Tuesday, 17 March 2020

top 10 free E-books to learn data science

Top 10 free E-books to learn Data Science:


E-books are known as Electronic Book.This is the medium of recording information.


You can download E-books by contacting us

  • Neural Networks and deep learning

  • Think Bayes

  • Statistical Learning with Sparsity:The Lasso and Generalizations

  • The Field Guide of Data Science

  • The White Book of data science

  • Machine Learning

  • Beginners Guide to Analytics

  • Data Science:Theories, Models,Algorithms,and Analytics

  • Automating Boring Stuff with Python

  • An introduction to statistical Learning

Monday, 16 March 2020

R vs Python

R vs Python:




Which One is better for data science?


R Programming:



R is one of the oldest programming language developed by academics and statisticians.R comes into existence in year 1995. Now R is providing the richest ecosystem for data analysis.


Python Programming:



On the other hand Python can do the same tasks as R programming language does. The major features of python are data wrangling, engineering, web scraping and also very useful for hackers and so on.


Difference R or Python:


  • R is more functional, Python is more object-oriented.
  • R has more data analysis built-in, Python relies on packages.
  • R has more statistical support in general.
  • Straightforward to do non-statistical tasks in Python.
  • Parallels between the data analysis workflow in both.
  • R is more functional, it provides variety of functions the data scientist i.e Im, predict and son on. Most of the work done by functions in R.
  • R was created as a statistical language, and it shows, statsmodels in python and other packages provide decent coverage for statistical methods, but the R ecosystem is far from large.
  • R provides the build in data analysis for summary statistics, it is supported by summary built-in functions in R.
  • Well placed libraries like beautifulsoup and request.
  • Web scrapping in python is much easier than R.This applies to other tasks that we don't see closely.