#data analytic services

Data analytics services have revolutionized the way businesses operate by providing insights into customers, trends, and patterns. The use of data analytics services has enabled companies to make informed decisions and gain a competitive advantage. In this article, we'll explore the benefits of data analytics services and how they can help businesses unlock valuable insights and opportunities.

Data analytics services involve collecting, processing, and analyzing data to reveal patterns, trends, and insights that can be used to make data-driven decisions. These services are used in a variety of industries, including healthcare, finance, retail, and more.

One of the biggest benefits of data analytics services is the ability to gain insights into customer behavior. By analyzing customer data, businesses can identify patterns and trends that can inform marketing and sales strategies. For example, a retailer may use data analytics services to analyze customer purchase history and identify products that are frequently bought together. This information can be used to create targeted promotions and discounts that encourage customers to buy more products.

Data analytics services can also help businesses identify operational inefficiencies and opportunities for cost savings. By analyzing data from supply chain and inventory systems, businesses can identify areas where they can reduce waste, optimize inventory levels, and improve logistics. For example, a manufacturer may use data analytics services to analyze production data and identify bottlenecks that are causing delays. This information can be used to optimize production processes and improve overall efficiency.

Another important benefit of data analytics services is the ability to identify emerging trends and market opportunities. By analyzing industry data and monitoring social media activity, businesses can identify trends and patterns that can inform product development and marketing strategies. For example, a fashion retailer may use data analytics services to analyze social media activity and identify trends in fashion that are gaining popularity. This information can be used to inform product development and marketing campaigns.

Data analytics services can also help businesses improve risk management and compliance. By analyzing data from financial systems and regulatory filings, businesses can identify potential risks and ensure compliance with regulatory requirements. For example, a financial institution may use data analytics services to analyze transaction data and identify patterns that indicate fraudulent activity. This information can be used to prevent financial losses and ensure compliance with anti-money laundering regulations.

Data Analytic Services is a word that is used to describe the process of analyzing data in order to generate insights.

Data analytics are the methods and processes used by companies to understand the behavior of their customers, competitors and other partners. The main goal of data analytics is to generate insights and make decisions based on them. In this context, data analytics refers to any kind of analysis that involves gathering, analyzing and interpreting large amounts of information.

Data analytic services are a type of service that is used to make sense of data. It is the process of extracting valuable information from the data and interpreting it in a way that is meaningful.

Data analytic services are used in many industries such as healthcare, financial, marketing, and retail. The process can be used for any type of data set regardless of size or complexity.

Analytics is the process of collecting, analyzing, and interpreting data in order to make decisions about the future.

The phrase "data analytics" refers to the procedures of gathering and analysing various amounts of data. To extract data and evaluate patterns and trends that contribute to practical insight, inform additional study, or decide on effective business strategy, a variety of methodologies can be used. Many of these methods rely on complex systems or software that can combine automation, machine learning algorithms, and other modalities.

Historical or trending data can be collected and analysed by businesses. The sort of information gathered is determined by the purpose for which it will be used.

Customer data, site visitors' data, and data purchased from third parties are all possible sources of data. In terms of content, this information is diverse. Customer demographics, hobbies, actions, and a variety of other elements that a firm deems significant are all common data sources.

It is critical to recognise that data analytics are becoming increasingly important in a variety of businesses. The common link of data analytics with marketing and online commerce is a blunder.

Data analytic services are used to extract and analyze data from different sources. They can be used in a variety of ways such as using data analytics to improve the effectiveness of marketing campaigns or using predictive analytics to predict customer behavior.

Data analytic services are often used by many business sectors. The most common use cases for these services include:

- Marketing: Data analytics help marketers understand what’s working and what’s not working for their business, which allows them to make more effective decisions.

- Customer service: Data analytics can help companies understand their customers’ needs and preferences better than before, which allows them to provide better customer service.

Data analytic services are becoming more and more popular among companies and organizations. These services are used to collect, store, analyze, and visualize data for various purposes.

data analytics services is a process of analyzing data in order to gain insights into its structure, context, meaning and significance. Data analytics is the process of using collected data to make decisions or predictions about future outcomes.

Factor Analysis

The analysis of variance is not a mathematical theorem, but rather a convenient method of arranging the arithmetic.- Ronald Fisher

The inexpensive Factor Analysis is a prominent statistical tool to identify a lot of underlying dormant factors. For more than a century it is used in psychology and also in a wide variety of situations.

Factor analysis explains correlations among multiple outcomes as a result of one or more factors.  As it attempts to represent a set of variables by a smaller number, it involves data reduction.  It explores unexplained factors that represent underlying concepts that cannot be adequately measured by a single variable.  It is most popular nowadays in survey research where the responses to each question represent an outcome.  It is because multiple questions are often related and the underlying factor may influence the subject responses.

The reduction technique of factor analysis in reducing a large number of variables into a fewer number of factors enables to extract maximum common variance. The common score from all variables as an index can be used for further analysis. Being part of the GLM, it assumes several assumptions including:

·         There exists a linear relationship.

·         There is no multicollinearity.

·         Includes relevant variables into the analysis

·         No true correlation between variables and factors.

