The two most popular programming tools in data science field are R and Python. Both are free and open-source and were developed in the early 1990s. R was developed for statistical analysis and Python was developed as a general-purpose programming language.
Data Collection, Data Exploration, Data Modeling, Data Visualization are the processes in Data Science. Let’s see how these two languages contribute in these processes.
|Data Collection||We can import data from Excel, CSV, and from text files into R. Files built in Minitab or in SPSS format can be turned into R data frames as well. |
Rvest will allow you to perform basic web scraping, while magrittr will clean it up and parse the information for you. These packages are analogous to the requests and beautiful soup libraries in Python.
|Python supports all kinds of different data formats. This includes CSV, JSON, SQL, etc. You can also create datasets. You can get any kind of data with Python. If you’re ever stuck, google Python and the dataset you’re looking for to get a solution.|
|Data Exploration||R was built to do statistical and numerical analysis of large data sets, it have many options while exploring data with R. You’ll be able to build probability distributions, apply a variety of statistical tests to your data, and use standard machine learning and data mining techniques.||We can use Pandas for data analysis. Pandas is organized into data frames, which can be defined and redefined several times throughout a project You’ll be able to easily scan through the data you have with Pandas and clean up data that makes no empirical sense.|
|Data Modeling||In order to do specific modeling analyses, you’ll sometimes have to rely on packages outside of R’s core functionality. There are plenty of packages out there for specific analyses such as the Poisson distribution and mixtures of probability laws.||You can do numerical modeling analysis with Numpy, scientific computing and calculation with SciPy, and can access a lot of powerful machine learning algorithms with the scikit-learn code library. Scikit-learn offers an intuitive interface that allows you to tap all of the power of machine learning without its many complexities.|
|Data Visualization||R was built to do statistical analysis and demonstrate the results. It’s a powerful environment suited to scientific visualization with many packages that specialize in graphical display of results. The base graphics module allows you to make all of the basic charts and plots you’d like from data matrices. You can then save these files into image formats such as jpg., or you can save them as separate PDFs. You can use ggplot2 for more advanced plots such as complex scatter plots with regression lines.||The IPython Notebook that comes with Anaconda has a lot of powerful options to visualize data. You can use the Matplotlib library to generate basic graphs and charts from the data embedded in your Python. If you want more advanced graphs or better design, you could try Plot.ly. This handy data visualization solution takes your data through its intuitive Python API and spits out beautiful graphs and dashboards that can help you express your point with force and beauty.|
Python is a powerful, versatile language that programmers use for a variety of tasks in computer science and is very user-friendly. On the other hand, R is a programming environment specifically developed for data analysis.