Innovative data analytics projects using R programming for engineering students

Nowadays data analytics is widely used in many industries to identify and analyze data as per authoritative necessities. Also, the data is used to upgrade business efficiency and gain. Thus, as an engineer, it is mandatory to know about data analytics if you want to pursue a career in this field. In this article we are going to discuss things like R program, data analytics projects using R, how to start a data analytics project, innovative data analytics projects for engineering students, etc.

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1. Data Analytics using R

What is Data Analytics with R?

Data analytics is the study and analysis of data in order to reach useful outcomes. When it comes to statistical computing, then everyone prefers the R programming language. It is a free open source software built especially for data analysis and data mining. It not only does data analysis but is also widely used to create software and other applications. With the graphical interface an add on feature of R programming language, this R language can be applied to various data analytics, linear and nonlinear modeling, time series analysis and many more. R language also has enough provisions to implement data sciences and machine learning algorithms. The first question that pops in any student’s mind is that what is the need to learn R for data analytics. Here are some points to be noted R, that will make it easier for you to understand its benefits.

  • R is an easy to learn and programming language.
  • It is free as it is an open-source platform.
  • There are over 6000 packages customized in a ready to implement a form for various functions.
  • A large number of forums are present to help you with this technology.
  • It performs straightforward handling of algorithms
  • Gives space and flexibility to amend mistakes

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How R is used in Data Science?

R programming language is now being used around the world in various firms and companies. The most basic use being collection, analysis of data and production of useful results using this data analytics for the benefit of the consumer and the company. Companies now don't have to wait for manual feeding of data and output production. With the R language, anyone can custom build data analytics programs that suit best to its interest and get the optimized results with limited manual work and fast response.

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Ways to study data analytics using R

The main focus of using R in data analytics is exploratory data analysis. In this type of data analysis, the R programming language helps to summarize and visualize the data set in such a way that the basic understanding of the data set and its structure is known. It helps in knowing the data’s origin and further in developing an algorithm for proper analysis and detailed visual representation. So, using R for data analytics is preferred over other languages.

R language consists of four main parts. These are

  • R console: involved in the writing of codes.
  • R Script: it is the interface to write codes. The steps are simple.
  • R environment: This is the space provided to add external data like data sets, vectors, variables, functions, etc.
  • Graphical Output: This is used to create graphs from the data set, that is this space is for visualization.

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How R is used in Data Science?

R data analytics allows users to analyze a wide variety of datasets by using its features and installed packages. Some of the data sets that can be analyzed using R data analytics concepts are as follows:

  • Vector: There are various kinds of vectors available like numerals, integers, etc. they can be studied and analyzed individually as well as if the vectors are present in any data set as a combination of two or three vector types, then also R programming can infer it.
  • Matrices: When any dataset is created in the form of rows and columns then that is called a matrix. This two-dimensional data analysis is also possible with the R language.
  • List: List is a data set with different types of data grouped together. The various variables that can be added to this data set are Continuous Variables, categorical Variables, Missing Values.

R programming also has the capability to deal with other structures such as if-else, for and while. R programming for data analytics is so common and easier because of the above mentioned wide variety of functions.

But this technology may appear difficult and wired at starting. That’s where the role of Skyfi labs come. Skyfi Labs provide step by step proper guidance to students.

How to start any data science project?

Below are mentioned some few steps to start any new project on data science:

  • Define the problem statement: Make clear what you want to achieve at the end of this analytics project. Write down the problem and the possible ways to solve it.
  • Data collection: This is the second step and also a crucial one. The collection of data is not an easy task. It requires time and effort. You will never find your required data to be present as it is in any data set. You have to search for it arrange it, rearrange it and finally assemble it.
  • Data cleaning: This step is a cumbersome task. Data cleaning is the process of removing duplicate and unnecessary data from your data collection. This step is essential in order to remove inconsistencies in your results.
  • Data analysis: This step is all about detecting patterns and trends in the entire data and group accordingly. This is the stage where you have to develop insights about the data behavior.
  • Data modeling: In this step, you build an algorithm to solve your problem. This step requires breaking your data into two parts that is data splicing. One set of data is for training and developing the model, the other is for testing purposes.
  • Optimization and deployment: This step usually involves making the model more accurate and efficient in producing the most optimized result possible.

Innovative Data Analytics projects using R Programming for engineering students

1. Data Analytics using R:

In this data analytics project, you will learn from basic to advance data analytics. You will also get step by step guidance to use the R programming language. You will get to perform real-time analysis on World Happiness and Diamond quality data sets. This entire project is divided into various sections. Section 1 contains an introduction to data analytics using the R programming language. You will also learn all the installation steps for R. Section 2 is all about R language, starting from the very beginning from its different types of data, loops, conditions, and matrices. Section 3 consists of the functions and various data frames in R. section 4 and 5 is about data manipulation and visualization. And at last, all of these concepts are applied in the module to develop the final project on data analytics using the R language.

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2. Fake news detection using Data analytics:

In this data analytics project, you will use news.csv data set to detect fake news. You will also learn about modules like numPY, pandas, Sklearn, etc. while doing this project

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3. Height and weight prediction data analytics project

As part of this data analytics project, you will use heights and weights dataset to develop the height and weight prediction model.

Apart from the above-mentioned projects, you can also refer to:

  • Data analytics project using text mining dataset
  • Data analytics project using human activity recognition dataset
  • Data analytics project using million song dataset
  • Data analytics project using census income dataset
  • Walmart sales forecasting data analytics project

Skyfi labs provide an overall development training course on R programming and data analytics.

So anyone willing to study this new technology in order to take it to an advanced level like data analytics can now enroll in Skyfi labs R programming language data analytics courses.

Innovative data analytics projects using R programming for engineering students
Skyfi Labs Last Updated: 2019-12-31

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