Data Analytics is the domain of science of analyzing the raw data so as to draw fruitful conclusions based on that information. It is a science that helps the individuals and companies make out sense of the raw data and then deliver insights and trends based on it. It is a vast process involving steps like inspecting the raw data, cleansing it, then transforming it, followed by modelling the raw data with the aim of discovering some useful information, presenting conclusions and then supporting in the decision-making process. Data Analytics use various tools, techniques and algorithms that help the individuals or organisations make useful insights and succeed. There are various types of Data Analysis including the prescriptive, diagnostics, descriptive and predictive analysis.
What exactly is Data Analytics?
Data Analytics is the discipline of science that focuses on extracting insights from the raw data, and also includes the collection, analysis, organization and storage of the data. The main aim of Data Analytics is to apply statistical analysis tools and technologies on the raw data to find the trends and solve the problems. It has developed itself as a means of analyzing data, improving decision-making processes and business results and based on that shaping the business processes. It examines huge amount of data to uncover hidden patterns, correlations and many such insights. The Data Analytics helps organizations to harness important and useful data and then use it to identify new business opportunities. This in turn helps the organizations in making smarter business moves, make more efficient operations, get higher profits, attract more customers and give their customers the utmost satisfaction that they expect from the organization.
Why Data Analytics is important?
Data Analytics has improved the business performances to capture greater market and succeed in an increasingly competitive world. It has got a key role to play in the finance sector as it mostly deals with huge amount of data. Here Data Analytics was used to predict market trends and then access the market risks. It has a use in the Credit Score analysis as this score is essential in determining the lending risks. It also proved useful in determining the market risk and for detecting and preventing the frauds that were prevalent in the banking sector thus improving the efficiency.
Data Analytics also has made a mark in the Healthcare sector by providing critical health informatics. It helped in predicting the patient outcomes, improving diagnostics techniques, efficiently allocating funding, predicting the risk of new treatment techniques and so on.
The Data Analytics has proved to be essential for almost all the fields may it be crime detection department, environmental protection department, cybersecurity, pharmaceuticals, logistics, and various other fields. The seemingly endless applications and the advantages that the Data Analytics has had, has clearly revolutionized the world. More and more data are created each day, collected and then Data Analytics is applied so as to create new opportunities for various parts of business, science, research and everyday life.
How does Data Analytics work?
Data Analytics is strategy-based science domain where raw data is analyzed to detect trends, answer various questions and draw conclusions from that large amount of data. The Data Analytics has various steps involved in the working. They are:
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Data RequirementFor making Data Analytics to work, first it is important to decide what kind of data the process is to be carried out on. As for different data the conclusion would differ. For instance, one might want the process to work for detection of population of a city, for other it may be to calculate marks of a certain group of students, and so on.
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Data ProcurementThe next step involves Data Procurement. Here, proper data collection is necessary because if the data collection is not proper then the results would differ accordingly. So, its utmost important to make accurate data collection so as to get the most precise and accurate results for the data collected.
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Data ProcessingAfter the Data Collection is done, next step is the Data Processing. Here the gathered data is to organized and analyzed for the further processing. Proper data organization too is required as if not done, it might also result in some sort of result inaccuracy.
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Data CleansingThe Data collected at all times may not be totally useful. It might have some sort of repetitive elements, or some error may be present in the collected data. So, it is important to either fix certain anomaly or get rid of it. So, at this stage the Data is properly cleansed by either removing or fixing the errors.
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Data AnalysisThis is the most important step in the Data Analytics part as at this step the data is analyzed and based on that the conclusions are drawn. Various data analysis tools, techniques and algorithms are used here like Data Visualization, Regression, Classification, Correlation and so on. It may be that even after the Data Cleansing step some anomalies may be present, so it is removed at this step.
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Data CommunicationAfter the Data Analysis step, the data is converted into an organized and simple document form. This is then made useful for taking insightful decisions and for making decisions based on this data. The document presented here may be in the form of graphs, charts, tables, or any other form.
Skills Required for becoming a Data Analyst
The person who works in the Data Analytics field is known as the Data Analyst. And to become Data Analyst various skillsets are required. Most of them are listed below:
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Data VisualizationData Visualization is an important and engaging way of presenting the raw data. This skill is important because it helps in creating and presenting the data in the form of charts, graphs, tables or any other form. The one who is skilled with this skillset does know how to present the data in an engaging form.
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Statistical KnowledgeIn today’s world, probability and statistics have become a key aspect for analyzing the data. This skill is needed because the individuals who have knowledge of this skill do not make errors in arranging, analyzing and interpreting the data.
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Machine LearningMachine Learning too is considered an important skillset for becoming a Data Analyst as it is used for Artificial Intelligence and for Predictive analysis. Though complete knowledge of the Machine Learning is not required for this job role but still basic knowledge is must to have.
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Data CleansingAs discussed above Data Cleansing is an important step in the Data Analytics process. So, the professionals working in this field need to have thorough knowledge of the Data Cleansing skill. Here one must know how to find inconsistencies, errors and anomalies in any raw data given.
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Microsoft ExcelA Data Analyst may have to present the data in any form so a proper knowledge of the Microsoft Excel is needed. From basic understanding of the Excel to advanced understanding of the Microsoft Excel may it be VBA lookups or writing macros, a thorough understanding is required.
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SQLNow the Data Analyst also needs to have a knowledge of a programming language. The SQL stands for Structured Query Language is also an important skill to possess which will help extract raw data from various sources.
To become a Data Analyst, one needs to have a formal training of the subject. An in-depth and designed learning of the subject is must for landing up with the job of a Data Analyst. If you think that you can excel in this field and are motivated and passionate about taking up your career in the Data Analytics field, then you should start researching and preparing for the Data Analyst job role from right now. The DockLearn blogs are full of information and for providing proper insight of the subject. Do log into the DockLearn website and get full researched data that you may want for becoming a Data Analyst. Also, there are many courses available that will help you in exceling and taking up a job as a Data Analyst.