What is all those noise in the analytics space?

Being an IT professional and being in the industry for more than 4 years now, I am no stranger to buzzwords like Machine Learning, Artificial Intelligence, and Data Science. Nevertheless, what I find interesting is not the algorithms nor the tremendous power those algorithms harness when they are applied to an enormous data set but the discussions and debates that go on when professionals try to prove which is better – R, Python, SAS, or any other data analytics platform? And when talking about all these, how can one forget the long-standing debate: Python for Data Science and Machine Learning vs. R for Data Science and Machine Learning.

If you are a professional do you recollect the usual debates that happen over a cup of coffee in the cafes: Python for Data Science and Machine Learning or any other language for data science course and Machine Learning?

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If you are a student, do you remember your teachers talking about how data is changing the digital world when you attend seminars and guest lectures? You would also recollect browsing the internet and the results that popped on your screen from various online education platforms.

If you belong to a non-technical background, have you ever wondered how Amazon can effortlessly show things you need the most without even searching for them in the mobile application?

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If your answer is yes to any one of these questions, perhaps you already know the answer. They all relate to data analytics, machine learning, and artificial intelligence. The one answer not many of us know is how these are made possible and what exactly happens inside the code that makes them so smart and efficient. In a nutshell, it is all about data that gets collected in your smart devices right from what you search in Google to the places you have visited to the things you viewed on other apps to the conversations you have with your friends and families over texting applications like WhatsApp or skype. All of this data is taken, analyzed, and evaluated to get custom outputs which are then displayed on your mobile screens.

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Does that interest you in data analytics?

Does that reignite the curiosity in you?

If yes, you should continue reading further. In this article, I would be covering the bird’s eye view on the various types of problems in Machine Learning with a special focus on Python for Data science and Machine Learning.

Data Science vs. Machine Learning – The emerging trends in the digital space

Data Science is scaling up at an exponential rate in today’s digital world filled with data everywhere. Data Science is considered one of the niche skills to possess in order to sustain in this ever-changing world of IT space. However, the concept of data science is overrated and different people have different opinions on the same. According to Wikipedia, data science is defined as the following,

“Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data”

In short, one can say that data science encompasses all the processes employed in a data analytics projects right from extracting the data from various sources, merging the data, visualizing the data and extracting information from the data.

Machine Learning on the other hand, can be considered as a subset of data science with a focus on analyzing the data and solving a specific problem as determined by the stake holders in a Business environment. Computer experts however, tend to categorize machine learning as a set of computer algorithms that improves itself with time, i.e. to say the more the algorithm is subjected to data, the more the algorithm works to predict the output, the better it becomes with the passage of time. But to keep pace with understanding the basics of Python for Data science and Machine Learning, assuming that machine learning is a subset of data science will do the trick for us.

As per louis Columbus in the 2017 edition of the Forbes magazine, IBM predicts that a data scientist would earn an average of $1,14,000 a year, 59% of the jobs as a data scientist would be listed in niche sectors such as finance, insurance, IT and healthcare, there is a possibility of 28% rise in the job vacancies as a data scientist soaring from 3,64,000 to 2,72,0000 in 2020 which of course got hindered due to the pandemic outbreak. Nonetheless, it should be clear by now that investments in developing data expertise is something one can focus on in order to sustain and gain more opportunities in the field of data science and analytics and also to sustain the shift in focus from service oriented industry to data oriented industry.

What do I choose: R vs Python?

Python vs. R for Data Science: What's the Difference? - DataCamp

The programming languages R and Python are perhaps the most famous tools in the data industry and it is very natural to ask the question which one do I choose for my next data science project? The obvious next step that you might be tempted to follow is to do an internet research on which one is the best for a data science project. A word of caution here, the more you search on this topic the more confused you become simply because there is no right answer to this question.

Programming languages are usually multipurpose suited to perform variety of functions. The only way to answer the question as to which is the best for a particular data science project is to understand the problem statement and figuring out the type of data one has to work with in order to meet those business requirements.

