Data Analytics training using R programming for engineering students

Summary

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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1. Data Analytics using R

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What is Data Analytics?

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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What is the Role of Data Analytics?

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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Why Data Analytics is Important?

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.

• The earliest adopter of Data Analytics is the financial sector. Data Analytics had a great impact on banking and other financial industries. It helps such institutions study the market and do risk assessment studies. The evaluation of credit scores is an every-day example of Data analytics in motion.

• It is used in healthcare to help with the detection of diseases. It also helps with targeting risk groups and allow for risk mitigation. It allows hospitals to predict outcomes of treatments, and foresee reactions to drugs.

• It also forms the crux of scientific research of all kinds. Everything from robotics, to astrophysics, requires it to make sense of large data. All complex analytic techniques use big data to draw new insights.

• Within the IT sector, it has caused revolutionary leaps thanks to the explosion of IoT and ML. They provide great opportunities for Data analytics to grow even further. Most IoT-based systems help with the collection of data required for analytics.

• It has helped improve the buying experience through customer feedback and pattern recognition. Thanks to Data Analytics, companies now know what their customers want. This puts them in a better position to cater to customer needs. Personalized customer recommendations are due to data analytics.

• Data analytics is also used for effective and efficient Energy Management. Companies use such technologies to optimize energy- production, generation, usage, and distribution. Networks are monitored to prevent energy wastage, making the system more efficient.

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What is R in big data analytics?

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

1. Variety of in-built techniques and functions that help in extracting data
2. In-built plotting commands make it easier to visualize data
3. Ability to plot at high levels of abstraction.
4. Efficient Data Wrangling methods
5. Efficient technology thanks to RHIPE, which is the R and Hadoop Integrated Programming Environment.
6. Convenient algorithms for MapReduce jobs
7. Predictive analysis through ORCH or Oracle R Connector for Hadoop
8. Linked to several big software and hardware such as Hive tables, Apache Hadoop and Oracle database tables.
9. It is open-source software and hence has great variety with regards to tools

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What Should I learn to become a Data Analyst?

Skills Required to Become a Data Analyst:

• Programming skills in languages such as R and Python
• Good command over statistical mathematics and inferential statistics utilizing experimental designs
• Experience and skills with regards to using Machine Learning
• Good Data wrangling skills to convert data into other, more convenient formats
• The ability to visualize data
• Effective communication skills
• Data Intuition

How to Become a Data Analyst

• Obtain a bachelor’s degree in mathematics, economics, statistics, finance, or computer science
• If you are interested, go for a master’s in Data Science or Business Analytics
• If you aren’t that skilled, get help in calculus and coding to better your skills
• Attend as many workshops as possible on these topics
• Take up projects on this field, attend online classes and get hands-on experience with regards to these fields
• Have a firm understanding of college algebra, graphing and multivariable calculus
• Improve your knowledge in the domain of descriptive and inferential statistics
• Be comfortable with Python, R, and Java
• Develop strong presentation skills and learn to be effective at communication
• Familiarize yourself with the software Microsoft Excel
• Invest your time learning about Data Management and Manipulation

Data Analytics training using R for engineering students

Learn more about this project

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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Data Analytics training using R programming for engineering students