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The storage of data is a big deal. Companies that USE big data have been in the news a lot lately, as they try to maximize its potential. Data storage is usually handled by traditional databases for the layperson. For storing, managing, and analyzing big data, companies use data warehouses and data lakes.

Data Warehouse: This is considered an ideal place to store all the data you gather from many sources. A data warehouse is a centralized repository of data where data from operational systems and other sources are stored. It is a STANDARD tool for integrating data across the TEAM- or department-silos in mid-and large-sized companies. It collects and manages data from varied sources to provide meaningful BUSINESS insights. Data warehouses can be of the FOLLOWING types:

• Enterprise data warehouse (EDW): Provides decision support for the entire organization.
• Operational Data Store (ODS): Has functionality such as reporting sales data or employee data.

Data Lake: Data lakes are basically large storage device that stores raw data in their original format until they are needed. with its large amount of data, analytical performance and native integration are improved. It exploits data warehouses' biggest weakness: their incapacity to be flexible. In this, neither planning nor knowledge of data analysis is required; the analysis is assumed to happen later, on-demand.

The purpose of Data Analysis is to transform data to discover valuable information that can be used for making decisions. The use of data analytics is crucial in many industries for various purposes, hence, the demand for Data Analysts is therefore high around the world. Therefore, we have listed the top data analyst interview questions & answers you should know to succeed in your interview. From data cleaning to data validation to SAS, these questions cover all the essential information related to the data analyst role.

Important Resources:

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Performing DATA ANALYSIS REQUIRES the USE of many different statistical techniques. Some important ones are as follows: 

• Markov process 
• Cluster analysis 
• Imputation techniques 
• Bayesian methodologies 
• Rank statistics 

N-gram, known as the PROBABILISTIC language MODEL, is defined as a connected SEQUENCE of n items in a GIVEN text or speech.  It is BASICALLY composed of adjacent words or letters of length n that were present in the source text. In simple words, it is a way to predict the next item in a sequence, as in (n-1).

Also known as source control, version control is the mechanism for configuring software. Records, files, DATASETS, or documents can be managed with this. Version control has the following advantages: 

• Analysis of the deletions, editing, and creation of datasets since the original copy can be done with version control.  
• Software development becomes clearer with this method.  
• It helps distinguish different versions of the DOCUMENT from ONE another. Thus, the latest version can be easily identified.  
• There's a complete history of project files maintained by it which comes in handy if ever there's a failure of the central server.  
• Securely storing and maintaining MULTIPLE versions and variants of code files is easy with this tool.  
• Using it, you can view the changes MADE to different files. 

Variance: In STATISTICS, variance is defined as the deviation of a data set from its mean value or average value. When the variances are greater, the numbers in the data set are farther from the mean. When the variances are SMALLER, the numbers are nearer the mean. Variance is calculated as follows: 

Here, X represents an individual data point, U represents the average of multiple data points, and N represents the total number of data points. 

COVARIANCE: Covariance is another common concept in statistics, like variance. In statistics, covariance is a measure of how two random VARIABLES change when compared with each other. Covariance is calculated as follows:  

Here, X represents the independent variable, Y represents the dependent variable, x-bar represents the mean of the X, y-bar represents the mean of the Y, and N represents the total number of data points in the sample. 

One of the most famous partitioning methods is K-mean. With this unsupervised LEARNING algorithm, the unlabeled data is grouped in clusters. Here, 'k' indicates the NUMBER of clusters. It tries to keep each cluster separated from the other. Since it is an unsupervised model, there will be no labels for the clusters to work with.

Logistic Regression is BASICALLY a MATHEMATICAL model that can be used to study datasets with one or more independent variables that determine a PARTICULAR outcome. By STUDYING the relationship between MULTIPLE independent variables, the model predicts a dependent data variable.

This algorithm group objects into clusters based on similarities, and it is ALSO called HIERARCHICAL CLUSTER analysis. When hierarchical clustering is performed, we obtain a set of clusters that differ from each other. 

This clustering TECHNIQUE can be divided into two types:

• Agglomerative Clustering (which uses bottom-up STRATEGY to decompose clusters)
• Divisive Clustering (which uses a top-down strategy to decompose clusters)

In order to handle Big DATA, multiple TOOLS are used. There are a few POPULAR ones as FOLLOWS: 

• Hadoop 
• Spark 
• Scala 
• Hive 
• Flume 
• Mahout, ETC.

• 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.
  Example:  A simple example of univariate data could be height as shown below: 

• 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.  
  Example: A simple example of bivariate data could be temperature and ice cream sales in the summer season. 

• 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.

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. 

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 

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

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. 

EXAMPLE:  
Collaborative filtering can be SEEN, for instance, on online shopping sites when you see phrases such as "recommended for you”.

The following are some disadvantages of data analysis: 

• Data Analytics may put customer privacy at risk and result in COMPROMISING transactions, purchases, and subscriptions. 
• Tools can be complex and require previous training. 
• Choosing the right analytics tool every time requires a lot of skills and expertise. 
• It is POSSIBLE to misuse the information obtained with data analytics by targeting PEOPLE with certain political BELIEFS or ethnicities. 

An effective data model must possess the FOLLOWING characteristics in order to be CONSIDERED good and DEVELOPED:

• Provides predictability performance, so the OUTCOMES can be estimated as precisely as possible or almost as ACCURATELY as possible.   
• As business demands change, it should be adaptable and responsive to accommodate those changes as needed.   
• The model should scale proportionally to the change in data.   
• Clients/customers should be able to reap tangible and profitable benefits from it.