The field of Data Science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of Data Science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. Basically, R programming language has been used, along with some Python libraries to perform exploratory data analysis on the datasets which have been used. Different packages or libraries which are available in R and Python have been explored. Data pre-processing has been performed using Python libraries.