Everyone is talking about Data Analytics these days. It happens to be one of the trending careers all around the world. There is a constant need for people to keep upgrading their skills and knowing what is data analytics.
As we see the ever-growing rise in the domain of Data Analytics. If they don’t put with that pace they are going to face the disadvantage of it. Students all around are looking for the best courses and institutes to do the course in it.
It has been projected by IDC that big data and business data analytics will generate a revenue of $274.3 billion by 2022.
But before you choose to follow the crowd, begin by understanding what is data analytics? As the revenue grows the higher becomes the need for better talent.
Any decision in multinational companies and other businesses depends on the data that they produce. There is so much data collected and used as well. What does this lead to? This leads to an increase in demand for data scientists.
What is Data Analytics?
An important word because it means drawing conclusions based on certain facts. In fact, there is so much emphasis given to it nowadays.
As the name itself suggests, it is a process by which data is examined and studied so that a conclusion can be reached. Today this has taken on a very sophisticated meaning as it involves specialized systems and technical software.
Types of Data Analytics Application
Since this, a very highly organized specialization, the systems of Data Analysis can be separated into two types of Analytics Applications. They are:
(a) Quantitative Data Analysis
This process involves analyzing the numerical data along with the quantifiable variables which can then be compared or measured statistically.
(b) Qualitative Data Analysis
This process involves less analysis and more of understanding the non – numerical data such as audio, video, images, and other points of view.
Data Analytics Tutorial Process
However, what is Data Analytics just does not involve only analyzing data only. There is a lot involved at the beginning of the process such as collecting, preparing and then testing, etc. So that the accurate results are produced. Data is first collected from various sources and then combined.
After that, it is analyzed. Because this process is very systematic and highly technical, so there is a separate team of Data Scientists who are responsible for getting the right data and making it ready for analysis.
How Does Big Data Analytics Make Working so Easy?
Because there are trained professionals available to do this process. Usually, it is a team that first looks out for the changing trends.
Based on that they compile the reports and analyze them as required, maybe daily, monthly and so on. In fact, it is easy because it can be classified into the following steps:
First Data Collection, then particular data analytics tools and applications and then studying or analyzing the results.
Types of Data Analytics
The process starts with the first building an Analytical Model. To aid in this process, there are predictive modeling tools and other software and programming languages such as SQL, Python, and Scala. Initially, the data is first tested and revised if required and then finally it is run in production mode so that the complete data can be analyzed.
What are the Various Tools Used in Data Analytics?
There are some analytics systems such as R Programming, NoSQL Database or Hadoop Cluster. In fact today, R programming is considered as the leading tool in the analytics industry. This is based on their popularity, performance and learning experience.
Overall, the data analytics tools available today are affordable and easy to use by most of the companies and many in fact consider it worth spending on because of the value that it brings to the Company.
So in Conclusion, today the focus is more and more on business needs and strategic oversight. So what is data analytics and its role has taken an important part in the field of business.
Today there are many open-source tools that are very popular and user-friendly. Then there are highly specialized Data analytic tools. For instance, there is R programming which is used for Data Mining, then there is Python which is used for data visualization. These help in making highly informed business decisions.
Today it is important to know what is Data Analytics and how is it used in various types of industries such as financial services, Energy management and even in Health care. Because knowing and learning what is Data Analytics helps the organizations to make better and most importantly informed decisions.
Why has Data Analytics become so important?
What is Data Analytics has been answered? As it is commonly understood is a tool that examines data andthen draws a reasonable conclusion from it.
Today, much commercial business uses Data Analytics tutorials because it helps them to take well-informed business decisions relating to increasing revenues, improving operational efficiency and customer relations. Not just that it has become more and more specialized with the latest software to help in this regard. That is the reason why it has become very important and popular.
Types of Data Analytics Applications
There are many different types of applications ranging from basic Business Intelligence (BI), then reporting and online analytical processing (OLAP) and then there is the Advanced Analytics. These can be further classified into
Exploratory Data Analysis (EDA) – that is here patterns and relationships in data are examined.
Confirmatory Data Analysis (CDA) – that is statistical techniques are used to understand if a certain hypothesis is true or false.
To state in simple terms, the work of EDA can be compared to Detective work and the CDA Analysis can be compared to the work of Judge during the trial.
Data Analytics Process
The process starts with the collection of data. For this, first of all, it is important to understand what type of data or information is needed.
After that, the engineers or respective IT team assembles or organizes the data. Then it is converted into the required format and then transferred onto the analytics system. Today there are many specialized Analytics systems such as Hadoop Cluster, NoSQL Database, etc. which analyze the data.
How do Big Data Analytics Tutorials Make Working so Easy?
Today there are many software tools that make the whole process easy. For example, there are advanced analytical tools that make the process of collecting, integrating and preparing data very specialized and easy. After that the process of developing, testing and revising the data starts.
All this is done through logical steps by different data engineers and IT staffers. They are highly professional and skilled in their respective fields making it all simple and easy.
Types of Data Analytics
Basically, there are two main types. The first one is the Quantitative data analysis which studies or analyzes numerical data and measures them statistically.
