10+ Decision Scientist Interview Questions and Answers for Freshers

I stay up to date with new analytical tools and techniques by attending workshops, online courses, reading research papers, and participating in industry conferences.

• Attend workshops and training sessions on new tools and techniques

• Take online courses and certifications to learn about the latest advancements

• Read research papers and articles to stay informed about cutting-edge methods

• Participate in industry conferences and networking events to exchange knowledge and ideas

Z test is used when sample size is large and population standard deviation is known. T test is used when sample size is small or population standard deviation is unknown.

• Z test is used for hypothesis testing when sample size is large (n > 30) and population standard deviation is known.

• T test is used when sample size is small (n < 30) or population standard deviation is unknown.

• Z test is used for comparing means of two populations when the population standard deviation is know...read more

Chi square test is a statistical test used to determine if there is a significant association between two categorical variables.

• Chi square test is used to compare observed frequencies with expected frequencies in a contingency table.

• It is commonly used in research to analyze data and determine if there is a relationship between two variables.

• For example, it can be used to test if there is a significant difference in the distribution of a disease between two groups.

• Another exa...read more

Utilized K-means clustering to group customers based on purchasing behavior. Evaluated clusters using silhouette score and inertia.

• Used K-means clustering algorithm to group customers into segments

• Evaluated the quality of clusters using silhouette score and inertia

• Silhouette score measures how similar an object is to its own cluster compared to other clusters

• Inertia measures how tightly the clusters are packed together

• Example: Clustered customers based on demographics and pur...read more

Developed predictive model to forecast customer churn using machine learning algorithms

• Collected and cleaned customer data from various sources

• Performed feature engineering to create relevant predictors

• Built and trained machine learning models such as logistic regression and random forest

• Evaluated model performance using metrics like accuracy, precision, and recall

• Implemented the model in a production environment for real-time predictions

Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

• Central Limit Theorem is a fundamental concept in statistics.

• It states that the sampling distribution of the sample mean will be approximately normally distributed regardless of the shape of the population distribution.

• As the sample size increases, the sampling distribution of the sample mean becomes more normally distributed.

• It is used ...read more

K-means clustering is a popular unsupervised machine learning algorithm used for clustering data points into groups based on similarity.

• Divides data points into K clusters based on similarity

• Minimizes the sum of squared distances within each cluster

• Requires specifying the number of clusters (K) beforehand

• Iteratively assigns data points to the nearest cluster centroid

• Commonly used in customer segmentation, image compression, and anomaly detection

Precision-Recall AUC is a metric used to evaluate the performance of classification models, particularly in imbalanced datasets.

• Precision-Recall AUC focuses on the trade-off between precision and recall for different threshold values.

• It is particularly useful when dealing with imbalanced datasets where the positive class is rare.

• A higher Precision-Recall AUC indicates better model performance in terms of precision and recall.

• It is often used in conjunction with the ROC AUC me...read more

Cross validation is a technique used to evaluate the performance of a machine learning model by testing it on multiple subsets of the data.

• It involves dividing the data into multiple subsets or folds.

• The model is trained on a subset and tested on the remaining subset.

• This process is repeated for all subsets and the results are averaged to get a final performance metric.

• It helps to prevent overfitting and provides a more accurate estimate of the model's performance.

• Examples in...read more

A confusion matrix is a table used to evaluate the performance of a classification model.

• It shows the number of true positives, true negatives, false positives, and false negatives.

• It helps in calculating various evaluation metrics like accuracy, precision, recall, and F1 score.

• It is useful in comparing the performance of different models.

• Example: A confusion matrix for a binary classification problem can be represented as follows: | | Predicted Positive | Predicted Negative ...read more