Start practicing these Data Analyst interview questions and answers today if you want to get through with toughest of interviews. These interview questions & answers on Data Analysts will help you prepare for your upcoming interviews. Get convert your interview into a job offer with these Data Analysts interview questions and answers by practicing. Data Analyst interview questions here will help you learn the beginner level questions like the process of data analysis, the difference between data mining and data analysis, etc. Prepare in advance and land your dream career as a Data Analyst.
The process of data analysis includes data collection, data inspection, data transformation and modelling data for valuable insights and support the organization with better decision making solutions. The steps which include in the process of data analysis are mentioned below:
|Data Mining||Data Analysis|
|Data mining usually does not require any hypothesis.||Data analysis starts with an assumption or a question|
|Data mining is dependent on well-documented data and cleaning of data.||Data analysis involves data cleaning.|
|Data mining outcomes are not always easy to interpret.||The outcome after data analysis is interpreted by the Data analysts conveyed to the stakeholders|
|Data mining algorithms automatically develop equations.||Based on the hypothesis, data analysts will have to develop their own equations.|
Data Validation is basically the process of validating data. This step plays one of the important roles in the process of data analysis. It mainly involves two processes namely, Data Screening and Data Verification.
The different types of hypothesis testing are as follows:
Usually, data is distributed in different ways with a bias to the left or to the right or it can be all spilled across. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. A normal distribution is an arrangement of a data set in which most values cluster in the middle of the range and the rest taper off symmetrically toward either extreme.
The random variables are distributed in the form of a symmetrical bell-shaped curve.
Properties of Normal Distribution:
In order to understand the alternate hypothesis, it is necessary to first know the null hypothesis. The null hypothesis is a statistical phenomenon which is used to test for possible rejection under the assumption that the result of chance would be true.
An alternative hypothesis, on the other hand, is also a statistical phenomenon which contradicts the null hypothesis. It is considered that the observations are a result of an effect with some chance of variation.
A data scientist must have the following skills
Statistical methods that are useful for a data scientist are:
Some of the missing patterns which are frequently observed by data analyst professionals are -
The three types of analysis methodologies have single, double or multiple variables.
Linear regression is a statistical model which attempts to fit the best possible straight line between the independent and the dependent variables when a set of input features are given. As the output is continuous, the cost function measures the distance from the observed to the predicted values. It can be said to be an appropriate choice to solve regression problems, for example, predicting sales number.
On the other hand, Logistic regression gives probability as its output. By definition, it is a bounded variable between zero and one, due to the sigmoid activation function. It is more appropriate to solve classification problems, for example, predicting whether a transaction is a fraud or not.
Eigenvectors are used to understand linear transformations and are calculated for a correlation or a covariance matrix. Eigenvectors are basically the directions along which a specific linear transformation acts either by compressing, flipping or stretching.
Eigenvalues refer to the strength of the transformation or the factor by which the compression occurs in the direction of eigenvectors.
Time series analysis is a statistical technique which deals with time series data or trend analysis. It helps to understand the underlying forces leading to a particular trend in the time series data points. Time series data is the data that is in a series of particular time periods or intervals. The types of data considered are -
Time series analysis can be performed in two domains - frequency domain and time domain
A/B Testing is also known as split testing or bucket testing. It is a statistical hypothesis test used for a randomized experiment with two variables A and B. Based on sample statistics, as an analytical method it estimates population parameters. A/B Testing shows the comparison of two web pages with the help of two variants A and B, to a similar number of visitors, and the variant wins which gives better conversion. A/B Testing is mainly used to identify the changes made to the web pages.
