HERE Technologies Data Scientist Interview Questions and Answers

Q1. Exptected salary
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Q2. What do you expect for work
Ans. 
I expect challenging projects, opportunities for growth, collaborative team environment, and work-life balance.
• Challenging projects that allow me to apply my data science skills and learn new techniques

• Opportunities for growth and advancement within the company

• Collaborative team environment where I can share ideas and work together towards common goals

• Work-life balance to ensure I can perform at my best both profession
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Topic was joining and calculating

I was asked to solve various problems (your typical algorithm and data structure subjects), as well as explain the various projects I worked on in my most recent position.

Divide candidates in a group of around 15 people, and put you through different activities such as role play exercises to measure your communication and team working skills.

Q1. What is hypothesis testing
Ans. 
Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• It involves formulating a hypothesis about a population parameter, collecting data, and using statistical tests to determine if the data supports or rejects the hypothesis.

• There are two types of hypotheses: null hypothesis (H0) and alternative hypothesis (H1).

• Common statistical tests for hypothesis testing include...read more
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Q2. What is null hypothesis
Ans. 
Null hypothesis is a statement that there is no significant difference or relationship between variables being studied.
• Null hypothesis is typically denoted as H0 in statistical hypothesis testing.

• It is the default assumption that there is no effect or relationship.

• The alternative hypothesis (Ha) is the opposite of the null hypothesis.

• For example, in a study testing a new drug, the null hypothesis would be that the drug...read more
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Q3. What is supervised and unsupervised learning
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.
• Supervised learning requires labeled data for training

• Unsupervised learning does not require labeled data

• Examples of supervised learning include classification and regression

• Examples of unsupervised learning include clustering and dimensionality reduction
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Q1. 2 round technical interview, both of them about ML and last one concerning statistics and ML.
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Q2. What is linear regression?
Ans. 
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.
• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commo...read more
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Q3. Question regarding confusion matrix?
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Q4. Name various ML algorithm?
Ans. 
ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.
• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinf
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Q1. Describe Projects in depth ?
Ans. What algos used ? Why ? Leads you got ? Whole data prep and deployment process ? Model Performance Tracking.
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Q2. Scree share coding - manipulate dataframe using pandas
Ans. 
Using pandas to manipulate dataframes through screen sharing coding.
• Use pandas library in Python for data manipulation

• Share screen to demonstrate coding techniques

• Use functions like merge, groupby, and apply for data manipulation
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Q3. How to extract top discussed topics on twitter ?
Ans. 
Use Twitter API to extract tweets, perform text analysis to identify top discussed topics.
• Access Twitter API to retrieve tweets

• Perform text analysis using NLP techniques like TF-IDF or LDA

• Identify keywords or hashtags with highest frequency to determine top discussed topics
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Q4. Everything about TN, TP, imbalanced dataset, senstivity, auc_roc? Difference between bagging and boosting.
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