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Data analytics is a branch that allows you to extract the required data from unstructured data. It has different tools, techniques and processes for performing data analysis and managing data. As part of data analytics, you can collect, organize and store data. Data analytics will use statistical analysis and technologies to find out trends in the data and solve problems. Many companies are using data analytics to analyse and shape their business processes. With the analysed data, you can take decisions and attain the best business results. Data analytics is used in different areas such as programming, mathematics, and statistics.

To perform robust analysis, data analytics will use various data management techniques to mine data, cleanse data, transform data, model data and so on. From the analysis done through the raw data, you can come to the conclusion and take the right decision that is good for your business. The analytics will help businesses to optimize performance, improve profits and take decisions that are strategically guided. Many techniques and processes are automated into algorithms to work on unstructured data.

**Types of Data Analytics**

The analytics are divided into four types. Each one is used to attain the desired outcome:

**Descriptive Analytics**

It uses historical and current information gathered from various sources to find out the present state or a specific historical state by checking the patterns and trends thoroughly.

**Diagnostic Analytics**

It makes use of the data that help you to find out the reasons for the decline or increase in performance.

**Predictive Analytics**

It applies statistical modelling, forecasting and machine learning to anticipate future outcomes. Predictive analytics also known as advanced analytics depends mostly on deep and machine learning.

**Prescriptive Analytics**

It is a type of advanced analytics where you can test applications and use other techniques to find out solutions that will give you desired outcomes. Predictive analytics will make use of machine learning, algorithms and business rules.

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Data analysts will use different techniques and methods for data analysis and extract informative data.

**Regression Analysis**

Regression analysis is the statistical process that is followed to find out the relationship between different variables. This also helps you find out how making a change to one or more variables will have an impact on the other one. For instance, using this analysis you can learn how social media has an impact on sales.

**Monte Carlo Simulation**

Monte Carlo simulations will help you to find out the probabilities of different outcomes in a specific process that is tough to predict due to inference from random variables. It is good to use this technique for carrying out risk analysis.

**Factor Analysis**

Factor analysis is a statistical method that will take a huge data set and divide that data set into small blocks, which is easier to manage. The benefit offered by this type of analysis is to uncover the hidden pattern. You can use factor analysis to explore various things, especially customer loyalty.

**Cohort Analysis**

Cohort analysis will break the dataset into small groups. The grouping is done based on similar traits for performing cohort analysis. It is best used to understand different customers.

**Cluster Analysis**

It has a group of techniques that will classify objects or cases into groups, which are called clusters. It is best to be used to learn the structure of the data. The insurance companies will use this type of analysis to thoroughly investigate a few locations that are linked to the insurance claims.

**Time Series Analysis**

This type of analysis will deal with the time-series information and trend analysis. The data will be related to the time period. The analysis done will help you find out the trends and cycles over a period of time. You can calculate the weekly sales number with ease through this analysis. It is best to be used for forecasting sales figures.

**Sentimental Analysis**

The sentimental analysis will make use of different tools like natural language processing, computational linguistics and text analysis to learn about the feelings that are expressed in the collected information. It helps you to learn how customers feel about your product, brand and service.

The following tools are used by small to large companies to analyse data:

**Apache Spark**

It is an open-source data science platform that is used to process big data and create cluster computing engines.

**Excel**

Microsoft Excel is a widely used data analytics tool to perform mathematical analysis and tabular reporting.

**Looker**

It will make use of the BI platform and data analytics.

**Power BI**

Microsoft data visualization, as well as data analysis tool, will be used to create and distribute the reports and make beautiful dashboards to represent the data.

**Python**

It is an open-source programming language that helps users to get, clean and visualize the information.

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