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Emily Pressman
In this course, you learn the foundational knowledge and skills you need to engage with data in meaningful ways. This is an introductory data literacy course, starting with the basics: what is data, what does it mean to be data literate, and why is it important in today’s world. You will build data skills by following the journeys of a concerned parent, a small business owner, and a public health expert, all of whom rely on data to navigate the COVID-19 pandemic. By the end of this course, you will be able to see the utility and interrogate the reliability of data, discover meaning in data by looking for patterns and trends, use data insights to make informed decisions, communicate data insights to others, and understand how bias impacts your work with data. If you are new to analyzing data, this course is for you. The focus in this course is on practical understanding while avoiding complicated statistical terminology to help you feel supported and encouraged throughout the process. Anybody can be successful in this course regardless or background.
Register for this course and see more details by visiting:
OpenCourser.com/course/hu1x6d/data
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What's inside
Syllabus
Welcome to the Data Literacy: Exploring and Visualizing Data Specialization
In this module you learn about the scope and structure of the courses in this Data Literacy: Exploring and Visualizing Data Specialization.
Read more
Syllabus
Welcome to the Data Literacy: Exploring and Visualizing Data Specialization
In this module you learn about the scope and structure of the courses in this Data Literacy: Exploring and Visualizing Data Specialization.
Read more
Data Literacy Essentials
In this course, you learn the foundational knowledge and skills you need to engage with data in meaningful ways. This is an introductory data literacy course, starting with the basics: what is data, what does it mean to be data literate, and why is it important in today’s world. You will build data skills by following the journeys of a concerned parent, a small business owner, and a public health expert, all of whom rely on data to navigate the COVID-19 pandemic. By the end of this course, you will be able to see the utility and interrogate the reliability of data, discover meaning in data by looking for patterns and trends, use data insights to make informed decisions, communicate data insights to others, and understand how bias impacts your work with data. If you are new to analyzing data, this course is for you. The focus in this course is on practical understanding while avoiding complicated statistical terminology to help you feel supported and encouraged throughout the process. Anybody can be successful in this course regardless or background.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the basics of data literacy and its importance in today's world
Uses real-world examples to showcase the application of data literacy
Emphasizes practical understanding and avoids complex statistical terminology
Designed for beginners with no prior data analysis experience
Taught by Emily Pressman, an experienced instructor in data literacy
Covers essential topics of data literacy, including data interrogation, analysis, and communication
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Save Data Literacy Essentials to your list so you can find it easily later:
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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 Literacy Essentials with these
activities:
Review Principles of Data Analysis
Show steps
Review your knowledge of the foundational principles of data analysis, such as inferential and descriptive statistics, to be better prepared for the course content.
Browse courses on
Data Analysis
Show steps
-
Summarize key concepts from your previous coursework or self-study on data analysis.
-
Complete practice problems to test your understanding of the concepts you reviewed.
Read "Data Visualization: A Practical Introduction"
Show steps
Gain a solid foundation in data visualization principles and techniques by reading this comprehensive guide.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
-
Read the book thoroughly, focusing on the principles and best practices of data visualization.
-
Complete the exercises and activities in the book to reinforce your understanding.
Visualizing Data using Excel
Show steps
Sharpen your ability to use visualization tools within Microsoft Excel to represent data effectively.
Browse courses on
Data Visualization
Show steps
-
Find a dataset that aligns with your interests.
-
Create various charts and graphs to visualize the data.
-
Interpret and draw insights from the visualizations.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Build a data visualization dashboard
Show steps
Gain hands-on experience with data visualization by creating your own dashboard, solidifying your understanding of the principles and techniques covered in this course.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset to work with.
-
Clean and prepare the data.
-
Choose appropriate visualization techniques.
-
Create interactive visualizations.
-
Design a user-friendly dashboard.
Explore Tableau for Advanced Data Visualization
Show steps
Enhance your data visualization skills by exploring advanced features of Tableau, a powerful data visualization tool.
Show steps
-
Enroll in online or in-person Tableau tutorials.
-
Follow the tutorials to learn about creating interactive data dashboards and customizing visualizations.
Participate in a Data Analysis Study Group
Show steps
Engage with fellow learners to discuss course concepts, share insights, and support each other's understanding of data analysis.
Browse courses on
Data Analysis
Show steps
-
Join an existing study group or create your own.
-
Meet regularly to discuss course materials.
-
Work together on data analysis projects and assignments.
Start a Personal Data Analytics Project
Show steps
Consolidate your learning by embarking on a personal data analytics project that allows you to apply your skills to a real-world dataset.
Browse courses on
Data Analytics
Show steps
-
Identify a dataset that aligns with your interests or professional goals.
-
Define the research question or problem you want to address.
-
Clean and prepare the data for analysis.
-
Conduct data analysis and derive meaningful insights.
-
Visualize your findings and communicate them effectively.
Develop a Data Dashboard on a Current Event
Show steps
Apply your data visualization and analysis skills to create a data dashboard that presents insights into a current event of your choice.
Browse courses on
Data Analysis
Show steps
-
Choose a current event that interests you.
-
Gather relevant data from credible sources.
