Top 100 Data Analyst Interview Questions and Answers

Answer:

Some of the responsibilities of a data analyst include:

• Collects and analyzes data using statistical techniques and reports the results accordingly.
• Interpret and analyze trends or patterns in complex data sets.
• Establishing business needs together with business teams or management teams.
• Find opportunities for improvement in existing processes or areas.
• Data set commissioning and decommissioning.
• Follow guidelines when processing confidential data or information.
• Examine the changes and updates that have been made to the source production systems.
• Provide end-users with training on new reports and dashboards.
• Assist in the data storage structure, data mining, and data cleansing.

Answer:

While analyzing data, a Data Analyst can encounter the following issues:

• Duplicate entries and spelling errors. Data quality can be hampered and reduced by these errors.
• The representation of data obtained from multiple sources may differ. It may cause a delay in the analysis process if the collected data are combined after being cleaned and organized.
• Another major challenge in data analysis is incomplete data. This would invariably lead to errors or faulty results.
• You would have to spend a lot of time cleaning the data if you are extracting data from a poor source.
• Business stakeholders’ unrealistic timelines and expectations
• Data blending/ integration from multiple sources is a challenge, particularly if there are no consistent parameters and conventions
• Insufficient data architecture and tools to achieve the analytics goals on time.

Answer:

Data cleaning, also known as data cleansing or data scrubbing or wrangling, is basically a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable.

Answer:

Following are the major differences between data mining and data profiling:

• Data mining involves analyzing a pre-built database to identify patterns. Whereas, Data profiling involves analyses of raw data from existing datasets.
• In Data mining existing databases also analyzes and large datasets to convert raw data into useful information. In Data profiling, statistical or informative summaries of the data are collected.
• Data mining usually involves finding hidden patterns and seeking out new, useful, and non-trivial data to generate useful information. In contrast, Data profiling usually involves the evaluation of data sets to ensure consistency, uniqueness, and logic.
• Data mining is incapable of identifying inaccurate or incorrect data values. In data profiling, erroneous data is identified during the initial stage of analysis.
• Classification, regression, clustering, summarization, estimation, and description are some primary data mining tasks that are needed to be performed. While, Data profiling process involves using discoveries and analytical methods to gather statistics or summaries about the data.

Answer:

Methods of data validation commonly used by Data Analysts include:

• Field Level Validation: This method validates data as and when it is entered into the field. The errors can be corrected as you go.
• Form Level Validation: This type of validation is performed after the user submits the form. A data entry form is checked at once, every field is validated, and highlights the errors (if present) so that the user can fix them.
• Data Saving Validation: This technique validates data when a file or database record is saved. The process is commonly employed when several data entry forms must be validated.
• Search Criteria Validation: It effectively validates the user’s search criteria in order to provide the user with accurate and related results. Its main purpose is to ensure that the search results returned by a user’s query are highly relevant.

Answer:

In a dataset, Outliers are values that differ significantly from the mean of characteristic features of a dataset. With the help of an outlier, we can determine either variability in the measurement or an experimental error. There are two kinds of outliers i.e., Univariate and Multivariate.

Answer:

The term data visualization refers to a graphical representation of information and data. Data visualization tools enable users to easily see and understand trends, outliers, and patterns in data through the use of visual elements like charts, graphs, and maps. Data can be viewed and analyzed in a smarter way, and it can be converted into diagrams and charts with the use of this technology.

Answer:

Hash table collisions are typically caused when two keys have the same index. Collisions, thus, result in a problem because two elements cannot share the same slot in an array. The following methods can be used to avoid such hash collisions:

• Separate chaining technique: This method involves storing numerous items hashing to a common slot using the data structure.
• Open addressing technique: This technique locates unfilled slots and stores the item in the first unfilled slot it finds.

