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Data Understanding and Visualization
Di Wu
Di Wu
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication.
Read more
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication.
Read more
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication.
Learning Objectives:
1. Understand and communicate the various aspects of statistics of datasets, including measures of central tendency, variation, location, and correlation.
2. Gain insights into basic statistical concepts and use them to describe dataset characteristics effectively.
3. Utilize Pandas for data manipulation and preparation to set the foundation for data visualization.
4. Master the utilization of Matplotlib and Seaborn to create accurate and meaningful data visualizations.
5. Choose appropriate plot types for different data types and research questions to enhance data comprehension and communication.
Throughout the course, students will actively engage in practical exercises and projects, enabling them to explore statistical concepts, conduct data analysis, and effectively communicate insights through compelling visualizations.
Throughout the course, students will actively engage in practical exercises and projects that involve statistical analysis and data visualization. By the end of the course, participants will be equipped with the knowledge and skills to explore, analyze, and communicate insights from datasets effectively through descriptive statistics and compelling visualizations.
Register for this course and see more details by visiting:
OpenCourser.com/course/4081kv/data
What's inside
Syllabus
Data Statistics
The "Data Statistics" week provides students with a fundamental understanding of statistics as it relates to data analysis. You will explore essential statistical concepts, including measures of central tendency, variation, location, correlation, and other key statistical measures. This week serves as a crucial foundation for students to develop your data analysis and interpretation skills.
Read more
Syllabus
Data Statistics
The "Data Statistics" week provides students with a fundamental understanding of statistics as it relates to data analysis. You will explore essential statistical concepts, including measures of central tendency, variation, location, correlation, and other key statistical measures. This week serves as a crucial foundation for students to develop your data analysis and interpretation skills.
Read more
Data Visualization with Pandas
The "Data Visualization with Pandas" week focuses on uilizing the Pandas package to create effective and insightful data visualizations. You will learn various data visualization techniques to present and communicate data in a clear and concise manner, enhancing your ability to derive valuable insights from datasets.
Data Visualization with Matplotlib
The "Data Visualization with Matplotlib" week focuses utilizing the Matplotlib package to create visually appealing and informative data visualizations. You will learn various data visualization techniques to effectively present and communicate data insights, enabling you to derive valuable information from datasets.
Data Visualization with Seaborn
The "Data Visualization with Seaborn" week focuses on utilizing the Seaborn package to create sophisticated and visually appealing data visualizations. You will learn various data visualization techniques using Seaborn to effectively present and communicate complex data patterns and relationships, empowering you to gain valuable insights from datasets.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Strengthens an existing foundation for intermediate learners of data analysis and visualization techniques
Taught by Di Wu, who are recognized for their work in data science and visualization
Examines fundamental statistical concepts and essential statistical measures, which are highly relevant to data analysis
Develops knowledge of Pandas, Matplotlib, and Seaborn, which are core skills for data manipulation, analysis, and visualization
Provides hands-on exercises and projects that allow learners to actively engage in data analysis and visualization
Covers the essential statistical concepts and techniques needed for effective data analysis and visualization
Teaches skills and knowledge that are useful for personal growth and development in understanding data
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Activities
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Data Understanding and Visualization.
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Career center
Learners who complete Data Understanding and Visualization will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They use a variety of statistical techniques to analyze data and build models. The Data Understanding and Visualization course can help aspiring Data Scientists develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They use a variety of statistical techniques to identify trends and patterns in data. The Data Understanding and Visualization course can help aspiring Data Analysts develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They work in a variety of industries, including business, government, and healthcare. The Data Understanding and Visualization course can help aspiring Statisticians develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make decisions. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course can help aspiring Business Analysts develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to help businesses understand their customers. They use a variety of statistical techniques to identify trends and patterns in data. The Data Understanding and Visualization course can help aspiring Market Researchers develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use a variety of statistical techniques to analyze financial data and identify trends. The Data Understanding and Visualization course can help aspiring Quantitative Analysts develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Actuary
Actuary
Actuaries use data to assess risk. They use a variety of statistical techniques to analyze data and develop models. The Data Understanding and Visualization course can help aspiring Actuaries develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Epidemiologist
Epidemiologist
Epidemiologists use data to investigate the causes of disease. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course can help aspiring Epidemiologists develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Biostatistician
Biostatistician
Biostatisticians use data to design and analyze clinical trials. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course can help aspiring Biostatisticians develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They use a variety of statistical techniques to analyze financial data and identify trends. The Data Understanding and Visualization course can help aspiring Financial Analysts develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Data Engineer
Data Engineer
Data Engineers design and build data systems. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course can help aspiring Data Engineers develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Product Manager
Product Manager
Product Managers design and manage products. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course may help aspiring Product Managers develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Consultant
Consultant
Consultants provide advice to businesses on a variety of topics. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course may help aspiring Consultants develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Researcher
Researcher
Researchers design and conduct research studies. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course may help aspiring Researchers develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
Software Engineer
Software Engineer
Software Engineers design and build software applications. They use a variety of statistical techniques to analyze data and identify trends. The Data Understanding and Visualization course may help aspiring Software Engineers develop the skills they need to be successful in this role, such as how to use statistical software to analyze data, and how to create effective data visualizations.