From its start of psychological usage in 1904, it is now widely used in a variety of industries and fields. Its use in physical sciences to identify factors that affect the availability and location of underground sources, water quality, and weather patterns. It is also extensively and successfully used in the marketing field and market research related to product attributes and perceptions. Along with other Quantitative Research and Quantitative Analysis tools, it is used in the construction of perceptual maps and product positioning studies.

Many social scientists are seeking the help of factor analysis to uncover major social and international patterns when confronted with

·         Entangled behavior

·         Unknown interdependencies

·         Masses of qualitative and quantitative variables

·         Big data

Factor analysis capabilities:

Factor analysis helps to find solutions for a social scientist with capabilities like:

·         To simultaneously manage over hundred variables.

·         Compensate for random error & invalidity.

·         Disentangles complex inter-relationships into their major & distinct regularities.

Factor analysis through inexpensive has its costs, including:

·         Its mathematical complications.

·         Entails diverse & numerous considerations in the application.

·         Strange terms in technical vocabulary.

·         Huge Analysis Reports covering most of the pages leaving less space for methodological introduction or explanation of terms.

·         Students unable to learn it in their formal learning.

All these costs make factor analysis results incomprehensible for non-specialists, social scientists, and policymakers to identify its nature and significance.  

Types of factor analysis:

There are many methods which extract factor from data set differently, including:

1.      EFA (Exploratory factor analysis)

EFA is the most common factor analysis method used in multivariate statistics to uncover the underlying structure of a relatively large set of variables.  EFA assumes that any indicator or variable may be associated with any factor to identify the underlying relationship between measured variables. It is not based on any prior theory and uses Multiple Regression and partial correlation theory to model sets of manifest or observed variables.

2.     CFA (Common factor analysis)

CFA is the second most preferred method to extract the common variance and put them into factors. It determines the factor and factor loading of measured variables. It also confirms what is expected on the basic or pre-established theory by assuming that each factor is associated with a specified subset of measured variables.

CFA commonly uses two approaches

·         The traditional method:

This method is based on principle factor analysis than CFA and allows the researcher to know more about insight factor loading.

·         SEM (structural equating model) method:

In Structural Equating Model (SEM) approach, it is assumed that when the standardized error term is below the absolute value of two then it is good for the factor. And if it is more than two, it implies that there is still unexplained variance left to be explained by factor.  

3.     PCA (principal component analysis)

The PCA starts by extracting maximum variance and puts them into the first factor. Then it starts with the second factor after removing the first variance and the analysis goes on until the last factor. Hence this method is most commonly used by researchers.

PCA VS CFA:

·         PCA analyzes all the variance of data while CFA does it only for the reliable, common variance of data

·         PCA is unfit for examining the structure of data as it tends to increase factor loadings in the study with a small number of variables.  But in CFA there is a hypothetical underlying process or construct involved which is not in PCA.

·         Since CFA provides an accurate result, it is the most preferred research. PCA is only a choice and could be used as an initial step in CFA to provide information regarding the maximum number and nature of factors.

 4.     GLM:

GLM also known as Multivariate Regression model is a useful framework to compare how several variables affect different continuous variables.  Being a linear statistical model, it is the foundation for several statistical tests like ANOVA, ANCOVA, and regression analysis.  GLM is described as

Data = Model + Error

5.     Image factoring:

It uses the OLS (Ordinary Least Square) Regression method to predict the factor, and this method is based on the correlation matrix.

6.     Maximum likelihood method:

It also uses the correlation matrix and has the advantage to analyze statistical models with different characters on the same basis.

7.     Factor loading:

Being the correlation coefficient for the variable, factor loading explains variance by the variable on that particular factor.

8.    Factor score:

All analysis of factor score will assume that the variables will behave as factor scores and will move.  Also known as component score, it is all of rows and columns used as an index of variables for further analysis.

9.     Eigen Values:

Also known as characteristic roots, Eigenvalues portray variance explained by that particular factor out of the total variance. Eigenvalues shows how much variance is explained by the first factor out of the total variance.

 10.Rotation method:

Rotation method is not affected by Eigenvalues or the percentage of variance extracted but it affects to make it more reliable to understand the output. Rotation methods have a lot of available methods, including:

·         No rotation method.

·         Varimax rotation method.

·         Quatrimax rotation method.

·         Direct oblimum rotation method.

·         Promax rotation method.

Assumptions in factor analysis:

·         No outlier:

Assuming that there are no outliers in data.

·         Interval data:

Interval data in Factor Analysis are assumed.

·         Adequate sample size:

The case used must be greater than the factor.

·         No perfect multicollinearity:

There should not be any multicollnearity between variables in factor analysis as it is an independency technique.

·         Homoscedasticity:

Being a linear function of measured variables, factor analysis does not require homoscedasticity between the variables.

 ·         Linearity:

As it is based on linearity assumption, factor analysis can use non-linear variables but on transfer they change to linear variables.

Data Mining Concepts And Techniques Via Programming Languages - http://bit.ly/2IfVmdl

In the developing domain of big data, the role of a data miner is pivotal; your business may see the value in harnessing data. The key lies in knowing how to mine data for business success. Australian statisticians from Statswork offer some suggestions to learn the programming languages for data mining.