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The General Data Science and R online test assesses a candidate’s ability to analyze data, extract information, suggest conclusions, and support decision-making. It also tests a candidate’s knowledge of programming in the R language and their ability to leverage commonly used built-in functions.

It's the ideal test for pre-employment screening. Data scientists and data analysts who use R for their tasks should be able to leverage the functionality provided by R’s standard libraries to extract and analyze knowledge and insights from data.

This test requires candidates to demonstrate their ability to apply probability and statistics when solving data science problems, and solve coding problems in the R language.

A classifier that predicts if an image contains only a cat, a dog, or a llama produced the following confusion matrix:

True values | ||||

Dog | Cat | Llama | ||

Predicted values | Dog | 14 | 2 | 1 |

Cat | 2 | 12 | 3 | |

Llama | 5 | 2 | 19 |

What is the accuracy of the model, in percentages?

Implement the function *findRoots* to find the roots of the quadratic equation: ax^{2} + bx + c = 0. The function should return a vector containing roots in any order. If the equation has only one solution, the function should return that solution as both elements of the vector. The equation will always have at least one solution.

The roots of the quadratic equation can be found with the following formula:

For example, *findRoots(2, 10, 8)* should return a vector containing *(-1, -4)* or *(-4, -1)* as the roots of the equation 2x^{2} + 10x + 8 = 0 are -1 and -4.

Two bacteria cultures, A and B, were set up in two different dishes, each covering 50% of its dish. Over 20 days, bacteria A's percentage of coverage increased to 70% and bacteria B's percentage of coverage reduced to 40%:

Earn a free certificate by achieving top 25% on the R and General Data Science test with public questions.

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- General Data Science
- Linear Regression
- Outliers
- Bayes' Theorem
- Probability
- Classification
- Decision Boundary
- Poisson Distribution
- Correlation
- Multicollinearity
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- Decision Tree
- Machine Learning
- Binomial Distribution
- P-Value
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- Curve Fitting
- R
- Data Cleaning
- DataFrame
- Arithmetic
- Iteration
- Vector
- Grouping

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