With the emergence of the era of Big Data, frameworks like Hadoop arose and the focus of the enterprise shifted to which was processing this data. This is where data science came into the picture.
Data science is a blend of various scientific methods and processes, machine learning principles and algorithms with the goal to solve analytically complex problems. The basic aspect of data science is to unveil the hidden patterns from raw data. Data science requires three basic prerequisite skillsets: mathematics expertise, technology and hacking skills and strategy and business expertise.
For any company that wishes their enterprise to be more data-driven, data science is their primary key to success. The professionals with data science skills are highly sought after these days and basically consists of three main components i.e. organizing, packaging and delivering the data (OPD of data). Now let us have a look at what happens during these three stages.
- Organizing the data: After applying the best practices in data handling, in the stage of organizing the data, the structure and physical storage of data is stored and executed.
- Packaging the data: At this stage, data is combined and modified and combined exquisitely in a presentable form. The development of visualization, application of statistics and creation of prototypes are that what happens mainly in this stage of packaging of data.
- Delivering the data: In this stage, it is made sure that the final outcome has been delivered to the concerned end-users/customers. It basically involves the final analysis of data.
In the war of Data Science tools, both R and Python have their own sets of pros and cons. Selecting one over the other should be done on the basis of certain criteria or attributes:
- Availability/Cost: Both R and Python are open-source programming languages that are absolutely free of cost. R is focused on giving a more user-friendly way to do data analysis, statistics and graphic models. Whereas, Python is a programming language that focuses more on productivity and code reliability.
- Learning: In comparison with all the programming languages, R is said to have the steepest learning curve making it a tough choice for beginners or for the ones who are not so comfortable with coding. Whereas Python’s documentation and sharing features are also awesome enough to make it more and more mainstream. Let’s have a look at the basic example. Let’s compare a Python and R program (to find factorial) to analyze the ease with which they can be learnt.
Also, both languages cannot be differentiated on the basis of data handling abilities, as both R and Python have good data handling abilities and options for parallel computations. Python possesses numerous Big Data frameworks like Feather (which enhances fast reading and writing of data), Ibis (which integrates with the rest of the Python ecosystem), ParaText (which integrates with Pandas: paratext.load_csv_to_pandas(“data.csv”)).
In graphical capabilities, both R and Python possess advanced graphical capabilities and they use the support of many frameworks and packages that would provide them better graphical capabilities. When R uses ggplot2, htmlwidgets,etc. Python uses Altair, Bokesh and Geoplotlib which are much better tools for data visualization.
Advancements are quick both in R and Python as both are open-source languages. Since R has been used in the academics for a very long time, the development is very fast in this field and since Python has an open contribution it will be having more advancements in comparison to R. In Data Science, Python’s flexibility and combination of specialised Machine Learning libraries (like Skikit-learn (that holds some 150000-160000 unique visitors per month), Pybrain,etc) plug directly into the production system.
R has recently added support in the form of basic packages that are still in progression. The KerasR is a package that act as an interface to the original Python package, Keras. Python, on the other hand, is rich with numerous packages such as Keras and Tensorflow.
In the simplest or layman language, data science is the grow-up version of a kid whose curiosity knows no bounds and can never let go of the question ‘why’. So basically every data scientist is a combination of many blends.
Although both the languages have their own share of benefits, we can clearly see that bending towards Python and it has become an obvious choice for startups because of its unique and attractive features that support Data Science. So are you planning to opt for the job of a Data Scientist? Get started with Python!