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Janani Ravi
This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python, including the use of Naive Bayes' classifiers and Seaborn to visualize relationships.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. So, it is really important that you know your data well.
Read more
This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python, including the use of Naive Bayes' classifiers and Seaborn to visualize relationships.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. So, it is really important that you know your data well.
Read more
This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python, including the use of Naive Bayes' classifiers and Seaborn to visualize relationships.
Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. So, it is really important that you know your data well.
In this course, Summarizing Data and Deducing Probabilities, you will gain the ability to summarize your data using univariate, bivariate, and multivariate statistics in a range of technologies.
First, you will learn how measures of mean and central tendency can be calculated in Microsoft Excel and Python. Next, you will discover how to use correlations and covariances to explore pairwise relationships. You will then see how those constructs can be generalized to multiple variables using covariance and correlation matrices.
You will understand and apply Bayes' Theorem, one of the most powerful and widely-used results in probability, to build a robust classifier.
Finally, you will use Seaborn, a visualization library, to represent statistics visually.
When you are finished with this course, you will have the skills and knowledge to use univariate, bivariate, and multivariate descriptive statistics from Excel and Python in order to find relationships and calculate probabilities.
Register for this course and see more details by visiting:
OpenCourser.com/course/ltx3x4/summarizing
What's inside
Syllabus
Course Overview
Understanding Descriptive Statistics for Data Analysis
Performing Exploratory Data Analysis in Spreadsheets
Summarizing Data and Deducing Probabilities Using Python
Read more
Syllabus
Course Overview
Understanding Descriptive Statistics for Data Analysis
Performing Exploratory Data Analysis in Spreadsheets
Summarizing Data and Deducing Probabilities Using Python
Read more
Understanding and Applying Bayes' Rule
Visualizing Probabilistic and Statistical Data Using Seaborn
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Good fit for learners interested in data science and data modeling
Teaches tools and skills core to those working in data analysis
Develops skills and knowledge core to those working in data analysis
Taught by Janani Ravi, who is recognized for their work in data analysis
Explores updated techniques and methodologies for exploratory data analysis (EDA)
Requires students to have some background knowledge in statistics
Save this course
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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 Summarizing Data and Deducing Probabilities with these
activities:
Review Statistics Basics
Show steps
Reinforce your statistics foundation by reviewing the basics of mean and central tendency, which are crucial for working with data in this course.
Browse courses on
Statistics
Show steps
-
Revisit the concepts of mean, median, and mode.
-
Review measures of central tendency, including measures of dispersion and skewness.
-
Go through practice problems related to mean and central tendency.
Create a Course Summary Document
Show steps
Reinforce your learning by creating a comprehensive summary document that consolidates your notes, assignments, and key concepts covered in the course.
Show steps
-
Review your lecture notes and handouts.
-
Summarize important concepts and formulas.
-
Include examples and practice problems to illustrate your understanding.
Attend a Data Science Meetup
Show steps
Connect with other data science enthusiasts and practitioners by attending a local meetup or online event.
Show steps
-
Find a data science meetup in your area.
-
Attend the meetup and introduce yourself to attendees.
-
Participate in discussions and share your experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Statistical Rethinking' by Richard McElreath
Show steps
Expand your understanding of Bayesian statistics, a powerful approach used for modeling uncertainty, by reviewing this foundational text.
View
Statistical Rethinking
on Amazon
Show steps
-
Read chapters 1-3 to grasp the basics of Bayesian inference.
-
Work through examples and exercises to reinforce your understanding.
-
Consider applying Bayesian techniques to your own data analysis projects.
Practice Calculating Covariance and Correlation
Show steps
Solidify your understanding of covariance and correlation by engaging in repetitive exercises and solving practice problems.
Browse courses on
Covariance
Show steps
-
Work through examples of calculating covariance and correlation coefficients.
-
Solve practice problems involving the interpretation of covariance and correlation.
-
Apply these concepts to real-world datasets.
Participate in a Hands-on Data Visualization Workshop
Show steps
Enhance your data visualization skills through hands-on practice in a workshop setting.
