Data Science Analysis Quiz

When hiring a Data Scientist, communication skills are crucial. This trait is essential because Data Scientists need to effectively communicate their findings and insights to both technical and non-technical stakeholders. They should be able to explain complex concepts in a clear and concise manner, as well as collaborate and work effectively with team members. Without strong communication skills, a Data Scientist may struggle to convey their ideas and findings, hindering the overall success and impact of their work.

The most basic need of data science is to collect data. Data collection is the first step in the data science process, as it involves gathering relevant and reliable data from various sources. Without data collection, there would be no data to analyze and derive insights from. Collecting data allows data scientists to have a foundation for their analysis and modeling, enabling them to make informed decisions and predictions based on the data collected.

The rules that will be kept in the analysis are B and D. This is because these rules have a confidence greater than 60%. Rule B states that if someone is a renter, they have good credit. Rule D states that if someone is a home owner, they have good credit. Both of these rules meet the criteria for the analysis, as their confidence is greater than 60%.

Association Rules is the technique used to identify pages commonly viewed during the same visit to the website. It analyzes the patterns and relationships between different pages visited by users and identifies the frequent co-occurrence of pages. This technique helps in understanding the behavior of visitors and can be used for various purposes such as recommending related pages or products, optimizing website layout, and improving user experience.

Document B is the most relevant to the analyst's search because it has the highest term frequency for the term "solid-state disk" compared to the other documents. The term frequency-inverse document frequency (TF-IDF) is a measure that indicates the importance of a term in a document relative to the entire corpus. In this case, the term "solid-state disk" is more frequent in Document B compared to the other documents, suggesting that it is more relevant to the analyst's search.

A Wilcoxson Rank Sum test, also known as the Mann-Whitney U test, is used when you cannot make an assumption about the distribution of the populations. This test is a non-parametric alternative to the independent samples t-test and does not require the assumption of normality. It is suitable when the data is ordinal or when the distribution is skewed or has outliers. The test compares the ranks of the observations between two independent groups to determine if there is a significant difference between them.

The decision tree provides the prediction for whether someone is a good or bad credit risk. The assigned probability, p(good), for a single male with no known savings would be 0.83.

The concern the data scientist should have about the data being "too processed" is that it may have undergone extensive transformations during the ETL process, which could potentially lead to loss or distortion of the original data. This could affect the accuracy and reliability of the model built using this data. It is important for a data scientist to have access to raw and unprocessed data to ensure the integrity of the model and to be able to perform necessary data cleaning and preprocessing steps.

The R-squared is a statistical measure that represents the proportion of the variance in the dependent variable that can be explained by the independent variables in a regression model. An R-squared value of 0.75 indicates that 75% of the variance in the true outcome can be explained by the predicted outcome. Generally, an R-squared value between 0.7 and 0.8 is considered good, suggesting that the model is able to explain a significant amount of the variation in the data. Therefore, the assessment of the model in this case is that it should perform well.

MADlib is the correct answer because it is specifically designed for large-scale machine learning tasks on relational databases. It provides a set of in-database algorithms, including logistic regression, that can be applied directly to the data stored in PostgreSQL. This means that there is no need to extract the data from the database and transfer it to another tool or library, reducing the effort required to produce the logistic regression models for the 100 countries.

The data scientist should try K Means Clustering first. K Means Clustering is a method used to group similar data points together based on their characteristics. In this case, the data scientist can use K Means Clustering to group articles based on their style and subject matter. By doing so, they can recommend articles that are similar in style and subject matter to the current article being read by the user. This method does not require any client tracking technologies and can be implemented using the available data in the database.

Based on the provided training dataset and the tuple X(1, 0, 0), we can calculate the classification and probability using a Naive Bayesian classifier.

The calculation involves estimating the conditional probabilities of each feature given the class (Y = 0 or Y = 1), as well as the prior probabilities of each class.

Here's how to calculate it:

Prior probabilities:

P(Y = 0) = 2/5

P(Y = 1) = 3/5

Conditional probabilities:

P(X1 = 1 | Y = 0) = 1/2

P(X2 = 0 | Y = 0) = 1/2

P(X3 = 0 | Y = 0) = 1/2

P(X1 = 1 | Y = 1) = 2/3

P(X2 = 0 | Y = 1) = 1/3

P(X3 = 0 | Y = 1) = 1/3

Now, let's calculate the probabilities for the tuple X(1, 0, 0):

P(Y = 0 | X = (1, 0, 0)) ∝ P(X = (1, 0, 0) | Y = 0) * P(Y = 0) = (1/2) * (1/2) * (1/2) * (2/5) = 1/20

P(Y = 1 | X = (1, 0, 0)) ∝ P(X = (1, 0, 0) | Y = 1) * P(Y = 1) = (2/3) * (1/3) * (1/3) * (3/5) = 2/45

Now, we normalize these probabilities:

P(Y = 0 | X = (1, 0, 0)) = (1/20) / ((1/20) + (2/45)) P(Y = 1 | X = (1, 0, 0)) = (2/45) / ((1/20) + (2/45))

After normalization, we find the probabilities to be:

Classification Y = 0, Probability ≈ 4/54

Classification Y = 1, Probability ≈ 4/54

Therefore, the correct option is:

Classification Y = 0, Probability ≈ 4/54

If a MapReduce job is successfully submitted but not completed, it indicates that there might be an issue with the TaskTracker. The TaskTracker is responsible for running the individual tasks of the MapReduce job. Therefore, ensuring that the TaskTracker is running is crucial for the successful completion of the job.

The UNION clause in a SQL statement is used to combine the result sets of two or more SELECT statements into a single result set. It operates on queries and potentially decreases the number of rows because it eliminates duplicate rows from the combined result set.

The Reduce function in the MapReduce framework is responsible for aggregating the results generated by the Map function. It takes the intermediate key-value pairs produced by the Map function and combines them based on the key. The Reduce function then generates processed output by performing operations such as summing, averaging, or counting on the grouped values. This processed output is typically written to storage or used for further analysis. However, the purpose of the Reduce function is not to distribute the input to multiple nodes for processing, as this task is handled by the Map function.