Interpreting Data Using Descriptive Statistics with Python from Pluralsight

This course covers measures of central tendency and dispersion needed to identify key insights in data. It also covers: correlation, covariance, skewness, kurtosis, and implementations in Python libraries such as Pandas, SciPy, and StatsModels.

The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data.

In this course, Interpreting Data using Descriptive Statistics with Python, you will gain the ability to identify the important statistical properties of your dataset and understand their implications.

First, you will explore how important measures of central tendency, the arithmetic mean, the mode, and the median, each summarize our data in different ways. Next, you will discover how measures of dispersion such as standard deviation provide clues about variation in a single variable.

Later, you will learn how your data is distributed using skewness and kurtosis and understand bivariate measures of dispersion and co-movement like correlation and covariance.

Finally, you will round out your knowledge by implementing these measures using different libraries available in Python, like Pandas, SciPy, and StatsModels.

When you are finished with this course, you will have the skills and knowledge to summarize key statistical properties of your dataset using Python.

What's inside

Syllabus

Course Overview

Understanding Descriptive Statistics

Working with Descriptive Statistics Using Pandas

Working with Descriptive Statistics Using SciPy and Statsmodels

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops key statistical concepts like central tendency and dispersion, which are foundational for data analysis

Provides hands-on experience with industry-standard Python libraries for data analysis

Covers essential statistical measures like correlation, covariance, skewness, and kurtosis

Teaches practical data summarization techniques that help identify key insights from data

Helps learners gain a foundational understanding of descriptive statistics, which is a core skill for data analysts

May require prior knowledge of Python, statistics, or data analysis for optimal comprehension

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 Descriptive Statistics with Python with these activities:

Form a study group with classmates

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Enhance your understanding through collaboration and peer support.

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Reach out to classmates and form a study group.
Schedule regular meetings to discuss the course material.
Work together to solve problems, clarify concepts, and prepare for assessments.
Provide feedback and support to each other.

Practice descriptive statistics calculations

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Reinforce your understanding of the formulas and principles behind descriptive statistics by solving practice problems.

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Review the formulas for mean, median, mode, standard deviation, skewness, and kurtosis.
Find practice problems online or in textbooks.
Solve the problems and compare your answers to the provided solutions.
Identify areas where you need more practice and focus on those.

Review Basic Statistics

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Reinforces the concepts of descriptive statistics and provides a deeper understanding of the foundational concepts.

Browse courses on Descriptive Statistics

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Identify the Measures of Central Tendency
Describe the Measures of Spread
Calculate Correlation and Covariance

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Use Python libraries to calculate descriptive statistics

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Develop proficiency in using Python libraries like Pandas, SciPy, and StatsModels for statistical calculations.

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Import the necessary libraries into your Python environment.
Create a dataset or load an existing one.
Use the appropriate functions from the libraries to calculate mean, median, mode, standard deviation, skewness, and kurtosis.
Print or visualize the results.

Attend a data analysis workshop

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Gain practical experience and insights from experts in data analysis.

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Research and identify relevant workshops in your area.
Register and attend the workshop.
Actively participate in discussions and demonstrations.
Follow up with the organizers or speakers for additional resources.

Advanced Python Libraries for Data Analysis

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Explore additional Python libraries for more advanced data analysis tasks.

Browse courses on Pandas

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Review documentation or tutorials on advanced functions in Pandas.
Install and import SciPy or StatsModels libraries.
Explore functions for specialized statistical analysis, such as hypothesis testing or time series analysis.

Practice Calculations Using Python

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Practice calculations using Python to reinforce concepts covered in the course.

Browse courses on Measures of Central Tendency

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Create a dataset with different numerical variables.
Calculate measures of central tendency (mean, median, mode) for each variable.
Calculate measures of dispersion (standard deviation, variance, range) for each variable.
Compute correlation and covariance between variables.

Practice Python Libraries for Descriptive Statistics

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Gives hands-on experience in applying statistical concepts in Python, enhancing understanding and implementation skills.

Browse courses on Pandas

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Load and manipulate data using Pandas
Calculate summary statistics using SciPy
Perform statistical modeling using StatsModels

Create a presentation on the importance of descriptive statistics

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Develop your communication and presentation skills while emphasizing the significance of descriptive statistics.

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Research the applications of descriptive statistics in various fields.
Gather real-world examples where descriptive statistics have been used to make informed decisions.
Create a visually appealing and informative presentation that highlights the importance and impact of descriptive statistics.
Practice your presentation and deliver it to your classmates or a broader audience.

Explore Online Tutorials on Advanced Statistical Techniques

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Provides exposure to advanced statistical techniques, broadening knowledge and skills in data analysis.

Browse courses on Time Series Analysis

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Identify relevant online tutorials
Complete tutorials and practice exercises

Data Analysis Project

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Apply descriptive statistics concepts to analyze a dataset and create visualizations.

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Choose a dataset that aligns with the course content.
Perform exploratory data analysis and identify key statistical properties.
Create visualizations to represent the data and its distributions.
Write a report summarizing the findings and insights gained.