Having said that, let’s have a quick look at both the programming languages one by one.

R for Data Science and Machine Learning

  • R is an open source, highly efficient programming tool relating to statistics and graphical methods.
  • R was typed in 1995 by Ross Ihaka and Robert Gentleman
  • R is used by Uber, Airbnb and Facebook in their applications
  • R can be used in data analysis pertaining to various researches in academia and comes bundled with a lot of packages that make data visualization (R packages such as ggvis, ggplot2, etc.) and getting statistical inferences (R packages such as dplyr, plyr, etc.) relatively simple and less time consuming
  • R as a standalone programming language already has several packages which can be used extensively for standard data analytics functionalities and visualization without installing additional packages unless necessary.
  • R is easy to integrate with local data sources such as the databases, excels, Power BI, etc.
  • Some of the disadvantages that I see in my working experience with R is as follows,
    1. The syntax of R is very unusual. For example, the assignment operator is <- whereas we see it as = in almost all the languages.
    2. The indexing in R starts with 1 whereas it starts with 0 generally for all other programming languages.
    3. One has to be very careful while typing the code since R returns unexpected types of objects when errors occur which makes it rather challenging while trying to debug.

Python for Data Science and Machine Learning

  • Python is a 4th generation high level programming language which makes it simple to learn. Code in python looks more like the general conversations we have in our daily lives.
  • Python is very easy to learn and understand even for professionals not belonging to the coding or mathematics background which makes it the go to programming language for beginners.
  • Python is used by Google, YouTube, Nasa and many other IT giants.
  • Python is a complete programming language which not only provides support for native data analytics algorithms but also has a rich library for web developments and software developments. Hence, it comes handy when one is looking for analytics embedded within a web page or a standalone application.
  • Python comes packaged with a rich community based packages for various data science algorithms.
  • The only disadvantage that I see in this programming language is the lack of equivalent packages from R language.

Now that we know on a very high level the advantages and disadvantages of programming languages such as R and Python, let’s take a closer look in what makes Python most suited for data science and machine learning?

What makes Python the number one choice in Data Science and Machine Learning?

In addition to the pointers mentioned in the last section, let’s take a closer look at some of the additional properties that makes Python the best suited alternative in the field of Data Science and Machine Learning.

  • Python is easy to learn and master: The first reason which makes beginners and even experts choose python for Data Science and Machine Learning is the ease with which it can be learnt and executed. Unlike many high level programming languages such as C, C++, Java or Basic, Python is perhaps the easiest to learn and master. An average professional can master Python programming language in a matter of hours which is not the case with other programing languages which on an average take months to master.
  • Python shortens the code length: The second reason which makes beginners and even experts choose python for Data Science and Machine Learning is the fact that code in Python is concise. Writing a function in Python that does the same work as that of a function in other programming language takes considerably less time, efforts and lines which makes python codes easier to debug and maintain.
  • Avalanche of Libraries and Packages: The third reason which makes beginners and even experts choose python for Data Science and Machine Learning is the tremendous support that Python receives from the large community of coders and their custom developed packages. Python is perhaps the only programming language which completely runs of communities across the globe. Almost all the libraries available in Python are handled by third party groups which makes the support base for Python extensively useful for programmers so that they can focus on their logic without having to worry for the underlying implementation.
  • Python is highly scalable: The fourth reason which makes beginners and even experts choose python for Data Science and Machine Learning is the scalability factor which makes it less of a headache for the developers. Python is highly scalable owing to the size of code written in this language. Many businesses use it to develop rapid custom applications as suited to their needs.
  • Python in visualization and graphics: The fifth reason which makes beginners and even experts choose python for Data Science and Machine Learning is the variety of visualizations it has to offer for data sets. Python is supported by a rich library of visualization tools such Matplotlib, Pytorch, Seaborn, etc. which makes data visualization very easy and less time-consuming.

Having understood the salient features which enables data science professionals choose Python for Data Science and Machine Learning, let us now get our hands dirty in the world of data science using python.

