The term data analysis and data analytics are often used interchangeabely and could be confusing. Data analytics is a broader term and in cludes data analysis as necessary subcomponent. Analytics defines the science behind the analysis.

Data Analysis

-Data analysis is a process that refers to hands on data exploration and evaluation.
It looks backeards, providing marketers with a historical view of what has happened.
-It helps design a strong business plan for businesses, using its historical data that tells about what worked, what did not and what was expected from a product or a service.
-Used to finding or extracting useful information for decision making.
-Data analysis is a process involving the collection, manipulation and examination of data for getting insight from data.
-It is process of studying, refining, transforming, and trainingbof past data to gain useful information, sugges conclusions and make decisions.
-Is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happened in the future based on the previous data.
-Data analytics also makes decisions but less good than data analytics.
-Tools used for data analysis are Open Refine, Rapid Miner, KNIME, Google Fusion Tables,Node XL, Wolfram Alpha, Tableau Piblic, etc.

Data Analytics

-Analytics is used for the discovery, interpretation,and communication of meaningful patterns and insights in the data. The term analytics is used to refer to any data driven decision making.
-Data analytics may analyze many varieties of data to provide views into patterns and insights that are not humanly possible.
-It is the science of examining raw data with the purpose of drawing conclusions from that information.
-Data analytics is defined as a science of extracting meaningful valueable information from row data.
-It is used in many industries to allow companies and organizations to make better business decisions, and in the science to verify or disprove existing models or theories.
-Data analytics and all associated strategies and techniques are essential when it comes to identifying different patterns, findings anomalies and relationships in large chunks or set of data and making the data or information collected more meaningful and more understandable.
-Tools used in data analytics are Python Tableau Public, SAS, Apache Spark, Excel, etc.
-There are some roles in the data analytics

1)Data Analyst:-Data analyst is an individual, who performs mining of huge amount of data, models the data, looks for patterns, relationships, trends and so on. The main role of data analyst is to extract data and interpret the information attained from the data for analyzing the outcome of a given problem.

2)Data Scientist:-A data scientist is a professional who works with an enormous amount of data to come up with integrated business insights through the deployment of various tools, techniques, methodologies, algorithms,etc. The primary task of a data scientist is to use matchine learning and deep learning based techniques to make an in depth analysis of input data.

3)Data Architect:-They are provides the support of various tools and platforms that are required by data engineers to carry out various tests with precision. The main task of data architects is to design and implement database systems, data models, and components of data architecture.

4)Data Engineer:-A data engineer works with massive amount of of data and responsible for building and maintaining the data architecture of a data Science project. Data engineers have a demanding roles in data analytics as they help in assuring that data are made available in a form that can be easily used for analysis and interpretation.

5)Analytics Manager:-They are involved in the overall management of the various data analytics operatons as discussed in above section.

-The art and Science of refining data to fech useful insight which further helps in decision making is known as Analytics.
-There are four types of data analytics.

i)Descriptive Analytics

Descriptive analytics examines the raw data or content to answer question, what happened?, by analyzing valuable information found from the available past data.
The goal of descriptive analytics is to provide insights into the past leading to the present, using descriptive statistics, interactive explorations of the data, and data mining.
Example:
1.An organizations records give a past review of their financials, operations, customers and stakeholders, sales and so on.
2.Using Descriptive analysis, a data analyst will be able to generate the statistical results of the performance of the hockey players of team India. For generating such results, the data may need to be integrated from multiple data sources to gain meaningful insights through statistical analysis.

ii)Diagnostic Analytics

Diagnostic analytics is a form of analytics which examines data to answer the question, why did it happen?.
The goal of diagnostic analytics is to find the root cause of issues. It can be accomplished by techniques like data discovery, correlations, data mining and drill down.
The main function of diagnostic analytics is to identify anomalies, drill into the analytics and determine the casual relationships.
Example:
1)Some form of social media marketing campaign where the user is interested in retrieving the number of likes or reviews. Diagnostic analytics can help to filter out thousands of likes and reviews into a single view to see the progress of the campaign.
2)Drop in website traffic of an organization can lead to a decrease in the sales and thereby revenue will also be reduce. In this case, diagnostic analytics finds the root cause initially, such as traffic has been reduced and form there,it will fine tune the problem after finding the reasons for the downside in website traffic such as software engine optimization, social marketing, emain marketing and any other factors, which are not enabling the website to reach many people.

iii)Predictive Analytics

Predictive analysis, as the name suggests, deals with Prediction of future based on available current and past data.
A Predictive analysis uses past data to create a model that answer the question, what will happen?.
It uses prediction based on historical data, build models and use them to forecast a future value. For example, demand for a particular package around holiday season.
Example:
Using Predictive analysis, a data analyst will be able to predict the performance of each player of the hockey team for the upcoming olympics. Such prediction analysis can help the Indian hockey federation to decide on the player's selection for thr upcoming Olympics.

iv)prescriptive Analytics

Prescription analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option.
Prescription analytics not only anticipates what will happen and when it will happen, but also why it will happen.
It is often associated with the data science. To gain insights from the data, data scientists uses deep learning and machine learning algorithms to find patterns and make predictions about future events.
It prescribes what steps are needed to be taken to avoid a future problem. It involves a high degree of responsibility, time and complicacy to reach to informed decision making.
Example:
In the Healthcare industry, we can use Prescriptive analytics to manage the patient population by measuring the number of patients who are clinically obese.

So as the above we discussed the topic Data Analysis and Data Analytics hear.
Their explanation, comparison, examples, and real use in life, etc.