Understanding and Visualizing Data with Python from Coursera

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling.

At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.

What's inside

Syllabus

WEEK 1 - INTRODUCTION TO DATA

In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. 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.

WEEK 2 - UNIVARIATE DATA

In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries.

WEEK 3 - MULTIVARIATE DATA

In the third week of this course on looking at data, we’ll introduce key ideas for examining research questions that require looking at more than one variable. In particular, we will consider both numerically and visually how different variables interact, how summaries can appear deceiving if you don’t properly account for interactions, and differences between quantitative and categorical variables. This week’s assignment will consist of a writing assignment along with reviewing those of your peers.

WEEK 4 - POPULATIONS AND SAMPLES

In this week, you’ll spend more time thinking about where data come from. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design. You’ll be exposed to important concepts related to sampling from larger populations, including probability and non-probability sampling, and how we can make inferences about larger populations based on well-designed samples. You’ll also learn about the concept of a sampling distribution, and how estimation of the variance of that distribution plays a critical role in making statements about populations. Finally, you’ll learn about the importance of reading the documentation for a given data set; a key step in looking at data is also looking at the available documentation for that data set, which describes how the data were generated.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for beginners who wish to learn about statistics

Taught by experts in the field of statistics, Brenda Gunderson, Brady T. West, and Kerby Shedden

Uses Python as a tool for data analysis and visualization

Covers a comprehensive range of statistical concepts, from data collection to analysis and interpretation

Uses Jupyter Notebook as the course environment

Provides hands-on labs and interactive materials to reinforce learning

Reviews summary

Statistics for dataviz in python

learners say this course is largely positive in its coverage of statistical concepts and their application in Python for data visualization. Key features of the course include the use of real data from NHANES, sample exercises in Jupyter notebooks, and a focus on understanding study design and sampling techniques. Instructors are generally praised for their clarity, and the course is considered well-paced. However, some learners note that the course may be a better fit for those with some prior knowledge of Python.

This course gives an excellent overview of statistics and practical experience of coding statistics in python.

"This course gives an excellent overview of statistics and practical experience of coding statistics in python."

engaging assignments

"Outstanding instruction and instructors."

"Excellent lectures and use of the Jupyter environment to teach the Python applications."

Clear presentation of probability and non-probability sampling, probability of selection, and sampling distribution

"This course is a good begin for statistic learning, especially sampling and visualization ideas."

"Very thorough introductory course to data understanding with a clear presentation of probability and non-probability sampling, probability of selection, and sampling distribution."

Hands-on experience working with real data

"This course give you experience working with real data in Python."

"The course is well-designed and easy to understand. You will also get hands-on experience through the course learning."

Clear, didactic, and entertaining way

"This is a very good course to start learning statistical analysis through python."

"This course is it, it was beyond my expectations and I'm happy I took the course "

"This is a fantastic course. The basics of statistics were a real joy to learn with python."

Python programming part is bit confusing

"Great course but python programming part is bit confusing, can be done on IDLE instead."

"The interactive lab is awesome. But data sampling and inference videos are very dry for beginner."

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 Understanding and Visualizing Data with Python with these activities:

Review Python basics

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To help refresh your memory and ensure your comprehension of the foundational principles in Python, take some time to go over Python basics before the course begins.

Browse courses on Python Basics

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Review basic data types e.g. string, int, float, and list
Practice defining and modifying variables
Go over Python syntax e.g. for loops and if statements

Organize and review course materials

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Enhance your understanding and retention of course content by organizing and reviewing your notes, assignments, quizzes, and exams. This will provide a comprehensive resource for future reference and revision.

Browse courses on Note taking

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Organize and categorize your notes and assignments
Review your notes regularly to reinforce learning
Create summaries or mind maps to consolidate your understanding

Review Statistical Methods for Psychology

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Supplement your understanding of statistical methods by reading Statistical Methods for Psychology. This book provides a comprehensive overview of statistical techniques used in psychological research.

View Fundamental Statistics for the Behavioral... on Amazon

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Read assigned chapters from the book
Summarize key concepts and methods
Apply statistical methods to analyze psychological data

Four other activities

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Participate in discussion forums

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Actively engage in discussion forums to share your insights, ask questions, and learn from your peers. This will enhance your understanding of course concepts and foster collaboration.

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Post thoughtful questions and responses in the discussion forums
Provide constructive feedback to your peers
Engage in respectful and meaningful discussions

Practice statistical methods

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Supplement your understanding of statistical methods by implementing the techniques discussed in the course through practice problems and activities.

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Solve practice problems related to calculating descriptive statistics
Apply statistical methods to analyze data using Python
Interpret the results of statistical analyses

Explore advanced Python libraries

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Expand your knowledge of Python by exploring advanced libraries such as Scikit-learn or Pandas. This will empower you to handle more complex data analysis tasks and enhance your Python proficiency.