Python for Data Science and Machine Learning: Attention to Details

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Data Science is a broad term which encapsulates the below activities,

  • Data Exploration
  • Data Visualization
  • Machine Learning
  • Deep Learning
  • Data Storage and Big Data
  • Advanced-Data Analytics

Let us know visit each one of the above aspects individually to understand why one should choose Python for Data Science and Machine Learning.

  1. Data Exploration: The first domain in understanding Python for Data Science and Machine Learning is understanding Python for Data Exploration. Data Exploration combines all the activities needed to understand the data in form of visual elements such as graphs or tabular formats such as pivots or summary statistics such as the mean, median and mode of the data to name a few. Data exploration can be further used to establish relationships between various attributes in the data set, to determine whether the data is correct and complete and to determine the relevance of the data to the expected parameters which is to be optimized. Some of Python’s libraries well equipped to handle data exploration include the following,

  • Numpy: Numpy is a community maintained library in Python that contains a set of functions and methods to do numerical analysis on the data. This package uses ndimensional arrays for all its manipulations and contains useful functions for reshaping the data set, iterating through the data set, contains various filter options, contains algorithms for search, sort and split and also various functions for slicing the data.

  • Pandas: Pandas is a community maintained library in Python which deals in data manipulation and analysis in particular. The key concept of pandas is the idea of the Data Frame which can be indexed in multiple ways. In addition to the functionalities provided by Numpy, Pandas provide an additional functionality in the domain of time series manipulation of data such as moving window Linear Regression, moving window statistics and so on.

  • Scipy: Scipy is mainly developed for scientific and numerical computing. Scipy is built on Numpy and provides capabilities for performing advanced data exploration including optimization, linear algebra, Fourier Transforms, Fast Fourier Transform, signal processing and image processing.

  1. Data Visualization: The next domain in understanding Python for Data Science and Machine Learning is understanding Python for Data Visualization. Data Visualization in simple terms can be referred to as taking the raw data in form of numbers and converting them into colorful graphs so that various underlying relationships can be read directly. When the data size becomes huge and immensely complex, figuring out initial information from the data becomes almost impossible, it is at this junction that data visualization helps the data scientists a lot. This is done by converting the numbers or data values into graphic marks which are then mapped with one another to get a plot. Python comes equipped with many such libraries but the ones which are famous in the market are the following,

  • Matplotlib: Matplotlib is the plotting library of Python which focuses on converting the numerical data points in graphical visualization so that extracting information from raw data becomes easy. It is based on the OOP approach for embedding the graphs and visualizations within applications as and when the data is processed.
    • Seaborn: The Seaborn package is used for making statistical graphs in Python and uses Matplotlib and pandas in its implementation. This package enhances the functionalities of matplotlib by adding varying tools and mechanisms by which the plots and graphs are more readable, clear and informative.

  • Datashader: Datashader is the most recent plotting library of Python. Datashader aims at creating meaningful and informative graphs from large data sets very quickly. It involves a series of steps so that developers spend less time in the trial and error process figuring out the most appropriate plot for a dataset.

  1. Machine Learning: The next and perhaps the most popular domain in understanding Python for Data Science and Machine Learning is understanding Python for Machine Learning. In technical terms, Machine Learning is a set of computational algorithms which improves its performance with time. Machine Learning can be roughly categorized as the set of all such algorithms which are not related to deep learning which mainly consists of several layers of neurons to learn a specified task. Machine Learning can be categorized as Supervised Learning (Learning from data and feedback from the output), Unsupervised Learning (Learning from data without any feedback from the output) and Reinforced Learning (Learning by doing things). Machine Learning is widely used in many sectors today for performing various functionalities including healthcare, finance, banking and insurance. Python contains the below packages for Machine Learning,

  • Scikit-Learn: Scikit-Learn or sklearn is the free machine learning software package written in Python. Scikit-Learn is the most popular Machine Learning algorithm at present which comes equipped with various algorithms for Regression, Classification, Clustering, Naïve Bayes, Perceptrons, Support Vector Machines and many more such algorithms.

