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[Supplemental] Introduction to Data
Mat Leonard,
Robert Crocker,
Ben Jones,
Malavica Sridhar,
and
Josh Bernhard
Mat Leonard,
Robert Crocker,
Ben Jones,
Malavica Sridhar,
and
Josh Bernhard
Register for this course and see more details by visiting:
OpenCourser.com/course/98ae8d/supplemental
What's inside
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical and mathematical 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 descriptive and inferential statistics.
Read more
Syllabus
In this lesson, you will learn about data types, measures of center, and the basics of statistical and mathematical 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 descriptive and inferential statistics.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers data types, measures of center, and statistical notation, which are essential concepts in data analysis
Introduces measures of spread, shape, and outliers in quantitative data, providing a foundation for understanding data distribution
Provides hands-on experience with basic spreadsheet functions, allowing learners to manipulate and organize data effectively
Teaches advanced spreadsheet functions, such as aggregation, conditional functions, pivot tables, and lookup functions, equipping learners with comprehensive data analysis skills
Covers data visualization techniques, including pie charts, bar charts, line charts, scatterplots, histograms, and boxplots, enabling learners to present data clearly and effectively
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Activities
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[Supplemental] Introduction to Data.
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Career center
Learners who complete [Supplemental] Introduction to Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst studies data from a variety of sources to assist in the decision-making process. They draw conclusions from the data, providing companies with insights to their operations. This course can help a Data Analyst collect data, clean it, and arrange it in a way that is easy to review. The course teaches statistical and mathematical notation, which can be helpful in understanding data and its implications.
Statistician
Statistician
A Statistician collects and analyzes data to draw conclusions that can be used to make informed decisions. This course can help a Statistician learn about data types, measures of center, measures of spread, shape, and outliers as associated with quantitative data. Additionally, the course covers descriptive and inferential statistics, as well as how to build data visualizations.
Data Scientist
Data Scientist
A Data Scientist uses data to build models that can be used to make predictions and recommendations. This course can help a Data Scientist develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to build models and how to evaluate the performance of those models.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure that is used to store and process data. This course can help a Data Engineer develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to build models and how to evaluate the performance of those models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. This course can help a Machine Learning Engineer develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to build models and how to evaluate the performance of those models.
Business Analyst
Business Analyst
A Business Analyst studies a company's operations to identify areas for improvement. They use data to evaluate the effectiveness of current processes and make recommendations for improvement. This course can help a Business Analyst develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to build visualizations that can help communicate insights to stakeholders.
Actuary
Actuary
An Actuary analyzes financial data to assess risk. This course can help an Actuary develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to assess risk.
Epidemiologist
Epidemiologist
An Epidemiologist studies the distribution and causes of disease. This course can help an Epidemiologist develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to study the distribution and causes of disease.
Marketing Manager
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. This course can help a Marketing Manager develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to measure the effectiveness of marketing campaigns.
Biostatistician
Biostatistician
A Biostatistician uses statistical methods to solve problems in biology and medicine. This course can help a Biostatistician develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to solve problems in biology and medicine.
Data Architect
Data Architect
A Data Architect designs and builds data architectures. This course can help a Data Architect develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to design and build data architectures.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and deploys software applications. This course can help a Software Engineer develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to build models and how to evaluate the performance of those models.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course can help a Database Administrator develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to manage and maintain databases.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. This course can help a Financial Analyst develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to make investment recommendations.
Product Manager
Product Manager
A Product Manager develops and manages software products. This course can help a Product Manager develop the skills needed to collect, clean, and analyze data. It can also teach them how to use data to make decisions about product development.
For more career information including salaries, visit:
OpenCourser.com/course/98ae8d/supplemental
Reading list
We've selected ten 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
[Supplemental] Introduction to Data.
Save
Comprehensive textbook on deep learning, covering a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning.
Save
Comprehensive textbook on data mining, covering a wide range of topics, including data preprocessing, clustering, classification, and association rule mining. It valuable resource for anyone who wants to learn more about data mining.
Classic textbook on statistical learning, covering a wide range of topics, including linear regression, logistic regression, regression trees, and support vector machines. It valuable resource for anyone who wants to learn more about statistical modeling.
Save
Practical guide to data visualization, covering a wide range of topics, including data visualization techniques, design principles, and best practices. It valuable resource for anyone who wants to learn more about how to create effective data visualizations.
Provides a comprehensive overview of data science, including data collection, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about the field of data science or how to use data to make better business decisions.
Gentle introduction to machine learning, covering a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about the basics of machine learning.
Practical guide to using Python for data analysis, covering a wide range of topics, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about how to use Python for data analysis.
Save
Practical guide to using R for data science, covering a wide range of topics, including data cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about how to use R for data analysis.
Save
Practical guide to using Power BI for data analysis, covering a wide range of topics, including data visualization, dashboard design, and best practices. It valuable resource for anyone who wants to learn more about how to use Power BI for data analysis.
Practical guide to using mathematics for machine learning, covering a wide range of topics, including linear algebra, calculus, and optimization. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/98ae8d/supplemental
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Similar courses
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Time
900 hours
Level
Introductory
Via
Udacity
Instructors
Mat Leonard
Robert Crocker
Ben Jones
Malavica Sridhar
Josh Bernhard
Language
English
Good to know
Covers data types, measures of center, and statistical notation, which are essential concepts in data analysis
Introduces measures of spread, shape, and outliers in quantitative data, providing a foundation for understanding data distribution
Provides hands-on experience with basic spreadsheet functions, allowing learners to manipulate and organize data effectively
Teaches advanced spreadsheet functions, such as aggregation, conditional functions, pivot tables, and lookup functions, equipping learners with comprehensive data analysis skills
Covers data visualization techniques, including pie charts, bar charts, line charts, scatterplots, histograms, and boxplots, enabling learners to present data clearly and effectively
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