Browse courses on
Data Visualization
Show steps
-
Attend a workshop that focuses on data visualization techniques.
-
Follow along with the instructor and create your own visualizations.
-
Ask questions and engage in discussions with other participants.
Develop Visualizations Using Seaborn
Show steps
Enhance your data visualization skills by creating interactive and informative visualizations using Seaborn, a popular Python library for visualizing statistical data.
Browse courses on
Seaborn
Show steps
-
Explore the Seaborn library and its various visualization options.
-
Create scatterplots, histograms, and other visualizations to represent statistical data.
-
Customize and refine your visualizations to effectively communicate insights.
-
Present your visualizations and explain the statistical relationships they illustrate.
Build a Data Analysis Dashboard
Show steps
Apply your knowledge of data analysis techniques to create a comprehensive dashboard that summarizes and visualizes key insights from a real-world dataset.
Browse courses on
Data Modeling
Show steps
-
Gather and clean a dataset relevant to your interests.
-
Summarize the data using univariate, bivariate, and multivariate statistics.
-
Design and develop interactive visualizations to represent the data.
-
Integrate your visualizations into a user-friendly dashboard.
-
Present your dashboard and explain how it can be used for data-driven decision-making.
Review Statistics Basics
Show steps
Reinforce your statistics foundation by reviewing the basics of mean and central tendency, which are crucial for working with data in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Revisit the concepts of mean, median, and mode.
- Review measures of central tendency, including measures of dispersion and skewness.
- Go through practice problems related to mean and central tendency.
Create a Course Summary Document
Show steps
Reinforce your learning by creating a comprehensive summary document that consolidates your notes, assignments, and key concepts covered in the course.
Show steps
Show steps
Show steps
- Review your lecture notes and handouts.
- Summarize important concepts and formulas.
- Include examples and practice problems to illustrate your understanding.
Attend a Data Science Meetup
Show steps
Connect with other data science enthusiasts and practitioners by attending a local meetup or online event.
Show steps
Show steps
Show steps
- Find a data science meetup in your area.
- Attend the meetup and introduce yourself to attendees.
- Participate in discussions and share your experiences.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'Statistical Rethinking' by Richard McElreath
Show steps
Expand your understanding of Bayesian statistics, a powerful approach used for modeling uncertainty, by reviewing this foundational text.
View
Statistical Rethinking
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to grasp the basics of Bayesian inference.
- Work through examples and exercises to reinforce your understanding.
- Consider applying Bayesian techniques to your own data analysis projects.
Practice Calculating Covariance and Correlation
Show steps
Solidify your understanding of covariance and correlation by engaging in repetitive exercises and solving practice problems.
Browse courses on
Covariance
Show steps
Show steps
Show steps
- Work through examples of calculating covariance and correlation coefficients.
- Solve practice problems involving the interpretation of covariance and correlation.
- Apply these concepts to real-world datasets.
Participate in a Hands-on Data Visualization Workshop
Show steps
Enhance your data visualization skills through hands-on practice in a workshop setting.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Attend a workshop that focuses on data visualization techniques.
- Follow along with the instructor and create your own visualizations.
- Ask questions and engage in discussions with other participants.
Develop Visualizations Using Seaborn
Show steps
Enhance your data visualization skills by creating interactive and informative visualizations using Seaborn, a popular Python library for visualizing statistical data.
Browse courses on
Seaborn
Show steps
Show steps
Show steps
- Explore the Seaborn library and its various visualization options.
- Create scatterplots, histograms, and other visualizations to represent statistical data.
- Customize and refine your visualizations to effectively communicate insights.
- Present your visualizations and explain the statistical relationships they illustrate.
Build a Data Analysis Dashboard
Show steps
Apply your knowledge of data analysis techniques to create a comprehensive dashboard that summarizes and visualizes key insights from a real-world dataset.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Gather and clean a dataset relevant to your interests.
- Summarize the data using univariate, bivariate, and multivariate statistics.
- Design and develop interactive visualizations to represent the data.
- Integrate your visualizations into a user-friendly dashboard.
- Present your dashboard and explain how it can be used for data-driven decision-making.