For more career information including salaries, visit:
OpenCourser.com/course/4081kv/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 Understanding and Visualization.
Provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning, model selection, and statistical inference. It valuable reference for students and practitioners interested in data analysis and machine learning.
Save
Provides a practical introduction to data visualization. It covers a wide range of topics, including data exploration, choosing the right charts, and creating effective presentations. It valuable resource for students and practitioners who want to learn more about data visualization.
Provides a comprehensive introduction to the ggplot2 package for data visualization in R. It covers a wide range of topics, including data transformation, plot customization, and interactive graphics. It valuable resource for students and practitioners who want to learn more about data visualization with ggplot2.
Provides a comprehensive introduction to Python for data analysis. It covers a wide range of topics, including data manipulation, data visualization, and statistical modeling. It valuable resource for students and practitioners who want to learn more about data analysis with Python.
Provides a practical introduction to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It valuable resource for students and practitioners who want to learn more about machine learning with Python.
Provides a comprehensive introduction to data science. It covers a wide range of topics, including data collection, data cleaning, data analysis, and data visualization. It valuable resource for students and practitioners who want to learn more about data science.
Save
Provides a comprehensive introduction to deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners who want to learn more about deep learning.
Provides a comprehensive introduction to reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy optimization. It valuable resource for students and practitioners who want to learn more about reinforcement learning.
Provides a comprehensive introduction to natural language processing with Python. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning for NLP. It valuable resource for students and practitioners who want to learn more about NLP with Python.
Save
Provides a comprehensive introduction to computer vision. It covers a wide range of topics, including image formation, feature detection, and object recognition. It valuable resource for students and practitioners who want to learn more about computer vision.
Provides a comprehensive introduction to speech and language processing. It covers a wide range of topics, including speech recognition, natural language processing, and machine translation. It valuable resource for students and practitioners who want to learn more about speech and language processing.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It covers a wide range of topics, including entropy, information gain, and Bayesian inference. It valuable resource for students and practitioners who want to learn more about information theory, inference, and learning algorithms.
Provides a comprehensive introduction to probabilistic graphical models. It covers a wide range of topics, including Bayesian networks, Markov random fields, and conditional random fields. It valuable resource for students and practitioners who want to learn more about probabilistic graphical models.
Provides a comprehensive introduction to convex optimization. It covers a wide range of topics, including linear programming, quadratic programming, and semidefinite programming. It valuable resource for students and practitioners who want to learn more about convex optimization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4081kv/data
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Effort
3-5 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Di Wu
Language
English
This course belongs to a
collection
called
Data Wrangling with Python. It's comprised of five courses:
- Data Collection and Integration
- Data Processing and Manipulation
- Fundamental Tools of Data Wrangling
- Data Understanding and Visualization (viewing)
- Data Wrangling with Python Project
Good to know
Strengthens an existing foundation for intermediate learners of data analysis and visualization techniques
Taught by Di Wu, who are recognized for their work in data science and visualization
Examines fundamental statistical concepts and essential statistical measures, which are highly relevant to data analysis
Develops knowledge of Pandas, Matplotlib, and Seaborn, which are core skills for data manipulation, analysis, and visualization
Provides hands-on exercises and projects that allow learners to actively engage in data analysis and visualization
Covers the essential statistical concepts and techniques needed for effective data analysis and visualization
Teaches skills and knowledge that are useful for personal growth and development in understanding data
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