Data Analytics is the domain of the science of analyzing the raw data to draw fruitful conclusions based on that information. It is a science that helps individuals and companies make 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 to discover some useful information, presenting conclusions and then supporting the decision-making process. Data Analytics use various tools, techniques and algorithms that help individuals or organisations make useful insights and succeed. There are various types of Data Analysis including 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 to the raw data to find trends and solve 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 amounts of data to uncover hidden patterns, correlations and many such insights. 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 make 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 business performances to capture a greater market and succeed in an increasingly competitive world. It has a key role to play in the finance sector as it mostly deals with huge amounts of data. Here Data Analytics was used to predict market trends and then assess the market risks. It has a use in 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 patient outcomes, improving diagnostics techniques, efficiently allocating funding, predicting the risk of new treatment techniques and so on.
Data Analytics has proved to be essential for almost all fields may it be the crime detection department, environmental protection department, cybersecurity, pharmaceuticals, logistics, and various other fields. The seemingly endless applications and the advantages that Data Analytics has had have clearly revolutionized the world. More and more data are created each day, collected and then Data Analytics is applied to create new opportunities for various parts of business, science, research and everyday life.
How does Data Analytics work?
Data Analytics is a strategy-based science domain where raw data is analyzed to detect trends, answer various questions and draw conclusions from that large amount of data. Data Analytics has various steps involved in the working. They are:
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Data RequirementFor making Data Analytics 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 the detection of the population of a city, for others, it may be to calculate the 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 importance to make accurate data collection 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 organized and analyzed for further processing. Proper data organization 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 anomalies or get rid of them. 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 making 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 a Data Analyst various skill sets 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 skill knows 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 skill set for becoming a Data Analyst as it is used for Artificial Intelligence and for Predictive analysis. Though complete knowledge of Machine Learning is not required for this job role but still basic knowledge is must.
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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 a 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 Microsoft Excel is needed. From a basic understanding of Excel to an advanced understanding of 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. 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 formal training in the subject. An in-depth and designed learning of the subject is a must for landing 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 right now. The DockLearn blogs are full of information and provide proper insight into the subject. Do log into the DockLearn website and get fully researched data that you may want for becoming a Data Analyst. Also, there are many courses available that will help you in excelling and taking up a job as a Data Analyst.