-
Clean and prepare the data for analysis.
-
Design and develop a data dashboard using visualization tools.
-
Share your dashboard with others to communicate your findings.
Contribute to Open-Source Data Visualization Projects
Show steps
Deepen your understanding of data visualization by contributing to open-source projects that focus on developing and improving data visualization tools.
Browse courses on
Open-Source
Show steps
-
Identify open-source data visualization projects that align with your interests.
-
Join the project's community and learn about their codebase.
-
Propose and implement improvements or new features.
Review Principles of Data Analysis
Show steps
Review your knowledge of the foundational principles of data analysis, such as inferential and descriptive statistics, to be better prepared for the course content.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Summarize key concepts from your previous coursework or self-study on data analysis.
- Complete practice problems to test your understanding of the concepts you reviewed.
Read "Data Visualization: A Practical Introduction"
Show steps
Gain a solid foundation in data visualization principles and techniques by reading this comprehensive guide.
View
Data Visualization: A Successful Design Process...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly, focusing on the principles and best practices of data visualization.
- Complete the exercises and activities in the book to reinforce your understanding.
Visualizing Data using Excel
Show steps
Sharpen your ability to use visualization tools within Microsoft Excel to represent data effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Find a dataset that aligns with your interests.
- Create various charts and graphs to visualize the data.
- Interpret and draw insights from the visualizations.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Build a data visualization dashboard
Show steps
Gain hands-on experience with data visualization by creating your own dashboard, solidifying your understanding of the principles and techniques covered in this course.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset to work with.
- Clean and prepare the data.
- Choose appropriate visualization techniques.
- Create interactive visualizations.
- Design a user-friendly dashboard.
Explore Tableau for Advanced Data Visualization
Show steps
Enhance your data visualization skills by exploring advanced features of Tableau, a powerful data visualization tool.
Show steps
Show steps
Show steps
- Enroll in online or in-person Tableau tutorials.
- Follow the tutorials to learn about creating interactive data dashboards and customizing visualizations.
Participate in a Data Analysis Study Group
Show steps
Engage with fellow learners to discuss course concepts, share insights, and support each other's understanding of data analysis.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Join an existing study group or create your own.
- Meet regularly to discuss course materials.
- Work together on data analysis projects and assignments.
Start a Personal Data Analytics Project
Show steps
Consolidate your learning by embarking on a personal data analytics project that allows you to apply your skills to a real-world dataset.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Identify a dataset that aligns with your interests or professional goals.
- Define the research question or problem you want to address.
- Clean and prepare the data for analysis.
- Conduct data analysis and derive meaningful insights.
- Visualize your findings and communicate them effectively.
Develop a Data Dashboard on a Current Event
Show steps
Apply your data visualization and analysis skills to create a data dashboard that presents insights into a current event of your choice.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a current event that interests you.
- Gather relevant data from credible sources.
- Clean and prepare the data for analysis.
- Design and develop a data dashboard using visualization tools.
- Share your dashboard with others to communicate your findings.
Contribute to Open-Source Data Visualization Projects
Show steps
Deepen your understanding of data visualization by contributing to open-source projects that focus on developing and improving data visualization tools.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Identify open-source data visualization projects that align with your interests.
- Join the project's community and learn about their codebase.
- Propose and implement improvements or new features.
Career center
Learners who complete Data Literacy Essentials will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are increasingly sought after by companies around the world. They format, clean, and make sense of raw data to gain meaningful insights and drive informed decision-making. This course's emphasis on making inferences from data, as well as recognizing and avoiding biases that may impact data, directly align with the responsibilities of a Data Analyst. You'll build the essential knowledge and skills needed to succeed in this in-demand field.
Data Science Manager
Data Science Manager
Data Science Managers lead a team of data science professionals to ensure projects are aligned with business objectives and are completed efficiently. This course is particularly relevant to Data Science Managers, as it helps build a foundation for evaluating the quality of data, interpreting it to extract meaningful insights, and effectively communicating these insights to non-technical stakeholders. These are all critical skills for successful Data Science Managers.
Data Engineer
Data Engineer
Data Engineers design, construct, and maintain the infrastructure needed to store and manage large datasets. This course will provide you with the foundational knowledge and skills you need to succeed as a Data Engineer. You'll learn how to work with different types of data, how to clean and prepare data for analysis, and how to develop data pipelines to automate the flow of data. This course's focus on understanding the utility and reliability of data will be particularly valuable for Data Engineers.
Data Scientist
Data Scientist
Data Scientists use a variety of techniques to extract insights from data and build predictive models. This course will help you develop the foundational knowledge and skills needed to succeed as a Data Scientist. You'll learn about the different types of data, how to clean and prepare data for analysis, and how to use statistical and machine learning techniques to build predictive models.
Market Research Analyst
Market Research Analyst
Market Research Analysts gather and interpret data to identify consumer trends and make informed recommendations. This course may be useful for Market Research Analysts, as it provides a foundational understanding of data analysis techniques and how to communicate data insights to others. The course's emphasis on recognizing and avoiding biases in data may also be particularly valuable in this field.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends and patterns that can help businesses make better decisions. This course will help you develop the foundational knowledge and skills needed to succeed as a Business Intelligence Analyst. You'll learn about the different types of data, how to clean and prepare data for analysis, and how to use data visualization techniques to communicate your findings. This course will specifically benefit those interested in the application of data to real-world business scenarios.
