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Brad Batesole,
Anke Audenaert,
Mat Leonard,
Dana Sheahen,
and
Josh Bernhard
Register for this course and see more details by visiting:
OpenCourser.com/course/b94gz2/introduction
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What's inside
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
Read more
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical notation.
In this lesson, you will learn about measures of spread, shape, and outliers as associated with quantitative data. You will also get a first look at inferential statistics.
Read more
In this lesson, you will learn about the basic functionality for spreadsheet software, use cell referencing and menu shortcuts.
Project: Working with Data
In this lesson, you will learn basic spreadsheet function: sort and filter data, use text and math functions, split columns and remove duplicates.
In this lesson, you will learn how to summarize data with aggregation and conditional functions. You will learn how to use pivot tables and lookup functions.
In this lesson you will build data visualizations for quantitative and categorical data; create pie, bar, line, scatter, histogram, and boxplot charts, and build professional presentations.
Project: Analyze Survey Data
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners by introducing basic spreadsheet fundamentals
Covers basic spreadsheet functionality and features, which can be useful in various settings
Introduces data analysis concepts and techniques, which are valuable for making informed decisions
Provides hands-on practice through projects, enabling learners to apply their knowledge and gain proficiency
Taught by experienced instructors, indicating the course's credibility and quality
Save this course
Save Introduction to Data 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 Introduction to Data with these
activities:
Refresh your understanding of basic statistical concepts
Show steps
Review the basics of statistical notation, measures of center and spread, and inferential statistics to strengthen your foundation before starting the course.
Browse courses on
Statistics
Show steps
-
Revisit notes or textbooks from previous courses in statistics or data analysis.
-
Complete practice problems related to calculating measures of center and spread.
-
Review examples of statistical inference, such as hypothesis testing and confidence intervals.
Review 'Introduction to Statistical Thinking' by Benjamin Yakir
Show steps
Supplement your understanding of statistical concepts by reading a comprehensive textbook that provides a clear and engaging introduction to the subject.
View
Introduction to Statistical Thinking
on Amazon
Show steps
-
Read the assigned chapters and make notes.
-
Solve the practice problems at the end of each chapter.
-
Discuss the concepts with classmates or the course instructor.
Explore online tutorials on spreadsheet functions
Show steps
Enhance your understanding of spreadsheet functionality by following guided tutorials on sorting, filtering, and using text and math functions.
Browse courses on
Spreadsheet Functions
Show steps
-
Search for online tutorials on spreadsheet functions, such as those offered by Microsoft Excel or Google Sheets.
-
Follow along with the tutorials, practicing the steps and experimenting with different functions.
-
Apply the functions you've learned to real-world data sets to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer study sessions
Show steps
Engage with classmates to discuss course concepts, solve problems together, and reinforce your learning.
Show steps
-
Form a study group with fellow students.
-
Meet regularly to discuss course material, ask questions, and work on assignments.
-
Take turns leading discussions and presenting concepts to the group.
Solve practice problems on statistical concepts
Show steps
Reinforce your understanding of statistical concepts by solving practice problems that cover measures of center, spread, and inferential statistics.
Browse courses on
Data Analysis
Show steps
-
Find practice problems online or in textbooks.
-
Attempt to solve the problems on your own.
-
Check your solutions against answer keys or consult with the course instructor or a tutor for guidance.
Create visual data representations
Show steps
Solidify your understanding of data visualization by creating various types of charts and graphs, such as bar charts, line charts, and scatterplots, to represent quantitative and categorical data.
Browse courses on
Data Visualization
Show steps
-
Choose a data set that interests you.
-
Use spreadsheet software or online tools to create different types of visualizations for the data.
-
Analyze the visualizations and draw insights from the data.
-
Share your visualizations with others for feedback and discussion.
Volunteer at a data analysis or research organization
Show steps
Gain practical experience in applying statistical concepts and data analysis techniques by volunteering at an organization that conducts research or provides data analysis services.
Show steps
-
Research organizations that align with your interests.
-
Contact the organizations to inquire about volunteer opportunities.
-
Attend volunteer training sessions.
