The world is full of data, thanks to the internet. Data makes up everything from family photos to your internet search histories. The amount of digital data out for grabs is growing and actually doubles every two years. This inflow of data is constantly changing the way we live. As per a study by IBM, we generate 2.5 billion GB of data on a daily basis, and this rate is also on the increase. By 2020, we will be producing 1.7 MB worth of data every second! Thus, it is safe to say that our future lies in successfully handling and analyzing this large pool of data. So, in this article, we will be taking a look at a Data Analytics project that will utilize R Programming to test various concepts. Here’s everything you need to know about the field of Data Analytics.
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Data Analytics includes everything used in analyzing data and finding answers from it. It consists of various sub-processes that when combined, helps you skim through large volumes of data and make sense of it. The different components of data analytics are:
1. Descriptive Analysis which looks for historical trends. This process aims to answer ‘what happened?’. It involves the analysis of the metric Return on Investment. But, this analysis only summarizes data, rather than making predictions about data.
2. Diagnostic analytics tries to find the answer to, ‘Why thing happened?’. The techniques are the same used in descriptive analytics, and it helps to find causes.
3. Predictive analytics answers the question, ‘What will happen?’. This includes identifying trends and finding out the probability of things happening.
4. Prescriptive analytics helps to answer the question, ‘What must be done?’. By utilizing insights, this process takes data-driven decisions. It is one of the most crucial components as it allows businesses to make informed decisions.
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Data analytics combines information technology, mathematical statistics, and business management. They use insights from these fields to help organizations make the right decisions. The primary role of Data analytics is to improve the efficiency of companies.
It consists of everything from data mining, to find patterns. Statistical analysis is important in data analytics, helping us draw insights from data. Hence, statistical programming languages like R and Python are essential to the process.
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Data analytics finds application in most major fields in the world. It helps to optimize operations, and leverage patterns and trends within the market. Improving a company’s performance enables it to grow and succeed. As competition increases, being able to use such technology becomes crucial to survive.
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R is an open-source statistical scripting language which came out in 1995. It has since then grown to become a medium for data scientists and analysts around the world. It includes several packages, shelf graph functions, modeling techniques, and segmentation functions. These help with handling big data sets, making it the ideal language for analytics. All major tech firms such as Microsoft, Google, and Facebook use R to perform Data analytics.
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Benefits of Using R
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Skills Required to Become a Data Analyst:
How to Become a Data Analyst
As mentioned before, a major part of Data Analytics is practice and gaining practical knowledge. The first step to being a good analyst is to build your knowledge with respect to R. Through this project, we will be taking a look at how R is used in Data Analytics. This R Programming project will help you learn the basic concepts of R and also teach you how to implement it in Data Analytics systems. As part of this course, you will work on 2 real-time projects, this data analytics training course ensures that you don’t get stuck with just theory, but rather learn the practical side of things.
This Data Analytics using R Project will teach you the basics of data analysis while showing you real-life examples and training on Diamond quality datasets. You will also learn how to visualize data, manipulate data using R and how to segment and group data. It will cover everything from the basics of R, including Software Installation to more advanced topics such as Data Frames and segmentation. Make use of your time, and pick up vital skills using this project to propel your career in the field of Data Analytics today!
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