In the emerging field of data science especially in big data science there are four essential objectivesR Programming

In the emerging field of data science, especially in big data science, there are four essential objectives, and they
are: (i) understanding of data, (ii) understanding of systems, (iii) understanding of machine learning, and (iv)
understanding of scalability and complexity. In addition, it is important data analysts and data scientists are
prepared to learn new big data systems (e.g., Hadoop, Spark) and programming languages (e.g., R, Python,
Scala). The report writing and presentation skills are also equally important; therefore, this course encourages
you prepare your documents using Latex with well-known IEEE, ACM, Springer, and Elsevier formats.
The main goal of this assignment is to prepare and test your knowledge in the first objective; that is, knowing
and preparing your data (Chapters 1, 2, and 3). It helps you understand the characteristics of data such that
correct techniques and technologies can be selected to process and analyze the data, and make decisions.
1. Download carpet.csv and hardwood.csv data sets from the following website and describe them.
2. Extract statistical information (e.g. number of observations, dimension of the data, mean of each feature,
etc.) from these datasets. Also present visual representations (e.g. histogram, scatter plot, etc.) of the data. Is
the dataset imbalanced, inaccurate or incomplete? Is it a trivial data or possibly a big data? Does it have
scalability problem? Are they high dimensional? Do you need to standardize? Do you need to normalize?
You must think about these problems and provide your explanation scientifically. You need to write
programs to read the data and generate results to explain all of the above – since you need to show/justify.
3. Select another two data sets from the links provided in the CSC 510 Course Resources.pdf document (on
Canvas) and describe the issues that you couldn’t explain using the carpet and hardwood floor data sets.
4. Merge carpet.csv and hardwood.csv and create a new csv file called carwood.csv in which insert a new
column with label 0 for carpet observations and label 1 for hardwood observations. Now shuffle the
observations randomly and create a new file called randcarwood.csv. Then divide this file into 80:20 and
name the files with Trainrandcarwood80.csv and Testrandcarwood20.csv respectively. You must write a
program to do these processes using a programming language of your choice. The use of the language R,
Python, or Scala is preferred; however, it is your choice.
5. Read the following paper and summarize it clearly in your own words (no cut-and-paste from the paper or
from the Internet):
? S. Suthaharan, "Big data classification: Problems and challenges in network intrusion prediction with
machine learning," ACM SIGMETRICS Performance Evaluation Review, 41(4), pp. 70-73, 2014.
6. Prepare a Latex document using one of the IEEE, ACM, Springer, or Elsevier Latex formats. However,
make sure you select two-column format. Submit your assignment report via Canvas

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