Data Science with Python: Distributions and Aggregations in Data course can help Market Researchers build a strong foundation in data analysis techniques, which are essential for success in this role. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis, which are all valuable skills for Market Researchers.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and systems. They work with data scientists and other stakeholders to ensure that data is accessible, reliable, and secure. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Data Engineers who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with data engineers and other stakeholders to ensure that data is stored, organized, and accessible in a secure manner. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Database Administrators who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with stakeholders to gather requirements, design software solutions, and implement and test software code. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Software Engineers who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Computer Scientist
Computer Scientist
Computer Scientists conduct research and develop new computing technologies. They work in a variety of fields, including artificial intelligence, machine learning, and data science. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Computer Scientists who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Actuary
Actuary
Actuaries use their mathematical and statistical skills to assess risk and uncertainty. They work in the insurance industry, helping companies to develop and price insurance products. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Actuaries who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Financial Analyst
Financial Analyst
Financial Analysts use their analytical skills to evaluate investment opportunities and make recommendations to clients. They work in the financial industry, helping individuals and organizations to make informed investment decisions. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Financial Analysts who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and develop strategies to prevent and control outbreaks. They work in public health agencies and research institutions, helping to protect the health of populations. The Data Science with Python: Distributions and Aggregations in Data course may be useful to Epidemiologists who want to build a stronger foundation in data analysis techniques. The course covers topics such as exploring data distributions, advanced data summarization techniques, and reshaping data for analysis.
For more career information including salaries, visit:
OpenCourser.com/course/f73x60/data
Reading list
We've selected 18 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 Science with Python: Distributions and Aggregations in Data.
A comprehensive guide to data science with Python. would provide a valuable reference for the techniques and tools used in the course.
A practical guide to data analysis with Pandas. would provide a good understanding of the Pandas library used in the course.
An introductory book on data science that covers topics such as data cleaning, data exploration, and machine learning. would provide a solid foundation for the concepts and techniques covered in the course.
A book dedicated to data aggregation techniques. would provide a deeper understanding of the aggregation techniques covered in the course.
Save
A classic book on data mining. would provide a good understanding of the fundamental concepts of data mining.
Provides a comprehensive overview of the field of data science. It would be a useful reference for students who want to learn more about the different aspects of data science.
Provides a comprehensive overview of Bayesian data analysis. It would be a helpful resource for students who want to learn more about the Bayesian approach to data analysis.
Save
Provides a comprehensive overview of data mining. It would be a helpful resource for students who want to learn more about the different data mining algorithms and their applications.
Provides an introduction to linear algebra. It would be a helpful resource for students who want to learn more about the mathematical foundations of data science.
Provides an introduction to numerical linear algebra. It would be a helpful resource for students who want to learn more about the mathematical foundations of data science.
Provides an introduction to information theory. It would be a helpful resource for students who want to learn more about the mathematical foundations of data science.
Provides an introduction to statistical thinking and data analysis using Python. It would be a good choice for students who want to learn more about the statistical methods used in data science.
An introductory book on data science for business. would provide a good understanding of the business applications of data science.
Save
A comprehensive guide to deep learning. would provide a good understanding of the fundamental concepts of deep learning.
An in-depth guide to data manipulation with Pandas. would provide a deeper understanding of the Pandas library and its capabilities.
A comprehensive guide to machine learning for finance. would provide a good understanding of the machine learning techniques used in the course.
An in-depth book on statistical methods for finance. would provide a deeper understanding of the statistical concepts used in the course.
A comprehensive guide to big data analytics. would provide a good understanding of the challenges and opportunities of big data.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/f73x60/data
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Time
29 hours
Level
Introductory
Via
Pluralsight
Instructor
Mihaela Danci
Language
English
Good to know
Students seeking advancement with Python advanced distribution and aggregation analysis
Those who wish to explore data deeply, summarize data effectively, and analyze datasets in-depth
Designed for those looking to build their expertise in data analysis using Python programming language
Teaches critical Python techniques to analyze distributions and implement advanced data summarization methods
Taught by Mihaela Danci, recognized for her work in data science
Involves hands-on exploration of complex datasets to extract valuable insights
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