Answer:

Based on user behavioral data, collaborative filtering (CF) creates a recommendation system. By analyzing data from other users and their interactions with the system, it filters out information. This method assumes that people who agree in their evaluation of particular items will likely agree again in the future. Collaborative filtering has three major components: users- items- interests.

Answer:

In the field of Time Series Analysis (TSA), a sequence of data points is analyzed over an interval of time. Instead of just recording the data points intermittently or randomly, analysts record data points at regular intervals over a period of time in the TSA. It can be done in two different ways: in the frequency and time domains. As TSA has a broad scope of application, it can be used in a variety of fields. TSA plays a vital role in the following places:

• Statistics
• Signal processing
• Econometrics
• Weather forecasting
• Earthquake prediction
• Astronomy
• Applied science

Answer:

Clustering is the process of categorizing data into groups and clusters. In a dataset, it identifies similar data groups. It is the technique of grouping a set of objects so that the objects within the same cluster are similar to one another rather than to those located in other clusters. When implemented, the clustering algorithm possesses the following properties:

• Flat or hierarchical
• Hard or Soft
• Iterative
• Disjunctive

Answer:

One of the basic tools for data analysis is the Pivot Table. With this feature, you can quickly summarize large datasets in Microsoft Excel. Using it, we can turn columns into rows and rows into columns. Furthermore, it permits grouping by any field (column) and applying advanced calculations to them. It is an extremely easy-to-use program since you just drag and drop rows/columns headers to build a report. Pivot tables consist of four different sections:

• Value Area: This is where values are reported.
• Row Area: The row areas are the headings to the left of the values.
• Column Area: The headings above the values area make up the column area.
• Filter Area: Using this filter you may drill down in the data set.

Answer:

• Univariate Analysis: The word uni means only one and variate means variable, so a univariate analysis has only one dependable variable. Among the three analyses, this is the simplest as the variables involved are only one.
• Bivariate Analysis: The word Bi means two and variate mean variables, so a bivariate analysis has two variables. It examines the causes of the two variables and the relationship between them. It is possible that these variables are dependent on or independent of each other.
• Multivariate Analysis: In situations where more than two variables are to be analyzed simultaneously, multivariate analysis is necessary. It is similar to bivariate analysis, except that there are more variables involved.

Answer:

The waterfall chart shows both positive and negative values which lead to the final result value. For example, if you are analyzing a company’s net income, then you can have all the cost values in this chart. With such kind of a chart, you can visually, see how the value from revenue to the net income is obtained when all the costs are deducted.

Answer:

Eigenvectors: Eigenvectors are basically used to understand linear transformations. These are calculated for a correlation or a covariance matrix.

Eigenvalue: Eigenvalues can be referred to as the strength of the transformation or the factor by which the compression occurs in the direction of eigenvectors.

Answer:

Various steps in an analytics project include:

• Problem definition
• Data exploration
• Data preparation
• Modelling
• Validation of data
• Implementation and tracking

Answer:

Some of the key skills required for a data analyst include:

• Knowledge of reporting packages (Business Objects), coding languages (e.g., XML, JavaScript, ETL), and databases (SQL, SQLite, etc.) is a must.
• Ability to analyze, organize, collect, and disseminate big data accurately and efficiently.
• The ability to design databases, construct data models, perform data mining, and segment data.
• Good understanding of statistical packages for analyzing large datasets (SAS, SPSS, Microsoft Excel, etc.).
• Effective Problem-Solving, Teamwork, and Written and Verbal Communication Skills.
• Excellent at writing queries, reports, and presentations.
• Understanding of data visualization software including Tableau and Qlik.
• The ability to create and apply the most accurate algorithms to datasets for finding solutions.

Answer:

Hierarchical clustering algorithm combines and divides existing groups, creating a hierarchical structure that showcase the order in which groups are divided or merged.

Answer:

Map-reduce is a framework to process large data sets, splitting them into subsets, processing each subset on a different server and then blending results obtained on each.

Answer:

SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyper planes to separate out different classes based on the provided kernel function.