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Personen, die in einem bestimmten Fach besonders gut sind, können ihre Erfahrung und ihr Wissen nutzen, um als Tutor andere zu unterstützen. Vorstellungsgespräche bestehen in der Regel aus fachspezifischen Fragen zur Beurteilung Ihres akademischen Wissens. Sie sollten auch damit rechnen, einige Verhaltens- und Situationsfragen beantworten zu müssen, z. B. wie Sie desinteressierte oder entmutigte Schüler und Schülerinnen oder Studierende motivieren würden. Zu den gefragten Eigenschaften zählen zwischenmenschliche Fähigkeiten sowie ausgeprägte Kompetenzen hinsichtlich Problemlösung und Zielsetzung.

Typische Bewerbungsfragen als Tutor (m/w/d) und wie Sie diese beantworten

Question 1

Frage 1: Warum möchten Sie im Bildungsbereich tätig sein und mit Kindern oder Studierenden arbeiten?

How to answer
So beantworten Sie die Frage: Hier können Sie Ihre Leidenschaft für Bildung und die Förderung von Kindern oder älteren Lernenden ansprechen. Ein Tutor zu sein bedeutet Engagement für Bildung, und hier können Sie aufzeigen, warum Sie so engagiert in diesem Feld sind.
Question 2

Frage 2: Was ist Ihr Stil als Tutor?

How to answer
So beantworten Sie die Frage: Bei dieser Frage können Sie erläutern, wie Sie an Ihre Arbeit als Tutor herangehen. Sie können ausführen, wie Sie Ihren Ansatz bei jedem/r Lernenden personalisieren, was Sie tun, damit Lernen mehr Spaß macht usw. Hier können Sie wirklich für sich und Ihre Lehrkompetenz als professioneller Tutor werben.
Question 3

Frage 3: Wie würden Sie einen Schüler motivieren, sich für etwas zu interessieren?

How to answer
So beantworten Sie die Frage: Es gibt viele Möglichkeiten, diese Frage zu beantworten, doch sollte jede Antwort Ihre Fähigkeit darlegen, sich gezielt um die Schüler zu bemühen und deren Aufmerksamkeit zu erregen. Ob dies Belohnungen, einen Appell an ihre aktuellen Interessen oder beliebige andere Taktiken beinhaltet, liegt an Ihnen und Ihrem persönlichen Lehrstil.

10,598 tutor interview questions shared by candidates

This should be completed in no more than 3 hours. 1. When would I use a Python dictionary to store data, instead of another data structure? Python Dictionary can be used to store data when the data consist of key-value pairs . It also helps in data lookups. 2. When writing Python code, why would you write a function? Functions are define for the purposes of computations and code re-usability 3. I have a Python list named mylist. What is the difference between `sorted(mylist)` and `mylist.sort()`? With sorted(mylist) : Return a new list containing all items from the iterable in ascending order. And with mylist.sort() : Returns None because it is not iterable. 4. I have a Pandas DataFrame named users. One of the Series is named age. I run this code: `users[users.age > 25]`. What does this code return, and how does it "work"? The code selects the ages column and filter out all ages greater than 25 based on the users DataFrame object. 5. What is the difference between a bar plot and a histogram? Histogram shows the distributions of some variables whiles bar plot is used to compare variables with respect to a certain descriptive statistics. 6. What is the difference between regression and classification problems, and how can you convert a regression problem into a classification problem? Regression problem is the one that predicts some continuous variable whiles classification problem is the one that predicts some binary or multi-class variable. You can convert a regression problem into a classification problem by adding a binary or multi-class response variable to the independent variables. But if you decide not to add a binary or multi-class response variable you can apply clustering technique like Kmeans on the data to make it a classification problem. 7. When Netflix predicts the number of stars that you will use to rate a movie, are they solving a regression or classification problem? They are solving a classification problem since the ratings are in the range of 1-5 stars. 8. What's wrong with training and testing a machine learning model on the same data? It is important not to test the prediction of an estimator on the data used to fit the estimator as this would not be evaluating the performance of the estimator on new data. This is why datasets are often split into train and test data. 9. What is the purpose of model evaluation procedures that "hold out" data, like train/test split and cross-validation? The purpose of the “hold out” like train/test split is used to prevent the introduction of bias into the model and hence Over-Fitting. 10. What are a few metrics (single numbers) I might use to evaluate how well a regression model is doing? R-Square, Mean Absolute Error (MAE), Mean Squared Error (MSE), Median Absolute Error 11. Why is a confusion matrix useful for evaluating the performance of a classifier? The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions. It also helps to determine the True positives, False Negative, True Negative etc. 12. I built a machine learning model to solve a binary classification problem. The accuracy of my classifier is 99%, and the AUC is 0.51. Is my model doing well? Why or why not? The model is not doing well because the AUC is about 0.51, which means that the classifier is not able to differentiate between the labels, hence a random classifier. The higher the AUC, the better the model and vice versa. However, the AUC is computed with the prediction scores specified as a parameter in the AUC method. 13. With regard to machine learning models, why should I care the about bias-variance trade-off? We should care because bias introduces under-fitting into the model whiles variance introduces over-fitting into the model, so we need to worry about this issue so that our model can generalize well to unseen data. 14. How does regularization reduce overfitting? Regularization reduce over-fitting by penalizing the parameters in the model as a result of using L1 or L2 penalty regularization terms. When we add a regularization term to the loss function, we shrink the weights a little, increasing the bias while reducing variance. 15. What are a few advantages and disadvantages of decision trees, as compared to other machine learning models? Advantages of Decision Trees: Disadvantages of Decision Trees:
avatar

