Interpreting Data Using Statistical Models with Python from Pluralsight

This course covers techniques from inferential statistics, including hypothesis testing, t-tests, and Pearson’s chi-squared test, along with ANOVA, which is used to analyze effects across categorical variables and the interaction between variables.

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 modeling tools, so what differs is how those models are applied to the data. Today, more than ever, it is really important that you know your data well.

In this course, Interpreting Data using Statistical Models with Python you will gain the ability to go one step beyond visualizations and basic descriptive statistics, by harnessing the power of inferential statistics.

First, you will learn how hypothesis testing, which is the foundation of inferential statistics, helps posit and test assumptions about data. Next, you will discover how the classic t-test can be used in a variety of common scenarios around estimating means. You will also learn about related tests such as the Z-test, Pearson’s Chi-squared test, Levene’s test and Welch’s t-test for dealing with populations that have unequal variances.

Finally, you will round out your knowledge by using ANOVA, a powerful statistical technique used to measure statistical properties across different categories of data. When you’re finished with this course, you will have the skills and knowledge to use powerful techniques from hypothesis testing, including t-tests, ANOVA and regression tests in order to measure the strength of statistical relationships within your data.

What's inside

Syllabus

Course Overview

Understanding Inferential Statistics

Performing Hypothesis Testing in Python

Implementing Predictive Models for Continuous Data

Implementing Predictive Models for Categorical Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches essential statistical techniques for analyzing data, such as hypothesis testing and ANOVA

Instructed by experienced academic Janani Ravi, who has a strong reputation in inferential statistics

Covers advanced statistical concepts, making it suitable for intermediate learners and practitioners

Emphasizes practical applications of statistical models, enhancing its relevance for real-world scenarios

Requires prior knowledge of basic statistics, which may limit accessibility for complete beginners

Does not provide hands-on practice through labs or interactive materials

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 Interpreting Data Using Statistical Models with Python with these activities:

Review Probability and Statistics

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Review basic probability and statistics concepts to strengthen the foundation for inferential statistics.

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Go over notes or textbooks from previous probability and statistics courses.
Complete practice problems and exercises to test your understanding.

Refresh Knowledge on Inferential Statistics

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Strengthen your foundation in inferential statistics before starting the course.

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Review notes and textbooks on inferential statistics.
Solve practice problems or complete online quizzes.

Review: Statistical Modeling and Computation by Edward George

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Expand your knowledge of statistical modeling and computation by delving into this comprehensive text.

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Read chapters relevant to hypothesis testing and statistical models.
Solve practice problems to test your understanding.

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Join a Study Group for Inferential Statistics

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Engage with peers in a study group to discuss concepts, solve problems, and enhance your understanding of inferential statistics through collaborative learning.

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Find or create a study group with classmates or fellow learners.
Meet regularly to discuss course material, work on problems together, and share insights.
Encourage active participation and open discussions.

Guided Tutorials on t-tests and ANOVA

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Enhance your understanding of t-tests and ANOVA through structured tutorials led by experienced professionals.

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Follow video tutorials explaining the concepts of t-tests and ANOVA.
Complete interactive exercises to practice applying these techniques.
Discuss your findings and questions with peers or instructors.

Solve practice problems on hypothesis testing

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Reinforce understanding of hypothesis testing by applying it to practical scenarios.

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Find a set of practice problems
Solve the problems using the appropriate hypothesis testing techniques
Check your answers and identify areas for improvement

Follow tutorials on ANOVA and its applications

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Expand understanding of ANOVA and its practical applications by exploring tutorials and demonstrations.

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Search for tutorials on ANOVA
Watch or read the tutorials
Follow along with the examples and practice exercises

Solve Hypothesis Testing Practice Problems

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Engage in repeated practice of hypothesis testing problems to improve understanding and strengthen problem-solving skills.

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Find online resources or textbooks with practice problems.
Solve problems independently, focusing on understanding the concepts.
Check your answers and identify areas for improvement.

Practice Hypothesis Testing in Python

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Reinforce the concepts of hypothesis testing by completing a series of exercises and challenges.

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Identify the null and alternative hypotheses.
Determine the appropriate statistical test.
Calculate the test statistic.
Determine the p-value.
Make a decision about the null hypothesis.

Follow Tutorials on T-tests and ANOVA

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Supplement course material by following guided tutorials specifically on t-tests and ANOVA, enhancing your understanding of these techniques.

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Search for reputable online tutorials or video courses.
Follow the tutorials step-by-step, taking notes and practicing the concepts.
Complete any quizzes or exercises provided within the tutorials.

Peer-Assisted Problem-Solving

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Enhance your understanding by collaborating with peers and discussing course-related problems.

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Form study groups with classmates.
Identify challenging concepts or practice problems.
Collaboratively solve problems and exchange knowledge.

Write a blog post summarizing key concepts

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Demonstrate understanding by creating a written summary of key concepts, theories, and applications discussed in the course.

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Choose a concept to focus on
Research and gather relevant information
Organize your thoughts and outline the post
Write the first draft
Revise and edit the post

Create a Regression Test Report

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Apply your knowledge of regression testing by developing a comprehensive report on a data analysis project.

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Gather data and explore its distribution.
Select and train a regression model.
Evaluate the model's performance.
Interpret the results and draw conclusions.

Conduct a Mini Data Analysis Project Using Inferential Statistics

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Apply your knowledge of inferential statistics by conducting a mini project, solidifying your understanding through practical application.

Browse courses on Hypothesis Testing

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Define a research question and gather a small dataset.
Apply hypothesis testing techniques to analyze the data.
Write a brief report summarizing your findings and conclusions.