  • StatsModels: StatsModels is comparatively less popular with limited functionality for Machine Learning. It mainly deals in performing estimation of statistical models and performing of statistical tests on data.

  1. Deep Learning: Our next domain in understanding Python for Data Science and Machine Learning is understanding Python for Deep Learning. Deep Learning is an extension to machine learning with a focus on complex problem solving such handwriting recognition, image recognition, board games programs and social media filtration which cannot be addressed via machine learning algorithms. The core of deep learning lies in the extensive usage of neural networks such as deep neural networks, Convolutional neural networks, recurrent neural networks, etc. In short, deep learning networks tries to mimic the working of the human brain by adding a number of layers to the traditional neural networks in machine learning which learn by backpropagation. The following packages in python enables deep learning in python,

  • Keras: Keras is an open source neural network library in python which aims at fast experimenting with deep neural nets. It is also written in a way which enables it to be user friendly, modular and can easily be extended whenever there is a need to.

  • TensorFlow: Tensorflow is an open source, free deep learning library for Python. It was developed by Google’s Google Brain team for internal usage which was then made public in 2015. It is a math intensive library which aims at computation for dataflow and differentiable programming technique.

  1. Data Storage and Big Data: The next domain in understanding Python for Data Science and Machine Learning is understanding Python for data storage and big data. Big data can be referred to any dataset which is large enough and distributed too which cannot be processed unless there is a presence of a distributed parallel computing system. Python is easily interfaced with Apache technologies which deals in big data and below are some of the libraries for doing the same,

  • Apache Spark: Apache spark is an open source distributed parallel computing framework which supports cluster programming with inbuilt fault tolerance and parallel processing of data so that large amounts of data can be handled.

  • Apache Hadoop: Apache Hadoop provides a framework for storage and computation involving massive amounts of data. It provides support for processing big data using MapReduce programming techiques.

  • HDFS: HDFS or Hadoop Distributed file system is a file system used for the Hadoop framework. HDFS is a part of Apache Hadoop deals in the storage part of the technology. It provides Command Line interface for data retrieval and storage and also has inbuilt APIs for usage in application development and interfaces.

  • H5py/ pytables: pytables is a free software package that is used for managing hierarchical file systems and data sets. It is known for its speed of operations and the optimization it provides for data storage and disk resources.

  1. Advanced-Data Analytics: The next domain in understanding Python for Data Science and Machine Learning is understanding Python for advanced data analytics. Advanced data analytics encompasses all the analytics which are done to solve more complex real world problems. Some of the advanced data analytics include analytics problems like, image analytics, text analytics, sentiment analysis and natural language processing. Some of the packages that python provides for working in the domain of advanced data analytics are below,

  • nltk: nltk or Natural Language Toolkit is predominantly used in Natural Language Processing in a sub domain called symbolic natural language processing and statistical natural language processing. This package is mainly used for education and conducting research in the field of natural language processing.

  • spaCy: spaCy is an open source software library for advanced Natural Language Processing. On one side, where nltk is used for teaching purposes and for conducting research, spaCy is used for commercial purposes in many applications.

  • open CV/CV2: Open CV is a free open source package providing functionalities for real time computer vision developed by Intel.

  • scikit-image: Scikit-image is an open-source library for image processing in python. It uses SciPy and Numpy as its core and is mainly used for image processing techniques like feature extraction, segmentation, etc.

Putting it all together: Wrapping it up

In this article, we have understood the various aspects that need serious thoughts while selecting the proper data analytics model and the proper programming language which encompasses all the things that is needed to put things together. We went into a bit more detail and understood the advantages of R and Python and what makes them suited to perform various operations involved in a data analytics project. We went into the details in what makes experts choose Python for Data Science and Machine Learning. Towards the ends, we dived deep into the various packages that are available in Python for Data Science.

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