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Statistics Using Python
Katie Gaertner
Katie Gaertner
This course is a compact primer in statistics as a foundation for data-driven business analysis. A selection of concepts include descriptive statistics, probability, inference, correlation, and regression. The course also exposes students to basic Python programming for use in statistics.
Read more
This course is a compact primer in statistics as a foundation for data-driven business analysis. A selection of concepts include descriptive statistics, probability, inference, correlation, and regression. The course also exposes students to basic Python programming for use in statistics.
Read more
This course is a compact primer in statistics as a foundation for data-driven business analysis. A selection of concepts include descriptive statistics, probability, inference, correlation, and regression. The course also exposes students to basic Python programming for use in statistics.
What you'll learn
You will learn to calculate descriptive statistics and generate basic visualizations using Python; explain principles of probability and derive measures related to probability functions; communicate the uncertainty in statistical estimates; and perform regression analysis and distinguish between correlation and causation.
Register for this course and see more details by visiting:
OpenCourser.com/course/dm1k86/statistics
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Develops critical statistics knowledge and foundational data analysis skills, building a strong base for further study and application of statistics in business analysis and data science
Created by Katie Gaertner, a reputable instructor specializing in statistics and data analysis, who has significant knowledge and experience in the field
Emphasizes the practical application of statistics in business analysis decisions by integrating Python programming for data analysis and visualization
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Career center
Learners who complete Statistics Using Python will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. They use statistical techniques to identify trends, patterns, and relationships in data. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Data Analysts who need to be able to understand and analyze data effectively.
Market Research Analyst
Market Research Analyst
A Market Research Analyst collects and interprets data about markets, customers, and competitors. They use statistical techniques to identify trends and patterns in data and develop insights that can help businesses make informed decisions. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Market Research Analysts who need to be able to understand and analyze data effectively.
Business Analyst
Business Analyst
A Business Analyst helps businesses improve their performance by analyzing data and making recommendations. They use statistical techniques to identify areas for improvement and develop strategies to address them. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Business Analysts who need to be able to understand and analyze data effectively.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses statistical techniques to solve business problems. They use data to identify inefficiencies and develop solutions to improve operations. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Operations Research Analysts who need to be able to understand and analyze data effectively.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates and interprets financial data to help businesses make investment decisions. They use statistical techniques to analyze financial statements and identify trends and patterns. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Financial Analysts who need to be able to understand and analyze data effectively.
Statistical Modeler
Statistical Modeler
A Statistical Modeler develops and uses statistical models to analyze data and make predictions. They use statistical techniques to identify trends and patterns in data and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Statistical Modelers who need to be able to understand and analyze data effectively.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and manages risks for businesses. They use statistical techniques to assess the likelihood and impact of risks and develop strategies to mitigate them. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Risk Analysts who need to be able to understand and analyze data effectively.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses statistical techniques to analyze financial data and make investment decisions. They use data to identify trends and patterns and develop models to predict future performance. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Quantitative Analysts who need to be able to understand and analyze data effectively.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer develops and uses machine learning algorithms to analyze data and make predictions. They use statistical techniques to identify trends and patterns in data and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Machine Learning Engineers who need to be able to understand and analyze data effectively.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines to collect, store, and process data. They use statistical techniques to analyze data and identify trends and patterns. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Data Engineers who need to be able to understand and analyze data effectively.
Data Scientist
Data Scientist
A Data Scientist uses statistical techniques to analyze data and solve business problems. They use data to identify trends and patterns and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Data Scientists who need to be able to understand and analyze data effectively.
Actuary
Actuary
An Actuary uses statistical techniques to assess risks and develop strategies to mitigate them. They use data to identify trends and patterns and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Actuaries who need to be able to understand and analyze data effectively.
Biostatistician
Biostatistician
A Biostatistician uses statistical techniques to analyze data in the field of healthcare. They use data to identify trends and patterns and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Biostatisticians who need to be able to understand and analyze data effectively.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They use statistical techniques to analyze data and identify trends and patterns. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Software Engineers who need to be able to understand and analyze data effectively.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to help businesses make informed decisions. They use statistical techniques to identify trends and patterns in data and develop models to predict future outcomes. This course provides a strong foundation in statistics, including descriptive statistics, probability, inference, correlation, and regression. These concepts are essential for Statisticians who need to be able to understand and analyze data effectively.
For more career information including salaries, visit:
OpenCourser.com/course/dm1k86/statistics
Reading list
We've selected 14 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
Statistics Using Python.
Save
Provides an introduction to probability theory and mathematical statistics, covering topics such as probability distributions, random variables, and statistical inference. It comprehensive and well-written book that is suitable for both beginners and more advanced students.
Provides a comprehensive introduction to using Python for data analysis. It covers a wide range of topics, including data cleaning, data exploration, data visualization, and statistical modeling.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Save
Provides a comprehensive introduction to deep learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive introduction to data science from scratch. It covers a wide range of topics, including data cleaning, data exploration, data visualization, and machine learning.
Provides a comprehensive introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to deep learning using Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides a comprehensive introduction to natural language processing using Python. It covers a wide range of topics, including text classification, text clustering, and text generation.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in engineering and science. It covers a wide range of topics, including descriptive statistics, probability distributions, statistical inference, and regression analysis.
Provides a comprehensive introduction to machine learning using R. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to statistical methods for the social sciences. It covers a wide range of topics, including descriptive statistics, probability distributions, statistical inference, and regression analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dm1k86/statistics
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Effort
12 to 15 hours per week for 8 weeks
Level
Elementary
Via
edX
Institution
The University of Wisconsin-Madison
Instructor
Katie Gaertner
Session
September 4, 2024
- October 28, 2024
Language
English
Transcript
English
Good to know
Develops critical statistics knowledge and foundational data analysis skills, building a strong base for further study and application of statistics in business analysis and data science
Created by Katie Gaertner, a reputable instructor specializing in statistics and data analysis, who has significant knowledge and experience in the field
Emphasizes the practical application of statistics in business analysis decisions by integrating Python programming for data analysis and visualization
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