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Data science is all about capturing data in an insightful way where Machine learning is a key area of it. Data science is a fantastic blend of advanced statistics, problem-solving, mathematics expertise, data inference, business acumen, algorithm development, and real-world programming ability. And Machine learning is a set of algorithms that enable software applications to become more precise in predicting outcomes or take actions to separate it without being explicitly programmed.
The distinction between data science and machine learning is a bit fluid, but the main idea is that data science emphasizes statistical inference and interpretability, while machine learning prioritizes predictive accuracy over model interpretability. And for both data science and machine learning, open source has become almost the de facto license for innovative new tools.
Are you planning a machine learning or data science project and confused between Python and R? Both are open-source, free and develop robust ecosystems of open source tools and libraries that help to perform analytical works more easily. So, let's have a look at whether Python or R, is better for data science considering machine learning and artificial intelligence as included in the term data science.
For data science and machine learning the best programming language that comes to mind will be R and Python but choosing between them is always a dilemma.
Python originated in the late 1980s, as an open source scripting language with a built-in object-oriented programming. It has been used in applications such as Dropbox, YouTube, Instagram, and Quora. Python also plays a key role in Google’s internal infrastructure.
Python has more sports libraries (numpy, scipy, and matplotlib) and functions for almost any statistical operation/model building. After the introduction of pandas, it has become very strong in operations on structured data and also easy to work with time series data and data frames. With Anaconda from Continuum Analytics, the package management has become very easy to use. The notebook IDE of IPython / Jupyter is also a very right choice.
R is an open source counterpart of SAS followed by the Python’s footsteps, which has traditionally been used in research and academics and is a very cost-effective option. RCPP makes it very easy to extend R with C++. RStudio is a mature and excellent IDE. Because of the open-source nature of R, the latest updates will get released quickly.
Theoretical the difference between Python and R is large. Python is a full-service language developed by Unix script writer where R is a tool for data analysis developed by GNU packages similar to the S language. Let’s discuss more about Python and R for data science and machine learning.
Python is a general-purpose programming language and if your project requires more than just statistics (for instance, building a functional website) it's a better choice. On the other hand, R includes fewer statistical model packages to gain a better understanding of the underlying details and build something truly innovative.
Here is an in-depth overview for whether to choose python or R:
Python has a high number of useful libraries for data wrangling, collection, manipulation, and machine learning. For instance, Scikit-learn contains tools for data mining and analysis enhances Python's excellent machine learning usability. Another package called Pandas offers high-performance structures and data analysis tools along with a shorter development cycle. RPy2 is the right package if your development team needs one of R's major functionalities.
Just like Python, R has over 5000 libraries and tools catering to many domains, that improves its performance in machine learning projects. For instance, Caret gives added value to R's machine learning capabilities with its set of functions that make creating more efficient predictive models. With R you can take advantage of advanced data analysis packages that cover the pre-modeling, modeling, and post-modeling stages, and are directed to specific tasks such as data visualization or model validation. The network of statistical model packages for R is more extensive than in Python.
Python integrates better than R in project environments. Even if you take benefit of a lower-level language such as C, C++, or Java, along with a Python wrapper allows better integration with other components. Also, a Python-based stack can easily integrate the work into production for bringing it smoothly.
Python is a lightweight, fast, easy-to-use binary format for file types. And its syntax is highly readable like other programming languages, whereas the syntax of R is different. As simply as possible, python push data frames in and out of memory. In contrast to R, Python's readability ensures high productivity of development teams i.e. 600 MB/s vs 70 MB/s of CSVs. Python also helps is passing data from one language to another. Using R's non-standard syntax, you risk disruptions in the programming process.
Both the languages are interpreted languages. If you're at the early stages of your project and need exploratory work in statistical models, with just a few lines of code R makes it easier to write them than Python. Also, both of them have good IDEs (For instance Spyder for Python and RStudio for R).
With the introduction of R by Revolution Analytics, the initial struggle with large computations (say, like nxn matrix multiplications) is addressed. Now intensive computational operations are written in C which is rapidly fast. Being a high-level language Python is relatively slow compared to R.
In data science, it always tends to plot data in patterns to users. Therefore, visualizations become an important criterion in choosing a software and R completely kills Python in this regard.
One of the constraints of R is it stores the data in system memory (RAM). So, when you are handling Big Data RAM capacity becomes a constraint. Python does well, but as both R and Python have HDFS connectors, leveraging Hadoop infrastructure would give a substantial performance improvement.
As R algorithms from third parties, you might end up with many inconsistencies. With R you need to use a new algorithm every time for development and also need to implement new ways to make predictions and model data. In a similar way, requires learning for every new package. And R's documentation limited and it doesn’t help much. All these have a negative impact on development while using R. Here Python scores with its wider developer community and flexible model.
Python is a top pick if your project needs a flexible, multi-purpose programming language with a large community of developers and extendable with machine learning packages.
If your project is statistics-heavy, R is a better choice for the task. R is also an excellent choice for projects that require a one-time dive into a dataset. For instance, if you want to analyze a collection of text by deconstructing paragraphs into words or phrases and identifying patterns, R is a right choice.
R is an excellent choice if data analytics or visualization is at the core of your project. It enables rapid prototyping and working with datasets to develop machine learning models.
When it comes to machine learning/ data science projects, both Python and R have their advantages with the extensive availability of packages. Once you master both the languages, you can make the best of both worlds because the majority of the common tasks associated with one of these languages are feasible in both.
In data manipulation and repetitive tasks, Python performs better and it's definitely the apt pick if you're planning to build a digital product based on machine learning. Even so, choose R if you're at the initial stages of your project and need to develop a tool for ad-hoc analysis and dataset exploration, unless you possess a team which is well-versed in Python.
Or you can use Python for the early stages of data aggregation and then feed the data into R, which applies the well-tested, optimized statistical analysis routines built into the language. This way you can use R as a library for Python or Python as a pre-processing library for R. Now you can decide.