The process of Imputation involves replacing missing data with substituted values. There are mainly three problems caused by missing data -
Due to these problems, missing data can create problems for analyzing data. Imputation avoids such problems involved with listwise deletion of cases which have missing values. The different types of imputation techniques are -
Single Imputation (the missing value is replaced by a value)
Multiple Imputation (method for handling missing data in multivariate analysis)
In the case of unstructured data, the iterative process should be used to classify data. Taking some data samples and modifying the model accordingly in order to evaluate the same for accuracy. It is necessary to always use the basic process for data mapping. Also, data mining, data visualization techniques, algorithm designing and so on needs to be performed properly. It becomes easier to convert unstructured data into well-documented data files as per customer trends if all these processes are performed accordingly.
KPI or Key Performance Indicator can be defined as the metric which consists of a combination of charts, reports, spreadsheets or business processes.
Design of experiment is the initial process which is used to split data, data sampling or data setup for statistical analysis.
80/20 rule, also known as the Pareto principle is the law of the vital few or the principle of factor sparsity. It states that, for many events, roughly 80% of the effects come from 20% of the causes.
Clustering is defined as the task of dividing the population or data points into a number of groups such that data points in the same group are more similar to other data points in the same group than those in other groups. Basically, the main aim is to segregate groups with similar traits and assign them into clusters.
Clustering can be divided into two subgroups :
Correlation is a statistic that measures the strength and direction of the associations between two or more variables.
On the other hand, causation is a relationship that describes cause and effect.
“Correlation does not imply causation.” This statement warns us about the dangers of the common practice of looking at a strong correlation and assuming causality. A strong correlation may manifest without causation in the following cases:
Causation is tricky to be inferred. The most usual solution is to set up a randomized experiment, where the variable that’s a candidate to be the cause is isolated and tested. Unfortunately, in many fields running such an experiment is impractical or not viable, so using logic and domain knowledge becomes crucial to formulating reasonable conclusions.
There are plenty of alternatives to handle missing data, although none of them is perfect or fits all cases. Some of them are:
Data is visually represented with the help of a chart using locations in the image (height, width and depth). Dealing with beyond three dimensions, we need to add more information and make use of other visual cues.
Some of the most common cues are -
While performing data analysis, there are few problem data analysts face, some of them are mentioned below:
There are few criteria mentioned below which can guide you to decide whether the model developed is good or not:
In statistics, a variance is said to be the spread of a data set. It is a measurement which is used to identify how far each number in the data set is from the mean. During market research, variance plays an important role in calculating the probabilities of future events. It is used to find all the possible values and likelihoods that a random variable can take within a given advantage. If the value of variance is zero, it means that all the values within a data set are identical, and the variances which are not equal will come in the form of positive numbers. The larger the value of variance, the more spread in the data set. If the variance is large, it means the number in the data set are far from the mean and each other whereas a smaller variance implies that the number is closer together in value.
On the other hand, covariance provides insights into the relationship between two variables. It is the measurement of how two random variables in a data set will change together. There are two types of covariance - positive covariance and negative covariance. A positive covariance means that two variables are positively related and are moving in the same direction. Similarly, negative covariance is when the variables are inversely related or are moving in opposite directions.
A data analyst is the one who collects, processes and performs statistical analyses of data. Every business no matter how big or small business is it needs data. Companies that use data needs a data analyst to analyze it. Data analysts with highly skilled are some of the most sought-after professionals in the world.
Data analyst jobs are found across different companies and industries. Data analyst work on different industries like healthcare, insurance, retail, etc. Big tech companies like Google and Facebook look for data analyst.
Data analyst pay will vary depending on skills and experience. According to the AbsoluteIT, data analytics in the lowest paid group will earn an average salary of $69,000 per year and the highest paid group earn an average of $110,00 and as per Payscale the average pay for a Data Analyst is Rs 369,329 per year.
If you’re looking for Data analyst interview questions and answers for experienced and freshers, then you are at the right place. There are a lot of opportunities in many reputed companies across the globe. Good hands-on knowledge concepts will put you forward in the interview. You can find job opportunities everywhere. Our interview questions for data analyst are exclusively designed for supporting employees in clearing interviews. We have tried to cover almost all the main topics related to Data Scientists.
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