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Statistics Using Python

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.

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What's inside

Learning objective

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.

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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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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 Statistics Using Python with these activities:

Read 'Introduction to Probability' by Charles Miller Grinstead and James Laurie Snell

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Supplement your understanding of probability, a core foundation of statistics, by delving into this authoritative text.

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Purchase or borrow a copy of 'Introduction to Probability'.
Read the book thoroughly, taking notes and highlighting key concepts.
Work through the practice problems and exercises to reinforce your learning.

Review the basics of statistics

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Sharpen your understanding of the fundamentals of statistics to enhance your comprehension of course content.

Browse courses on Statistics

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Revisit key concepts in probability, such as events, probability distributions, and Bayes' theorem.
Brush up on descriptive statistics, including measures of central tendency, dispersion, and graphical representations.

Mentor a peer in statistics

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Strengthen your understanding of statistical concepts by helping others learn. Engage in peer mentorship to reinforce your knowledge and develop soft skills.

Browse courses on Mentorship

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Identify a peer who is struggling with statistics concepts.
Offer your assistance and schedule a time to meet regularly for study sessions.
Review statistical concepts, provide guidance on problem-solving, and encourage your peer's understanding.

Six other activities

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Join a study group for statistics

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Collaborate with peers to enhance your understanding of statistical concepts through discussions and problem-solving.

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Find or create a study group with peers who share similar learning goals.
Meet regularly to discuss course material, work on assignments together, and quiz each other.
Share insights, perspectives, and resources to deepen your collective understanding.

Explore Python resources for data analysis

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Complement your understanding of statistical concepts by delving into Python's capabilities for data manipulation and visualization.

Browse courses on Python

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Identify online tutorials or courses on Python for data analysis.
Follow the tutorials to practice writing Python code for statistical tasks, such as data cleaning and exploratory data analysis.
Experiment with Python libraries for data visualization, such as Matplotlib and Seaborn, to create informative charts and graphs.

Attend a data science workshop

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Supplement your learning by attending a workshop that provides hands-on experience with advanced statistical concepts and techniques.

Browse courses on Data Science

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Research and identify upcoming data science workshops or conferences.
Register for a workshop that aligns with your interests and learning goals.
Attend the workshop and actively participate in discussions and hands-on activities.

Solve statistical problems using Python

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Solidify your grasp of statistical techniques by applying them to practical problems using Python.

Browse courses on Statistical Inference

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Find online resources or textbooks with statistical problems and Python solutions.
Attempt to solve the problems on your own using Python code.
Compare your solutions to the provided answers and identify areas for improvement.

Participate in a data analytics competition

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Challenge yourself and apply your statistical knowledge in a competitive setting to enhance your analytical and problem-solving skills.

Browse courses on Data Analytics

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Identify and register for a data analytics competition that aligns with your interests.
Assemble a team or work individually to analyze the competition data and develop solutions.
Use statistical techniques and data visualization to support your analysis and present your findings.

Develop a case study using statistical analysis

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Apply your statistical knowledge to a real-world business scenario by creating a case study that demonstrates data-driven insights.

Browse courses on Data-Driven Decision-Making

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Identify a business problem or question that can be addressed using statistical analysis.
Collect relevant data and perform statistical analysis to extract meaningful insights.
Develop a case study report that presents the problem, analysis, and recommendations, supported by statistical evidence.

Career center

Learners who complete Statistics Using Python will develop knowledge and skills that may be useful to these careers:

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Market Research 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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Operations Research 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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Statistical Modeler

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.

See salaries and explore the career path for Risk 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Machine Learning 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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Actuary

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.

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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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Statistician

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.

Probability Theory

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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.

Probability Theory: The Logic of Science

Hardcover

$$$

Probability Theory: The Logic of Science

Kindle Edition

$$$$

Python for Data Analysis

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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.

Statistics Using Python

Two deals to help you save

What's inside

Learning objective

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Activities

Career center

Reading list

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