Kaggle Competitions on Inferential Statistics

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Test your skills and benchmark your progress by participating in Kaggle competitions focused on inferential statistics.

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Identify relevant competitions on the Kaggle website.
Analyze data and develop statistical models.
Submit your solutions and track your performance.

Contributions to Inferential Statistics Libraries

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Deepen your understanding by contributing to open-source libraries for implementing inferential statistics techniques.

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Identify open-source libraries such as SciPy or Statsmodels.
Study the codebase and identify areas for improvement.
Develop and submit pull requests with code contributions.

Career center

Learners who complete Interpreting Data Using Statistical Models with Python will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians conceive, design, conduct, analyze, and interpret statistical studies. They develop new statistical theory and methods and apply statistical methods to scientific research. The main topics this course covers, inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, are core skills applied by statisticians daily. This course may help you build a strong foundation for a career as a statistician, especially if you plan on applying statistical methods to scientific research.

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Data Scientist

Data scientists analyze data, using statistics and machine learning, in order to uncover patterns and trends. They use their findings to make recommendations to businesses and organizations. Many of the topics in this course, which include hypothesis testing, t-tests, chi-squared test, and ANOVA, are skills that data scientists commonly use to extract actionable insights from data. This course may be useful for building a strong foundation for a career as a data scientist, especially if you plan on specializing in statistical modeling.

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Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze data and make recommendations for investment decisions. Some of the topics in this course, which include hypothesis testing, t-tests, chi-squared test, and ANOVA, are skills that quantitative analysts use to interpret data to make informed investment decisions. This course may be useful for building a strong foundation for a career as a quantitative analyst, especially if you plan to apply statistical techniques to your investment models.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

Market researchers design, conduct, and analyze surveys and other studies to gather information about consumer behavior. The main topics this course covers, inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, are core skills used by market researchers to analyze the needs and wants of consumers. This course may help you build a strong foundation for a career as a market researcher, especially if you plan to specialize in statistical modeling.

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Biostatistician

Biostatisticians apply statistical methods to biological, medical, and health-related data. Many of the topics in this course, which include hypothesis testing, t-tests, chi-squared test, and ANOVA, are skills that biostatisticians use to analyze and interpret clinical data. This course may help you build a strong foundation for a career as a biostatistician, especially if you plan to work in the field of pharmaceuticals or medical research.

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Epidemiologist

Epidemiologists investigate the causes of disease and injury in populations. Some of the topics covered in this course, which include hypothesis testing, t-tests, and chi-squared test, are skills that epidemiologists commonly use to analyze and interpret epidemiological data. This course may be useful for building a strong foundation for a career as an epidemiologist, especially if you plan to study the causes and spread of infectious diseases.

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Operations Research Analyst

Operations research analysts develop and use mathematical models to solve complex problems in business and industry. Several topics in this course, including hypothesis testing, t-tests, chi-squared test, and ANOVA, are common statistical techniques used by operations research analysts to build and validate mathematical models. This course may be useful for building a strong foundation for a career as an operations research analyst, especially if you plan to work in the field of logistics or supply chain management.

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Financial Analyst

Financial analysts use financial data to evaluate and make recommendations on investments. The main topics this course covers, inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, are core skills applied by financial analysts to analyze financial data and make investment decisions. This course may help you build a strong foundation for a career as a financial analyst, especially if you plan to work in the field of investment banking or asset management.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. Some of the topics in this course, which include hypothesis testing, t-tests, chi-squared test, and ANOVA, are essential skills that actuaries use to calculate insurance premiums and plan for future financial risks. This course may be useful for building a strong foundation for a career as an actuary, especially if you plan to work in the field of insurance or risk management.

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Data Analyst

Data analysts collect, clean, and analyze data to extract meaningful insights. Many of the topics in this course, which include hypothesis testing, t-tests, chi-squared test, and ANOVA, are skills that data analysts use to clean and analyze data. This course may be useful for building a strong foundation for a career as a data analyst, especially if you plan to specialize in statistical analysis.

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Machine Learning Engineer

Machine learning engineers design, build, and maintain machine learning models. Some of the topics in this course, which include hypothesis testing, t-tests, and chi-squared test, are fundamental statistical skills that machine learning engineers use to develop and evaluate machine learning models. This course may be useful for building a strong foundation for a career as a machine learning engineer, especially if you plan to work in the field of artificial intelligence.

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Software Engineer

Software engineers design, develop, and maintain software systems. This course covers inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, which are statistical methods used in various areas of software engineering, such as software testing and quality assurance. While this course may not be directly related to the core responsibilities of a software engineer, it can provide a strong foundation for understanding and applying statistical methods in software development.

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Business Analyst

Business analysts use data to analyze business processes and make recommendations for improvement. The main topics this course covers, inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, are core skills used by business analysts to analyze data and make recommendations for improving business processes. This course may help you build a strong foundation for a career as a business analyst, especially if you plan to specialize in data analysis or process improvement.

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Product Manager

Product managers oversee the development and launch of new products. Some of the topics in this course, which include hypothesis testing, t-tests, and chi-squared test, are essential skills that product managers use to test and validate new product ideas. This course may be useful for building a strong foundation for a career as a product manager, especially if you plan to work in the field of technology or consumer products.

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Consultant

Consultants provide advice and guidance to businesses and organizations. The main topics this course covers, inferential statistics, hypothesis testing, t-tests, Pearson's chi-squared test, and ANOVA, are core skills used by consultants to analyze data and make recommendations to clients. This course may help you build a strong foundation for a career as a consultant, especially if you plan to specialize in data analysis or business strategy.

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