- Statistics and Probability Knowledge Checklist
- Top 18 Probability and Statistics Interview Questions for Data Scientists
- What Is the Difference Between Descriptive and Inferential Statistics?
- What Are the Main Measures Used to Describe the Central Tendency of Data?
- What Are the Main Measures Used to Describe the Variability of Data?
- What Are Skewness and Kurtosis?
- Describe the Difference Between Correlation and Autocorrelation
- Explain the Difference Between Probability Distribution and Sampling Distribution
- What Is the Normal Distribution and How Is It Characterized?
- What Are the Assumptions of Linear Regression?
- What Is Hypothesis Testing?
- What Are the Most Common Statistical Tests Used?
- What Is the P-Value and How Can We Interpret It?
- What Is the Confidence Interval?
- What Are the Main Ideas of the Law of Large Numbers?
- What Is the Central Limit Theorem?
- What's the Difference Between Population and Sample in Data Analysis?
- The Difference Between Probability and Likelihood
- What's Your Knowledge of Statistics, and How Have You Used it as a Data Analyst?
- How to Data Scientists Use Statistics?
- General Interview Tips & Tricks
- Probability and Statistics Interview Questions for Data Scientists: Next Steps
The 10 Most Important Packages in R for Data Science
The 10 Most Important Packages in R for Data Science Learn about different packages in R used for data science. Including how to load them and different resources you can use to advance your skills with them. R is the most popular language for Data Science. There are many packages and libraries provided for doing different tasks. For example, there is dplyr and data.table for data manipulation, whereas libraries like ggplot2 for data visualization and data cleaning library like tidyr . Also, there is a library like 'Shiny' to create a Web application and knitr for the Report generation where finally mlr3 , xgboost , and caret are used in Machine Learning. 1. ggplot2 ggplot2 is based on the 'Grammar of Graphics", which is a popular data visualization library. Graphs with one variable, two variables, and three variables, along with both categorical and numerical data, can be built. Also, grouping can...
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