Database Administrator
Database Administrator
Database Administrators are responsible for the maintenance and security of databases. This course may be useful for Database Administrators, as it provides a foundational understanding of data management and security principles. The course's emphasis on understanding the reliability of data may also be particularly valuable in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze data and make investment decisions. This course may be useful for Quantitative Analysts, as it provides a foundational understanding of data analysis and statistical techniques. The course's focus on recognizing and avoiding biases in data may also be particularly valuable in this field.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course may be useful for Statisticians, as it provides a foundational understanding of data analysis and statistical techniques. The course's emphasis on understanding the reliability of data and recognizing biases may also be particularly valuable in this field.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and inform the public. This course may be useful for Data Journalists, as it provides a foundational understanding of data analysis and data visualization techniques. The course's emphasis on communicating data insights to others may also be particularly valuable in this field.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be useful for Actuaries, as it provides a foundational understanding of data analysis and statistical techniques. The course's focus on recognizing and avoiding biases in data may also be particularly valuable in this field.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks. This course may be useful for Risk Managers, as it provides a foundational understanding of data analysis and risk management principles. The course's emphasis on understanding the reliability of data may also be particularly valuable in this field.
Auditor
Auditor
Auditors examine financial records to ensure accuracy and compliance. This course may be useful for Auditors, as it provides a foundational understanding of data analysis and accounting principles. The course's focus on recognizing and avoiding biases in data may also be particularly valuable in this field.
Compliance Officer
Compliance Officer
Compliance Officers ensure that organizations comply with laws and regulations. This course may be useful for Compliance Officers, as it provides a foundational understanding of data analysis and compliance principles. The course's focus on recognizing and avoiding biases in data may also be particularly valuable in this field.
Fraud Investigator
Fraud Investigator
Fraud Investigators investigate and prevent fraud. This course may be useful for Fraud Investigators, as it provides a foundational understanding of data analysis and fraud investigation principles. The course's focus on recognizing and avoiding biases in data may also be particularly valuable in this field.
For more career information including salaries, visit:
OpenCourser.com/course/hu1x6d/data
Reading list
We've selected 12 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 Literacy Essentials.
Comprehensive guide to data analysis using Python. It covers topics such as data exploration, statistical modeling, and visualization. It good choice for anyone who wants to learn more about data analysis and how to use Python to solve real-world problems.
Save
Great introduction to statistics and data analysis. It covers topics such as probability, hypothesis testing, and regression in a clear and concise way. It good choice for anyone who wants to learn more about statistics without getting bogged down in technical details.
Save
Comprehensive guide to data science using R. It covers topics such as data exploration, statistical modeling, and visualization. It good choice for anyone who wants to learn more about data science and how to use R to solve real-world problems.
Classic textbook on statistical learning. It covers topics such as supervised learning, unsupervised learning, and statistical modeling. It good choice for anyone who wants to learn more about statistical learning and how to use it to solve real-world problems.
Save
Classic textbook on data mining. It covers topics such as data preprocessing, clustering, classification, and association rule mining. It good choice for anyone who wants to learn more about data mining and how to use it to find patterns in data.
Save
Comprehensive guide to machine learning. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Comprehensive guide to deep learning using Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for anyone who wants to learn more about deep learning and how to use it to solve real-world problems.
Save
Comprehensive guide to deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It good choice for anyone who wants to learn more about deep learning and how to use it to solve real-world problems.
Practical guide to machine learning using Python. It covers topics such as data preprocessing, model selection, and evaluation. It good choice for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Save
Comprehensive guide to data analysis using R. It covers topics such as data exploration, statistical modeling, and visualization. It good choice for anyone who wants to learn more about data analysis and how to use R to solve real-world problems.
Great introduction to data science for beginners. It covers topics such as data collection, analysis, and visualization. It good choice for anyone who wants to learn more about data science and how to use it to solve real-world problems.
Great introduction to data science for business professionals. It covers topics such as data collection, analysis, and visualization. It good choice for anyone who wants to learn more about data science and how to use it to improve their business.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hu1x6d/data
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Level
Introductory
Via
Coursera
Institution
SAS
Instructor
Emily Pressman
Language
English
This course belongs to a
collection
called
Data Literacy: Exploring and Visualizing Data. It's comprised of four courses:
- Data Analysis and Reporting in SAS Visual Analytics
- Getting Started with SAS Visual Analytics
- Data Literacy Essentials (viewing)
- Data Literacy in Practice
Good to know
Teaches the basics of data literacy and its importance in today's world
Uses real-world examples to showcase the application of data literacy
Emphasizes practical understanding and avoids complex statistical terminology
Designed for beginners with no prior data analysis experience
Taught by Emily Pressman, an experienced instructor in data literacy
Covers essential topics of data literacy, including data interrogation, analysis, and communication
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