So, you have heard of software frameworks, you have heard of Data Science. Do you really know what do they even mean (assuming that you do not have a coding background)? And both put together what new sense do they make? Well, let us delve into that briefly before we take a look at the most popular data science frameworks.

Framework in software terminology is a collection of individual software components available in code form and ready to run (what we call as libraries) that can be run independently or together to achieve a complicated task on any machine. The important part is ready to run, which means you do not have to put any effort in reinventing the wheel, it is already done for you, and you just have to learn to customize them to your liking to create your application-specific software, suiting your business needs.

Let’s pick a formal definition of the same. According to Wikipedia,” a software framework is an abstract or concrete framework under which software providing generic functionality can be selectively changed by additional user-written code, thus providing application-specific software”.

Now to the other part. What is Data Science? Data Science is a bunch of techniques that can be used to understand massive data sets without individually going through them. Understand what the whole data set is trying to convey or can convey about the current state of your business, the key drivers of your business and how are they impacted by the environment that your business runs in.

Here is a formal definition of Data Science from Wikipedia-“Data Science is an interdisciplinary field, that uses scientific methods, processes, algorithms to extract knowledge and insights from many structural and unstructured data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge, and information science”.

Put together you have a framework of software tools, that help you with executing data science techniques on your business data to get the best insights that drive your decisions.

There are a number of Data Science frameworks in the open market and otherwise. The open market enjoys extensive community support, but the proprietary software is more custom installed for your business with excellent support.

Let’s look at the most popular data science frameworks:

  1. TensorFlow
  2. Scikit-learn
  3. Keras
  4. Pandas
  5. Spark MLib
  6. PyTorch
  7. Matplotlib
  8. Numpy
  9. Seaborn
  10. Theano

1.    TensorFlow

TensorFlow is an end-to-end Machine Learning platform featuring comprehensive, flexible framework of tools and libraries along with community resources, helping you build Machine Learning powered applications easily. It also makes it easy to integrate data like inputting SQL tables, graphs, images all together. TensorFlow was first created by Google Brain Team and to this day remains open-source.

2.    Scikit-learn

Scikit-learn is an open-source Machine Learning library for use in Python programming language, featuring various classification, clustering and regression algorithms. It is designed to interoperate with numerical and scientific libraries like NumPy and SciPy, both developed and used in Python.

3.    Keras

Keras is a popular open-source software library that is capable of running atop other libraries like TensorFlow, Theano and CNTK. With a lot of data, you can dabble in Deep Learning and AI over this framework.

4.    Pandas

A data manipulation and analysis language written in python and for python offer data structures and operations for manipulating Numpy based tables and time series. It is used to normalize incomplete and messy data with features of shaping, slicing, dicing and merging datasets.

5.    Spark MLib

A library with an extensive support for Java, Scala, Python and R, this framework can be used on Hadoop, Apache Mesos, Kubernetes, over cloud services dealing with multiple data sources.

6.    PyTorch

A Facebook developed framework, PyTorch is an AI-specific framework for Deep Learning. The PyTorch library allows dynamic updates of graphs allowing on the fly changes to the architecture.

7.    Matplotlib

Based on MATLAB, Matplotlib is a plotting library for Python, with extensive support for rich visualization and dynamic charts. It is a numerical extension of the Numpy library to generate stunning graphs and plots. The default visualization library in every data science project in Python, Matplotlib helps you create interactive visualizations including histograms, 3Dplots, scatterplots, image plots, bar charts and many more

8.    Numpy

Numpy, an open-source library, brings in the computational power of C to Python, with powerful data structures for number-crunching applications like Quantum Computing, Statistical computing, signal processing, image processing, graphs and networks, astronomy processes, cognitive psychology and more.

9.    Seaborn

An open-source Python library, Seaborn is a visualization package based on Matplotlib. You get to work with high-level interfaces for producing rich and attractive statistical graphs.

10.    Theano

Similar to Numpy, Theano is for numerical computation and is best at manipulating and evaluating mathematical expressions. Theano ensures that computations are expressed efficiently on either CPU or GPU architectures.


A Data Science framework helps your data science staff to focus on business problems than getting entangled in the coding business. Get the best out of data science and scientists with a data science framework.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 



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