Data Science and Data Analytics are two most trending terminologies of today’s time. Presently, data is more than oil to the industries. Data is collected into raw form and processed according to the requirement of a company and then this data is utilized for the decision making purpose. This process helps the businesses to grow & expand their operations in the market.
What is Data Analytics?
Data or information is in raw format. The increase in size of the data has lead to a rise in need for carrying out inspection, data cleaning, transformation as well as data modeling to gain insights from the data in order to derive conclusions for better decision making process. This process is known as data analysis.
Data Mining is a popular type of data analysis technique to carry out data modeling as well as knowledge discovery that is geared towards predictive purposes. Business Intelligence operations provide various data analysis capabilities that rely on data aggregation as well as focus on the domain expertise of businesses. In Statistical applications, business analytics can be divided into Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA).
EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive Analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All these are varieties of data analysis.
Difference between Data Analysis and Data Reporting
The analysis is an interactive process of a person tackling a problem, finding the data required to get an answer, analyzing that data, and interpreting the results in order to provide a recommendation for action.
- A report will show the user what had happened in the past to avoid inferences and help to get a feel for the data while analysis provides answers to any question or issue. An analysis process takes any steps needed to get the answers to those questions.
- Reporting just provides the data that is asked for while analysis provides the information or the answer that is needed actually.
- We perform the reporting in a standardized way, but we can customize the analysis. There are fixed standard formats for reporting while we perform the analysis as per the requirement; we customize it as needed.
- We can perform reporting using a tool and it generally does not involve any person in the analysis. Whereas, a person is there for doing analysis and leading the complete analysis process.
- Reporting is inflexible while analysis is flexible. Reporting provides no or limited context about what’s happening in the data and hence is inflexible while analysis emphasizes data points that are significant, unique, or special, and it explains why they are important to the business.
Data Analysis Process
Whenever any requirement occurs, firstly we need to determine the business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.
For the further process, we need to gather initial data, describe and explore data and lastly verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and the need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.
From the data collected in the last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally, we need to format the data to get the appropriate data. Data is selected, cleaned, and integrated into the format finalized for the analysis in this phase.
After gathering the data, we perform data modeling on it. For this, we need to select a modeling technique, generate test design, build a model and assess the model built. The data model is built to analyze relationships between various selected objects in the data. Test cases are built for assessing the model and model is tested and implemented on the data in this phase.
Here, we evaluate the results from the last step, review the scope of error, and determine the next steps to perform. We evaluate the results of the test cases and review the scope of errors in this phase.
We need to plan the deployment, monitoring and maintenance and produce a final report and review the project. In this phase, we deploy the results of the analysis. This is also known as reviewing the project.
Why do we need to learn Data Analytics Basics?
- Data analytics helps to acquire problem-solving skills in any type of business as it is used by many professionals and students. This helps to approach the problems analytically and solve them in the most logical way. This helps the user in daily life as well.
- The skill is in high demand right now as there is skills’ shortage globally. Learning basics is the starting point of learning data science and hence machine learning.
- Data is available everywhere and it has become the need of the hour to know how to analyze the data in our hand and to understand how our data is being analyzed for various businesses.
Applications of Data Analytics Basics
- It helps in health care to know various cases of different diseases and to analyze the data to help the patients understand the risk involved and to recognize the disease easily. This helps in giving proper treatment to the patients.
- Database analytics helps to do the internet search as various data helps to collect information in searching the data.
- The analytics helps in determining the images and doing speech recognition. Clustering the images and determining their groups is one of the applications of Data analytics.
- Data analytics is easy to learn and can be mastered by anyone with the basics of computer programming and analytics. This helps the users to grasp more about data.
- Data analytics helps in recommending the websites to the clients and this is helpful to the users who do data analytics in a perfect manner.