Write a Blog Post on a Statistical Concept

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Encourages a deeper understanding and articulation of statistical concepts through written explanation, fostering critical thinking and communication skills.

Browse courses on Correlation Analysis

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Select a statistical concept
Research and gather information
Write a clear and informative blog post

Visualize descriptive statistics findings

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Enhance your ability to present statistical findings effectively by creating visualizations.

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Choose appropriate visualization techniques for the type of data and statistics calculated.
Use Python libraries like Matplotlib or Seaborn to create visualizations.
Consider the audience and context when selecting visual elements and labels.
Present your visualizations in a clear and concise manner.

Explore advanced topics in descriptive statistics

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Deepen your understanding of descriptive statistics by exploring advanced concepts.

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Identify gaps in your knowledge or areas where you want to expand.
Find relevant tutorials, articles, or online courses on those topics.
Follow the tutorials and complete any exercises or assignments provided.
Discuss your findings and questions with classmates or instructors.

Contribute to an open-source project related to descriptive statistics

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Engage with the broader community and make meaningful contributions to the field of descriptive statistics.

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Identify open-source projects related to descriptive statistics on platforms like GitHub.
Review the project's documentation and identify areas where you can contribute.
Communicate with the project maintainers and propose your contributions.
Make code contributions, write documentation, or participate in discussions.

Career center

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

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. Knowledge of descriptive statistics is essential for this role, as it allows Data Analysts to summarize and interpret data in a meaningful way. This course provides a solid foundation in descriptive statistics, covering measures of central tendency, dispersion, skewness, kurtosis, and correlation. Understanding these concepts will enable you to effectively analyze data and extract valuable insights.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use data to make recommendations on investments and financial decisions. A strong understanding of descriptive statistics is crucial for this role, as it allows Financial Analysts to interpret financial data and make informed decisions. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are essential for analyzing financial data. By completing this course, you will gain the skills necessary to succeed as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior and market trends. Descriptive statistics play a vital role in this field, as they help Market Researchers summarize and interpret data to gain insights into consumer preferences and market dynamics. This course provides a comprehensive understanding of measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are essential for conducting market research and making data-driven decisions.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems and improve efficiency. Descriptive statistics are essential for this role, as they allow Operations Research Analysts to analyze data and identify patterns and trends. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Operations Research Analysts. By completing this course, you will gain the skills necessary to succeed in this field.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. A strong foundation in descriptive statistics is essential for this role, as it allows Quantitative Analysts to interpret financial data and make informed decisions. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are crucial for analyzing financial data. By completing this course, you will gain the skills necessary to succeed as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Data Scientist

Data Scientists use data to solve business problems and improve decision-making. Descriptive statistics play a fundamental role in this field, as they provide a foundation for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are essential for Data Scientists. By completing this course, you will gain the skills necessary to succeed in this rapidly growing field.

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Statistician

Statisticians collect, analyze, and interpret data to solve problems and make informed decisions. A strong foundation in descriptive statistics is essential for this role, as it provides the basis for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Statisticians. By completing this course, you will gain the skills necessary to succeed in this field.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use data to analyze business processes and identify areas for improvement. Descriptive statistics are essential for this role, as they provide a foundation for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Business Analysts. By completing this course, you will gain the skills necessary to succeed in this field.

See salaries and explore the career path for Business Analyst

Economist

Economists use data to analyze economic trends and make policy recommendations. Descriptive statistics play a vital role in this field, as they provide a foundation for understanding and interpreting economic data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are essential for Economists. By completing this course, you will gain the skills necessary to succeed in this field.

See salaries and explore the career path for Economist

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. Descriptive statistics are essential for this role, as they provide a foundation for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Actuaries. By completing this course, you will gain the skills necessary to succeed in this field.

See salaries and explore the career path for Actuary

Biostatistician

Biostatisticians use statistical methods to analyze biological data and make informed decisions. Descriptive statistics are essential for this role, as they provide a foundation for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Biostatisticians. By completing this course, you will gain the skills necessary to succeed in this field.

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Epidemiologist

Epidemiologists use statistical methods to study the distribution and determinants of health-related states and events in specified populations. Descriptive statistics are essential for this role, as they provide a foundation for understanding and interpreting data. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which are fundamental concepts for Epidemiologists. By completing this course, you will gain the skills necessary to succeed in this field.

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Software Engineer

Software Engineers design, develop, and maintain software systems. While not directly related to descriptive statistics, this course may be useful for Software Engineers who need to analyze data to improve the performance or functionality of their software systems. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which can be applied to software performance analysis and optimization.

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Computer Scientist

Computer Scientists conduct research and develop new computing technologies. While not directly related to descriptive statistics, this course may be useful for Computer Scientists who need to analyze data to evaluate the performance or efficiency of new algorithms or systems. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which can be applied to data analysis in computer science.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. While not directly related to descriptive statistics, this course may be useful for Data Engineers who need to analyze data to optimize the performance or efficiency of their data pipelines or infrastructure. This course covers measures of central tendency, dispersion, skewness, kurtosis, and correlation, which can be applied to data analysis in data engineering.

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