-
Assist with data collection, analysis, or visualization projects under the guidance of experienced professionals.
Contribute to open-source projects related to data analysis
Show steps
Deepen your understanding of data analysis techniques and gain valuable experience by contributing to open-source projects in the field.
Browse courses on
Open Source
Show steps
-
Identify open-source projects that align with your interests.
-
Fork the project repository and make your own contributions.
-
Collaborate with other contributors and the project maintainers.
-
Share your learnings and insights with the community.
Refresh your understanding of basic statistical concepts
Show steps
Review the basics of statistical notation, measures of center and spread, and inferential statistics to strengthen your foundation before starting the course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Revisit notes or textbooks from previous courses in statistics or data analysis.
- Complete practice problems related to calculating measures of center and spread.
- Review examples of statistical inference, such as hypothesis testing and confidence intervals.
Review 'Introduction to Statistical Thinking' by Benjamin Yakir
Show steps
Supplement your understanding of statistical concepts by reading a comprehensive textbook that provides a clear and engaging introduction to the subject.
View
Introduction to Statistical Thinking
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters and make notes.
- Solve the practice problems at the end of each chapter.
- Discuss the concepts with classmates or the course instructor.
Explore online tutorials on spreadsheet functions
Show steps
Enhance your understanding of spreadsheet functionality by following guided tutorials on sorting, filtering, and using text and math functions.
Browse courses on
Spreadsheet Functions
Show steps
Show steps
Show steps
- Search for online tutorials on spreadsheet functions, such as those offered by Microsoft Excel or Google Sheets.
- Follow along with the tutorials, practicing the steps and experimenting with different functions.
- Apply the functions you've learned to real-world data sets to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in peer study sessions
Show steps
Engage with classmates to discuss course concepts, solve problems together, and reinforce your learning.
Show steps
Show steps
Show steps
- Form a study group with fellow students.
- Meet regularly to discuss course material, ask questions, and work on assignments.
- Take turns leading discussions and presenting concepts to the group.
Solve practice problems on statistical concepts
Show steps
Reinforce your understanding of statistical concepts by solving practice problems that cover measures of center, spread, and inferential statistics.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Attempt to solve the problems on your own.
- Check your solutions against answer keys or consult with the course instructor or a tutor for guidance.
Create visual data representations
Show steps
Solidify your understanding of data visualization by creating various types of charts and graphs, such as bar charts, line charts, and scatterplots, to represent quantitative and categorical data.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a data set that interests you.
- Use spreadsheet software or online tools to create different types of visualizations for the data.
- Analyze the visualizations and draw insights from the data.
- Share your visualizations with others for feedback and discussion.
Volunteer at a data analysis or research organization
Show steps
Gain practical experience in applying statistical concepts and data analysis techniques by volunteering at an organization that conducts research or provides data analysis services.
Show steps
Show steps
Show steps
- Research organizations that align with your interests.
- Contact the organizations to inquire about volunteer opportunities.
- Attend volunteer training sessions.
- Assist with data collection, analysis, or visualization projects under the guidance of experienced professionals.
Contribute to open-source projects related to data analysis
Show steps
Deepen your understanding of data analysis techniques and gain valuable experience by contributing to open-source projects in the field.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests.
- Fork the project repository and make your own contributions.
- Collaborate with other contributors and the project maintainers.
- Share your learnings and insights with the community.
Career center
Learners who complete Introduction to Data will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A professional who extracts insights and patterns from data using scientific methods, often involving data mining, machine learning, and statistical analysis. This course can help you understand data storage, types, and common data tools. You will also learn to apply statistical methods to gain insights from complex data.
Statistician
Statistician
A Statistician designs, conducts, and interprets statistical studies to collect and analyze data. They use their findings to make informed decisions and solve problems. This course will teach you the basics of statistical notation and how to use statistical methods to draw conclusions from data.
Database Administrator
Database Administrator
A Database Administrator is responsible for the design, implementation, and maintenance of corporate databases. This course will help you understand data storage, types, and common data tools.