The second is the Qualitative Data Analysis which studies or examines any-numerical data such as a phrase, themes, points of view and this may include audio and video as well.
There are various basically broken down into four basic types:
(i) Descriptive analytics
(ii) Predictive analytics
(iii) Diagnostic analytics
(iv) Prescriptive analytics
Data Analysis is the process of analyzing the set of data in order to make decisions or come to a conclusion with the help of specialized software.
The techniques and technologies of data analysis are used to stimulate organizations in commercial industries for assisting in taking the correct business decisions by researchers and scientists. Business Intelligence (BI), Advanced Analytics, Reporting and online analytical processing (OLAP) approaches to analyzing data.
Knowing what is Data analytics can greatly help to increase the revenues, improve the operational efficiency, customer service efforts, and optimize the marketing campaigns in order to boost the overall business performance of the company.
(i) Exploratory data analysis (EDA) which help in relationships in data and patterns
(ii) Confirmatory data analysis (CDA) which gives statistical techniques to verify the hypotheses about a data set if it is true or false.
(iii) Quantitative data analysis ( numerical data)
(iv) Qualitative data analysis (none- numerical like images, text, videos, audio etc)
(v) Data Mining is a process of documenting via large data sets to identify patterns, relationships and trends
(vi) Predictive analysis to predict the behavior of customers, future events equipment failure, etc.
(vii) Machine Learning used in automated algorithms, artificial intelligence, etc
(vii) Big data analytics tutorials
(viii) Text mining for analyzing emails, documents, and text contents.
It is the science of examining of analyzing if the raw day to draw out conclusions of findings of the given information. It involves referring to a mechanical or algorithmic technique of data sets to look for correlations.
It is beneficial in large businesses to make the right decisions, to verify and to discredit the existing models or theories. What is Data analytics at a broader level? It has quantitative and qualitative strategies to enhance the growth and productivity of the business to achieve further success.
How Does Big Data Analytics Make the Working so Easy?
Knowing what is data analytics and how it is used for credit card companies, insurance firms, retail banks, and other financial services have become important. The big data is used in various ways like:
(i) Compliance analytics
(ii) Customer analytics
(ii) Operational analytics
(iv) Fraud analytics
The data used in communications increase the number of new customers or subscribers, retaining the current customers and expanding the current subscribers based on their priorities.
What are the different kinds of Data Analytics tools?
The data analytics tools that are available for personal use, easy to use since no coding us required. These data analytics tutorials and tools will help to interpret and manage data in a better and more efficient way.
The various tools included are:
(i) Tableau public
(ii) Open Refine
(iii) KNIME
(iv) Google fusion table
(v) NodeXL
(vi) Wolfram Alpha
(vii) Google search operators
(viii) Excel server
(ix) Dataiku DSS
This article has given you the insight to differentiate between Big data, Data Analytics, data science-based on who is who in data analytics so that you can be a professional in this field.
Data Science VS Data Analytics
Big data is definitely the talk of the town as it plays a major role in the technological world nowadays. The legal insights and amazing results that business sectors can glean are all because of big data. Having said that, how can such massive datasets be created?
This requires proper tools and understanding in order to expose the right information from all the data. To understand big data better, data science vs data analytics tools have also become part of academics in addition to a major part in business intelligence.
Check out the detailed video of the difference here:
It can be a bit confusing to find the differences in data science vs data analytics. They are both surely interconnected however with different results and also have different approaches. If you have decided to study the data that your business is creating then it is important to understand what is being brought to the table and the uniqueness of each one.
You can optimize the big data analytics by breaking down both the categories, examining the differences and show the real value they will deliver.
Data Science
This study is focused on finding insights that are actionable from massive sets of structured as well as raw data. It helps to identify what we don’t know first.
The data scientists make use of various techniques to get answers. They incorporate computer science, statistics, machine learning, predictive analytics, etc. they come up with solutions to problems that have not even been thought about yet.
The main goal of data scientists is to ask questions. Then they locate potential ways to study. They are not concerned about particular answers.
They lay more emphasis on finding the correct question that should be asked. The professionals accomplish this by finding better ways to analyze the data. The try to predict potential trends and explore the disconnected data sources as well.
Data Analytics
This analytics focuses on working on existing data sets. The process and perform statistical analysis of the existing data. They find solutions to current existing problems.
They focus on creating various ways to capture. Organize and process the datasets. The result that comes out of this process gives immediate solutions and improvements. They also give simplified results.
Spotting Difference
Data science is a larger process when compared to what is data analytics. The umbrella term used for mining large datasets is called data science. Data analytics tools and the study itself is concentrated version and can also be a part of this large process. Analytics can be applied with immediate effect on current problems.
Conclusion
Data science does not work with specific queries. It is just going through and analyzing massive datasets. What is Data analytics – it is more focused and looks for solutions to existing issues in current data.
Data science finds out the questions to be asked whereas what is data analytics- finds the answers to the questions that have been asked.
So, if you are also planning to build your career in data analytics to the next level, then you should enroll in the Certified Data Analytics Course.
