You must first ensure that your qualifications are sufficient to get the job. Then, you can do other things that will help balance the odds in your favor. It is important to know your stuff, but it is equally important to be prepared.
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Given below are some of the top data science interview questions and Answers for freshers and experienced candidates.
Top Data Science Interview Questions & Answers
Data science is a field that combines domain knowledge, programming skills, and mathematical and statistical knowledge to extract meaningful insights out of data.
- Data Science and Data Analytics are two different things
Data Science is focused on finding meaningful correlations among large datasets. Data Analytics is focused on uncovering the details of extracted insights. Data Analytics, also known as Data Science, is a subdivision of Data Science that seeks to answer more specific questions than Data Science.
- How do you check for data quality?
Some examples of definitions that are used to verify data quality include:
- Completeness
- Consistency
- Uniqueness
- Integrity
- Conformity
- Accuracy
- What is logistic regression in Data Science, and how does it work?
Logistic regression uses statistical analysis to predict a binary outcome based on previous observations. In addition, logistic regression models predict dependent variables by analyzing the relationship between existing independent variables.
- What is a confusion matrix?
A confusion matrix is a table used to describe how a classification model or "classifier" performs on test data. The true values of the test data are not known. Although the confusion matrix is easy to understand, it can be difficult to grasp the terminology.
- Explain the difference between unsupervised and supervised learning.
The input data and the output are provided to the model when supervised learning is used. Unsupervised learning only provides input data to the model. Supervised learning trains the model to predict output from new data.
- What is the purpose of Python for Data Cleaning in DS
Data scientists need to clean and convert large data sets to work with them. It is crucial to eliminate meaningless outliers and malformed records for better results.
Some of the most widely used Python packages for data analysis and cleaning are Matplotlib and Pandas. These libraries allow you to load and clean your data for effective analysis. For example, a CSV file called "Student" can contain information about students at an institute, including their names, standard, address and phone numbers, grades, and marks.
- Three types of biases can be caused by sampling
There are three types of biases in the sampling process:
- Selection bias
- Under coverage bias
- Survivorship bias
- How do you choose k for your k-means?
The elbow method is used to select k for K-means clustering. The elbow method allows you to run k-means clustering on the data, where 'k is the number of clusters.
It is the summation of all the squared distances between the cluster members and their centroid.
- What is a Z, Chi-Square, F, and T-test?
Hypothesis Testing for Small Sample Sizes: t-Test
Z test: Hypothesis testing with large samples
Chi-square Test: To determine the expected frequency of certain observations and the difference between what is observed, use the Test of Significance.
F test: Hypotheses about interest concern the differences in population means.
- What is the ROC curve, and how does it work?
The ROC curve represents the trade-off between sensitivity (1 - TPR) or specificity (1 - FPR). A classifier that gives curves closer towards the top-left corner indicates higher performance. On the other hand, the test is less accurate if the curve is closer to the 45-degree diagonal in the ROC space.
Natural Language Processing, also known as NLP, can be described as the automated manipulation of natural language by software. Natural language processing is a field that has existed for over 50 years. It grew out of the field of Linguistics and the advent of computers.
Deep learning, a combination of machine learning and AI, mimics how humans acquire certain types of knowledge. Deep learning is extremely useful for data scientists, who have to collect, analyze and interpret large quantities of data.
A p-value is a measure of how likely it is that an observed variation could have been caused by chance.
Typically, p-value is = 0.05
This is strong evidence against the null hypothesis. Therefore, you reject the null hypothesis.
Typically, p-value > 0.05
This is a weak argument against the null hypothesis. Therefore, you accept the null hypothesis.
p-value at the cutoff: 0.05
This is considered marginal and could lead to any other outcome.
- What is the difference between an error and a residual?
An error is a difference between the observed and true values (often unobserved, generated by the DGP). A residual is the difference in the observed and predicted values (generated by the model).
- Check out these Python libraries used for Data Analysis or Scientific Computations.
Python Libraries for Data Analysis
- Fundamental Scientific Computing: Numpy and Scipy
- Pandas - Data Manipulation & Analysis
- Matplotlib - Plotting and Visualization
- Scikit-learn Machine Learning and Data Mining
- StatsModels - Statistical Modeling and Testing.
- Seaborn - Data Visualization
- What is Ensemble Learning?
Ensemble learning refers to the combination of multiple models (e.g., experts or classifiers) that are strategically generated to solve a specific computational intelligence problem. Ensemble learning is used primarily to improve (classification prediction, function approximation, etc.). Performance of a model or decrease the chance of poor selection.
- What is the purpose of A/B testing?
A/B testing allows people, teams, and companies to test user experience changes while collecting data. This testing method aims to identify changes that can be made to a website to improve or maximize the results of a strategy.
- What is association analysis? What is association analysis?
It is the process of discovering interesting relationships within large datasets. This is used to determine how data items are related.
- What is DBSCAN clustering?
DBSCAN (Density-Based Spatial Clustering Of Applications With Noise) is a well-known unsupervised learning method used in machine learning algorithms and model building.
DBSCAN is a clustering technique used in machine learning to distinguish clusters with high density from those with low density. DBSCAN clustering has two important parameters.
Epsilon - Minimum distance or radius between two points
Min - Sample points - The minimum number of samples required to identify a single cluster.
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