Career center
Learners who complete Summarizing Data and Deducing Probabilities will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist collects, analyzes, and interprets data to help organizations make better decisions. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Data Scientist.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to help organizations make better decisions. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in statistics, which is essential for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Statistician.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and deploys machine learning models to solve business problems. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Machine Learning Engineer.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Quantitative Analyst.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make better decisions. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis, which is essential for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. This course will give you the skills you need to succeed as a Data Analyst.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis, which is essential for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. This course will give you the skills you need to succeed as a Business Analyst.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of organizations. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as an Operations Research Analyst.
Risk Analyst
Risk Analyst
A Risk Analyst assesses the risk of potential events and develops strategies to mitigate those risks. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Risk Analyst.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Financial Analyst.
Actuary
Actuary
An Actuary uses mathematical and statistical models to assess and manage risk. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as an Actuary.
Biostatistician
Biostatistician
A Biostatistician uses statistical methods to analyze data in the field of biology. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in statistics, which is essential for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Biostatistician.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes of disease and develops strategies to prevent and control it. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as an Epidemiologist.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and preferences. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis, which is essential for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. This course will give you the skills you need to succeed as a Market Researcher.
Social Scientist
Social Scientist
A Social Scientist studies human behavior and society. The Summarizing Data and Deducing Probabilities course can help you build a strong foundation in data analysis and probability theory, which are essential skills for this role. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. You will also learn how to apply Bayes' Theorem to build robust classifiers. This course will give you the skills you need to succeed as a Social Scientist.
Teacher
Teacher
A Teacher educates students in a variety of subjects. The Summarizing Data and Deducing Probabilities course may be useful for a Teacher who wants to use data analysis to improve their teaching methods. You will learn how to use statistical techniques to summarize data, identify trends, and make predictions. This course will give you the skills you need to succeed as a Teacher.
For more career information including salaries, visit:
OpenCourser.com/course/ltx3x4/summarizing
Reading list
We've selected 13 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
Summarizing Data and Deducing Probabilities.
Provides a comprehensive introduction to probability and mathematical statistics, covering topics such as probability theory, measure theory, and stochastic processes. It suitable reference for readers who want to delve deeper into the mathematical foundations of probability.
This textbook provides a clear and accessible introduction to probability theory, with a focus on examples and applications. It covers topics such as probability spaces, random variables, and conditional probability.
Provides a comprehensive introduction to using Python for data analysis. It covers topics such as data wrangling, data visualization, and machine learning.
Save
Provides a practical guide to using R for exploratory data analysis. It covers topics such as data visualization, data manipulation, and statistical modeling.
Provides a practical guide to using R for machine learning. It covers topics such as supervised learning, unsupervised learning, and model evaluation.
Provides a comprehensive introduction to data mining with R. It covers topics such as data preprocessing, feature selection, and model evaluation.
Provides a comprehensive introduction to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to natural language processing with Python. It covers topics such as text classification, text clustering, and machine translation.
Provides a comprehensive introduction to computer vision with Python. It covers topics such as image processing, object detection, and facial recognition.
Provides a comprehensive introduction to reinforcement learning with Python. It covers topics such as Markov decision processes, value iteration, and policy iteration.
Provides a comprehensive introduction to Bayesian analysis with Python. It covers topics such as Bayesian inference, Markov chain Monte Carlo, and approximate Bayesian computation.
Provides a comprehensive introduction to causal inference for social and biomedical researchers. It covers topics such as causal graphs, potential outcomes, and instrumental variables.
Provides a practical introduction to Bayesian statistics with R and Stan. It covers topics such as Bayesian modeling, Markov chain Monte Carlo, and hierarchical models.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ltx3x4/summarizing
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Time
170 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
Good to know
Good fit for learners interested in data science and data modeling
Teaches tools and skills core to those working in data analysis
Develops skills and knowledge core to those working in data analysis
Taught by Janani Ravi, who is recognized for their work in data analysis
Explores updated techniques and methodologies for exploratory data analysis (EDA)
Requires students to have some background knowledge in statistics
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