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Identify and install relevant Python libraries
Follow tutorials and documentation to learn the functionality of these libraries
Apply these libraries to solve real-world data analysis problems

Develop a data visualization dashboard

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Create a data visualization dashboard to apply your skills in data manipulation and visualization. This project will reinforce your understanding of data analysis and presentation.

Browse courses on Data Visualization

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Gather and prepare data for visualization
Choose appropriate visualizations for different data types
Design and implement a user-friendly dashboard layout

Career center

Learners who complete Understanding and Visualizing Data with Python will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use data to solve business challenges. They collect, analyze, interpret, and present information to help organizations understand their customers and make informed decisions. This course provides a solid foundation in statistics, data management, and visualization, which are essential skills for Data Analysts. The course also introduces Python, a popular programming language for data analysis and visualization.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use scientific methods to extract knowledge from data. They use statistical models and machine learning algorithms to identify trends, patterns, and anomalies in data, and they use this information to make predictions and recommendations. This course provides a strong foundation in statistics, which is essential for Data Scientists, and introduces Python, a popular programming language for data science.

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Statistician

Statisticians collect, analyze, interpret, and present statistical data to help organizations make informed decisions. They work in a variety of fields, including finance, healthcare, education, and government. This course provides a solid foundation in statistics, which is essential for Statisticians, and introduces Python, a popular programming language for statistics.

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Market Researcher

Market Researchers conduct research to understand consumer behavior and market trends. They use this information to help businesses develop and market their products and services. This course provides a solid foundation in statistics and data visualization, which are essential skills for Market Researchers, and introduces Python, a popular programming language for market research.

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Business Analyst

Business Analysts help organizations make better decisions by analyzing data and providing insights. They use statistical models and other analytical techniques to identify trends and patterns in data, and they use this information to recommend ways to improve business processes. This course provides a strong foundation in statistics and data visualization, which are essential skills for Business Analysts, and introduces Python, a popular programming language for business analysis.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. They analyze financial statements, economic data, and other information to identify investment opportunities and risks. This course provides a solid foundation in statistics and data visualization, which are essential skills for Financial Analysts, and introduces Python, a popular programming language for financial analysis.

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Data Journalist

Data Journalists use data to tell stories and inform the public. They work with data from a variety of sources, including databases, logs, and sensors, and they use this data to create articles, visualizations, and other content that can be used by the public to understand current events and trends. This course provides a strong foundation in data visualization and interpretation, which are essential skills for Data Journalists, and introduces Python, a popular programming language for data journalism.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work with data from a variety of sources, including databases, logs, and sensors, and they use this data to create software applications that can be used by businesses and consumers. This course provides a strong foundation in data visualization, which is an essential skill for Software Engineers, and introduces Python, a popular programming language for software development.

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Web Developer

Web Developers design and develop websites. They work with data from a variety of sources, including databases, logs, and sensors, and they use this data to create websites that can be used by businesses and consumers. This course provides a strong foundation in data visualization, which is an essential skill for Web Developers, and introduces Python, a popular programming language for web development.

See salaries and explore the career path for Web Developer

Data Engineer

Data Engineers design, build, and maintain data pipelines. They work with data from a variety of sources, including databases, logs, and sensors, and they use this data to create data sets that can be used by data scientists and other analysts. This course provides a strong foundation in data management and visualization, which are essential skills for Data Engineers, and introduces Python, a popular programming language for data engineering.

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Operations Manager

Operations Managers plan and execute operations to achieve specific goals. They work with data from a variety of sources, including production data and inventory data, and they use this data to identify inefficiencies and develop improvements. This course provides a strong foundation in data visualization and interpretation, which are essential skills for Operations Managers, and introduces Python, a popular programming language for operations management.

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Marketing Manager

Marketing Managers plan and execute marketing campaigns to promote products and services. They work with data from a variety of sources, including market research and sales data, and they use this data to develop marketing campaigns that are effective and profitable. This course provides a strong foundation in data visualization and interpretation, which are essential skills for Marketing Managers, and introduces Python, a popular programming language for marketing.

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UX Designer

UX Designers design user interfaces for websites and mobile applications. They work with data from a variety of sources, including user research and surveys, and they use this data to create user interfaces that are easy to use and understand. This course provides a strong foundation in data visualization and interpretation, which are essential skills for UX Designers, and introduces Python, a popular programming language for UX design.

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Project Manager

Project Managers plan and execute projects to achieve specific goals. They work with data from a variety of sources, including project plans and budgets, and they use this data to track progress and identify risks. This course provides a strong foundation in data visualization and interpretation, which are essential skills for Project Managers, and introduces Python, a popular programming language for project management.

See salaries and explore the career path for Project Manager

Sales Manager

Sales Managers lead and motivate sales teams to achieve sales goals. They work with data from a variety of sources, including sales data and customer feedback, and they use this data to develop sales strategies and tactics. This course provides a strong foundation in data visualization and interpretation, which are essential skills for Sales Managers, and introduces Python, a popular programming language for sales management.

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