By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data.
Basic Statistics in Python (Correlations and T-tests)
Instructor: Laura Gemmell
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(34 reviews)
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What you'll learn
Clean data, including removing missing data and unnecessary columns
Explore data using descriptive statistics
Create visualizations of our data and analysis
Perform statistical tests, including t-tests and correlation
Skills you'll practice
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Set up a new Google Colab notebook
Load data into notebook
Clean data to remove missing data and unnecessary columns
Perform exploratory data analysis
Create boxplots using for loops
Explore relationships between data using correlation and scatter plots
Perform t-tests to compare data from different years
Recommended experience
Knowledge of statistics, including p-values, significance, t-tests and correlation
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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Reviewed on Dec 3, 2020
This is a very helpful tool in research. Thank you very much!
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