Top 10 Data Analytics Interview Questions #1- What is the difference between Data Mining and Data Analysis? Data Mining Data Analysis A hypothesis is not required for Data Mining Data analysis begins with a hypothesis. Data Mining demands clean and well-documented data. Data analysis involves data cleaning. Results of data mining are not always easy to interpret. Data analysts interpret the results and present it to the stakeholders. Data mining algorithms automatically develop equations. Data analysts have to develop their own equations. #2- Mention what are the various steps in an analytics project? Data analytics deals with collecting, cleansing, transforming and modelling data to gain valuable insights and support better decision making …show more content…
#7- Explain K-mean Algorithm and Hierarchical Clustering Algorithm? K-Mean Algorithm - K mean is a famous partitioning method. In the K-mean algorithm, the clusters are spherical i.e. the data points in a cluster are centered on that cluster. Also, the variance of the clusters is similar i.e. each data point belongs to the closest cluster Hierarchical Clustering Algorithm - Hierarchical clustering algorithm combines and divides existing groups and creating a hierarchical structure for them to show the order in which groups are divided. #8- What is data cleansing? Mention few best practices that you need to follow while doing data cleansing? From a given dataset, it is extremely important to sort the information required for data analysis. Data cleaning is a crucial step wherein data is inspected to find any anomalies, remove repetitive and incorrect information, etc. Data cleansing does not involve removing any existing information from the database, it just enhances the data quality so that it can be used for analysis. Some of the best practices for data cleansing include …show more content…
• Simplex algorithm • Mathematical optimization #10- Explain what is imputation? List out different types of imputation techniques? Which imputation method is more favourable? During imputation, we have a tendency to replace missing information with substituted values. The kinds of imputation techniques involve are – • Single Imputation: Single imputation denotes that the missing value is replaced by a value. In this method, the sample size is retrieved. • Hot-deck imputation: A missing value is imputed from a randomly selected similar record by using punch card • Cold deck imputation: It works same as hot-deck imputation, but a little more advanced and chooses donors from other datasets • Mean imputation: It involves replacing missing value with the predicted values of other variables. • Regression imputation: It involves replacing missing value with the predicted values of a certain value depending on other variables. • Stochastic regression: It is same as regression imputation, however, it adds the common regression variance to the regression imputation • Multiple Imputation: Unlike single imputation, multiple imputations estimates the values multiple
The next project deliverable is a robust, modernized database and data warehouse design. The company collects large amounts of website data and uses this data to analyze it for the company’s customers. This document will provide an overview of the new data warehouse along with the type of database design that has been selected for the data warehouse. Included in the appendix of this document is a graphical depiction of the logical design of the
The K-Means algorithm is used for cluster analysis by dividing data points into k clusters. The K means algorithm will group the data into the cluster based on feature similarity.
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
AHIMA's data quality management model depicts data collection as one of the four primary data functions. The others are application, warehousing, and analysis. All characteristics of data quality management should be applied to data collection ...
The four key processes in the data quality management model are analysis, warehousing, collection and application of data (AHIMA 2)
and database systems. The basic goal of data mining is to get the data from large data sets in order to make it understandable structure for further use.[1]
Due to the invisibility of the population, a sampling frame can not be developed. Without the ...
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
Normalization, Integrity and Security are the important role for a DBA, Normalization helps to avoid data redundancy by reviewing data base structure at certain level. It helps to build an effective data model. Data Integrity provide some level of assurance over the information getting store and retrieved from database, DBA has to understand all DBMS features use them correctly for Data Integrity. Data Security is toughest part for DBA, auditing and multiple level security can protect data but none of them provide complete security, security can also be managed by encrypting and masking the organization data.
Often uses random sampling to select a large statistically representative sample from which generalizations can be drawn.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Companies have transformed technology from a supporting tool into a strategic weapon.”(Davenport, 2006) In business research, technology has become an essential means that many organizations use in their daily operations. According to the article, Analytics is a major technological tool used. It is described as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions."(Davenport, 2006) Data is compiled to enhance business practices. When samples are taken, they are used to examine research and understand how to solve problems or why situations are as they are. Furthermore, in this article, Thomas Davenport discusses analytics from a business standpoint. He refers to organizations that have been successful in their usage of data and statistical analysis. In addition, he also discusses how data and statistics can be vital in the efforts to improve the operations of businesses.
Databases are becoming as common in the workplace as the stapler. Businesses use databases to keep track of payroll, vacations, inventory, and a multitude of other taske of which are to vast to mention here. Basically businesses use databases anytime a large amount of data must be stored in such a manor that it can easily be searched, categorized and recalled in different means that can be easily read and understood by the end user. Databases are used extensively where I work. In fact, since Hyperion Solutions is a database and financial intelligence software developing company we produce one. To keep the material within scope I shall narrow the use of databases down to what we use just in the Orlando office of Hyperion Solutions alone.
Missense: In this mutation changed codon specifies a different amino acid. Resulting protein may be more or less active. E.g Sickle cell anemia (Disease caused by signle point mutation i.e. T replaced A in gene of globin)