Data Analyst
Data Analyst
A professional who collects, analyzes, and interprets data to help organizations make informed decisions. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Business Analyst
Business Analyst
A professional who analyzes business processes and systems to identify areas for improvement. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Market Researcher
Market Researcher
A professional who conducts research to gather information about target markets, customer preferences, and industry trends. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Financial Analyst
Financial Analyst
A professional who analyzes financial data to make investment recommendations and assess the financial health of companies and organizations. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Operations Research Analyst
Operations Research Analyst
A professional who uses mathematical and analytical methods to solve complex business problems. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Management Consultant
Management Consultant
A professional who helps organizations improve their performance by providing advice on strategy, operations, and technology. This course will teach you the basics of data analysis, including data cleaning, data preparation, and data visualization.
Software Engineer
Software Engineer
A professional who designs, develops, and maintains software applications. This course will teach you the basics of data structures and algorithms, which are essential for software development.
Computer Scientist
Computer Scientist
A professional who studies the theory and application of computer science. This course will teach you the basics of data structures and algorithms, which are essential for computer science.
Data Engineer
Data Engineer
A professional who designs, builds, and maintains data pipelines and infrastructure. This course will teach you the basics of data storage, types, and common data tools.
Information Architect
Information Architect
A professional who designs and organizes information systems to meet the needs of users. This course will teach you the basics of data storage, types, and common data tools.
Technical Writer
Technical Writer
A professional who writes and edits technical documentation, such as user manuals, training materials, and technical reports. This course may help you understand the basics of data analysis, which can be helpful for writing about technical topics.
Librarian
Librarian
A professional who helps people find and access information. This course may help you understand the basics of data storage and organization, which can be helpful for working with library resources.
For more career information including salaries, visit:
OpenCourser.com/course/b94gz2/introduction
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
Introduction to Data.
Save
Deep Learning comprehensive introduction to deep learning, and is especially useful for learning more about the different types of deep learning architectures and how to build deep learning models.
Deep Learning with Python provides a comprehensive introduction to deep learning with Python, and is especially useful for learning more about the different types of deep learning architectures and how to implement them in Python.
The Art of Data Analysis provides a practical guide to data analysis, and is especially useful for learning more about how to approach and solve data analysis problems.
Save
Data Mining: Practical Machine Learning Tools and Techniques comprehensive introduction to data mining techniques, and is especially useful for learning more about how to use data mining techniques to solve real-world problems.
Introduction to Data Science provides a comprehensive overview of data science, and is especially useful for learning more about the different aspects of data science, including data collection, data preprocessing, data analysis, and data visualization.
Data Science for Business provides a practical guide to data science for business professionals, and is especially useful for learning more about how to use data science to solve business problems.
The Data Science Design Manual provides a practical guide to data science project design, and is especially useful for learning more about how to design and implement successful data science projects.
Save
Data Visualization: A Practical Introduction great resource for learning more about how to create effective data visualizations. It is especially useful for learning more about the different types of data visualizations and how to choose the right visualization for your data.
Data Science from Scratch provides a hands-on introduction to data science, and is especially useful for learning more about how to use Python for data science.
Data Analysis Using Excel great resource for learning more about how to use spreadsheet software for data analysis. It is especially useful for learning more about the functions and features of Microsoft Excel.
The Statistical Sleuth comprehensive introduction to data analysis techniques, and is especially useful for learning more about inferential statistics. It is more valuable as a current reference than it is as background reading.
Introduction to Statistical Thinking provides a great overview of basic statistical concepts, and is helpful for solidifying the material you learn from this course. It is more valuable as background reading than it current reference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b94gz2/introduction
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Time
24480 hours
Level
Introductory
Via
Udacity
Instructors
Brad Batesole
Anke Audenaert
Mat Leonard
Dana Sheahen
Josh Bernhard
Language
English
Good to know
Builds a strong foundation for beginners by introducing basic spreadsheet fundamentals
Covers basic spreadsheet functionality and features, which can be useful in various settings
Introduces data analysis concepts and techniques, which are valuable for making informed decisions
Provides hands-on practice through projects, enabling learners to apply their knowledge and gain proficiency
Taught by experienced instructors, indicating the course's credibility and quality
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