Data Analyst Interview Questions Accenture

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Data Analyst Interview Questions Accenture – Check out these top data analyst interview questions with answers to help you land your dream job. Data analysis is one of the most integral parts of the technological revolution. This has opened a clear path to the data analyst role as it is among the top 10 jobs of this decade. In it, you will learn what data analysis, data validation and data mining are.

A career in data analytics is not only fun but also very informative and profitable at the same time. Companies around the world have invested billions of dollars in the exploration and use of this field. So this accounts for many high paying jobs around the world. But with that, comes a lot of competition. To give you an edge over this competition, we’ve curated these Top Data Analyst Interview Questions to help you get the edge you need. Examining these questions will give you in-depth insight and understanding of frequently asked Data Analysis interview questions and answers, thus helping you ace them.

Data Analyst Interview Questions Accenture

How to Prepare for Data Analyst Interview – We aim to answer this question thoroughly through this post. Continue reading.

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Data analysis involves the process of cleaning, organizing and using data to produce meaningful information. Data mining is used to search for hidden patterns in data.

Data analysis produces results that are much more understandable to a variety of audiences than the results of data mining.

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Data validation, as the name suggests, is the process that involves determining the accuracy of the data and the quality of the source as well. There are many data validation processes, but the main ones are data verification and data validation.

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Data analytics is the structured process that involves working with data by performing activities such as ingestion, cleaning, transformation and evaluation to provide insights that can be used to increase revenue.

Data is initially collected from various sources. Since the data is a raw entity, it must be cleaned and processed to fill in missing values ​​and remove any out-of-scope entity.

After the data is pre-processed, it can be analyzed with the help of models, which use the data to do some analysis on it.

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The final step involves reporting and ensuring that the output data is transformed into a format that can also serve a non-technical audience, including analysts.

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This question is subjective, but some simple evaluation points can be used to evaluate the accuracy of a data model. Is the following:

Data Cleansing is also known as Data Inclusion. As the name suggests, it is a structured way of finding erroneous content in data and safely removing it to ensure that the data is of the highest quality. Here are some of the ways to clean data:

There can be many issues that a Data Analyst can face when working with data. Here are some of them:

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Data profiling is a methodology that involves analyzing all the entities present in the data in more detail. The goal here is to provide highly accurate insights based on the data and its characteristics, such as data type, frequency of the event, and more.

Data is never a static entity. If the business expands, this could cause sudden opportunities that require data changes. In addition, evaluating the model to check its health can help the analyst to analyze whether the model will be retrained or not.

However, the general rule is to ensure that models are retrained when there is a change in protocols and business proposals.

There is a wide range of tools that can be used in data analysis. Here are some of the popular ones:

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An outlier is a value in a data set that is considered to be far from the mean value of the data set characteristic. There are two types of outliers: univariate and multivariate.

12: How can we deal with problems that arise when data comes in from different sources?

There are many ways to deal with multi-source problems. However, these are mainly done to solve the problems:

Many tools are used to handle Big Data. Some of the most popular are:

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Pivot tables are one of Excel’s key features. They allow the user to easily view and summarize entire large data sets. Most of the functions with pivot tables include drag and drop functions that help create reports quickly.

KNN is the method that requires selecting a number of nearest neighbors and measuring distance at the same time. It can predict discrete and continuous characteristics of a data set.

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Here a distance function is used to find the similarity of two or more features, which will help in further analysis.

MapReduce and Hadoop are considered the best Apache frameworks when the situation requires working with a huge dataset in a distributed environment.

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Hierarchical clustering, or hierarchical cluster analysis, is an algorithm that groups similar objects into common groups called clusters. The aim is to create a set of clusters, where each cluster is different from the other and, individually, contains similar entities.

There are many steps involved in working from start to finish on a data analysis project. Some of the important steps are as given below:

Time series analysis, or TSA, is a statistical technique widely used when working with trend analysis and time series data in particular. Time series data consists of the presence of data at specific time periods or defined intervals.

Since time series analysis (TSA) has a wide range of uses, it can be used in many fields. Here are some of the places where the TSA plays an important role:

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Collaborative filtering is an algorithm used to create recommendation systems based primarily on customer or user behavior data.

For example, when browsing e-commerce websites, there is a section called “Recommended for you”. This is done using browsing history, analysis of previous purchases and combined filtering.

The K-thinking algorithm groups data into different sets based on how close the data points are to each other. The number of clusters is indicated by “k” in the k-means algorithm. It tries to maintain a good degree of separation between each of the clusters.

However, since it works in an unsupervised nature, the clusters will not have any kind of tags to work with.

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It is standard practice to use a t-test when the sample size is less than 30 and consider the z-test when the sample size is greater than 30 in most cases.

Naive Bayes is called naive because it makes the general assumption that all existing data are unambiguously significant and independent of each other. This is not true and will not apply in a real world scenario.

In the case of standardized coefficients, they are interpreted based on standard deviation values. The unstandardized coefficient is calculated based on the actual value present in the data set.

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Multiple methodologies can be used to detect outliers, but the two most commonly used methods are:

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K-Nearest Neighbor (KNN) is preferred here due to the fact that KNN can easily approximate the value to be determined based on the values ​​closest to it.

If there are any discrepancies in the data, a user can continue using any of the following methods:

Among many differences, the main difference between PCA and FA lies in the fact that factor analysis is used to identify and work with the variance among variables, while PCA tries to explain the covariance among components or variables present.

Next, in this list of best data analyst interview questions and answers, let’s look at some of the best questions from the advanced category.

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Next, in these data analyst interview questions, we need to look at the trends associated with this field.

With this question, the interviewer tries to assess your ability in the subject and your knowledge in the field. Be sure to include valid facts and their corresponding verification from sources to add a positive aspect to your candidacy. Also, try to explain how artificial intelligence has a huge impact on data analysis and its potential in the same.

Here, the interviewer tries to see how well you can convince him about your competence in the subject alongside the need for data analysis in the company you have applied for. It is always an added advantage to know the job description in detail, along with compensation and company details.

With this question, the interviewer is trying to understand your understanding of the subject, your confidence and your spontaneity. The most important thing to note here is that you answer honestly based on your ability.

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This is a question about the last program you completed at college. Talk about the degree you got, how it was useful,

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