Data Scientist Tutor

Interviewed at General Assembly

3.6★
Oct 14, 2016

This should be completed in no more than 3 hours. 1. When would I use a Python dictionary to store data, instead of another data structure? Python Dictionary can be used to store data when the data consist of key-value pairs . It also helps in data lookups. 2. When writing Python code, why would you write a function? Functions are define for the purposes of computations and code re-usability 3. I have a Python list named mylist. What is the difference between `sorted(mylist)` and `mylist.sort()`? With sorted(mylist) : Return a new list containing all items from the iterable in ascending order. And with mylist.sort() : Returns None because it is not iterable. 4. I have a Pandas DataFrame named users. One of the Series is named age. I run this code: `users[users.age > 25]`. What does this code return, and how does it "work"? The code selects the ages column and filter out all ages greater than 25 based on the users DataFrame object. 5. What is the difference between a bar plot and a histogram? Histogram shows the distributions of some variables whiles bar plot is used to compare variables with respect to a certain descriptive statistics. 6. What is the difference between regression and classification problems, and how can you convert a regression problem into a classification problem? Regression problem is the one that predicts some continuous variable whiles classification problem is the one that predicts some binary or multi-class variable. You can convert a regression problem into a classification problem by adding a binary or multi-class response variable to the independent variables. But if you decide not to add a binary or multi-class response variable you can apply clustering technique like Kmeans on the data to make it a classification problem. 7. When Netflix predicts the number of stars that you will use to rate a movie, are they solving a regression or classification problem? They are solving a classification problem since the ratings are in the range of 1-5 stars. 8. What's wrong with training and testing a machine learning model on the same data? It is important not to test the prediction of an estimator on the data used to fit the estimator as this would not be evaluating the performance of the estimator on new data. This is why datasets are often split into train and test data. 9. What is the purpose of model evaluation procedures that "hold out" data, like train/test split and cross-validation? The purpose of the “hold out” like train/test split is used to prevent the introduction of bias into the model and hence Over-Fitting. 10. What are a few metrics (single numbers) I might use to evaluate how well a regression model is doing? R-Square, Mean Absolute Error (MAE), Mean Squared Error (MSE), Median Absolute Error 11. Why is a confusion matrix useful for evaluating the performance of a classifier? The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. The higher the diagonal values of the confusion matrix the better, indicating many correct predictions. It also helps to determine the True positives, False Negative, True Negative etc. 12. I built a machine learning model to solve a binary classification problem. The accuracy of my classifier is 99%, and the AUC is 0.51. Is my model doing well? Why or why not? The model is not doing well because the AUC is about 0.51, which means that the classifier is not able to differentiate between the labels, hence a random classifier. The higher the AUC, the better the model and vice versa. However, the AUC is computed with the prediction scores specified as a parameter in the AUC method. 13. With regard to machine learning models, why should I care the about bias-variance trade-off? We should care because bias introduces under-fitting into the model whiles variance introduces over-fitting into the model, so we need to worry about this issue so that our model can generalize well to unseen data. 14. How does regularization reduce overfitting? Regularization reduce over-fitting by penalizing the parameters in the model as a result of using L1 or L2 penalty regularization terms. When we add a regularization term to the loss function, we shrink the weights a little, increasing the bias while reducing variance. 15. What are a few advantages and disadvantages of decision trees, as compared to other machine learning models? Advantages of Decision Trees: Disadvantages of Decision Trees:

The process is long and much paperwork. You can watch on You tube interview quesitons by Absolute Abby. She is a recruiter for companies turned speaker and advocate for unemployed she will tell you how to pass and ace an interview. she came to the college.
avatar

Tutor

Interviewed at Laurus College

4.4★
Mar 15, 2016

The process is long and much paperwork. You can watch on You tube interview quesitons by Absolute Abby. She is a recruiter for companies turned speaker and advocate for unemployed she will tell you how to pass and ace an interview. she came to the college.

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