August 10, 2022


From simply collecting the data to driving key business decisions based on the insights - data science has changed a lot in its meaning. Also, when it comes to data science and analysis, the two widely discussed terms are automation and machine learning. Data scientists carry out analysis with the help of automated machine learning and deep learning models to simplify complex processes, calculations, and functions.

Python is a high-level programming language, rich in libraries that assist data scientists in carrying out even the most complex data analysis for accurate results. While there is an array of programming languages that one can choose from, Python, with its simplicity and efficiency, remains the first choice of data scientists.

Python In Data Sciences

Python has an extensive set of libraries with machine learning capabilities that specialise in data collection, analysis, visualisation, and real-time update and sharing of information. These libraries are single handedly efficient, eliminating the need for Python developers to code from scratch, freeing up a significant chunk of their time to work on the ideas and make solutions even better. This makes Python the best choice for developers as well.

Python libraries and frameworks are used in the combination of automation and machine learning together, known as AutoML (Automated Machine Learning).

How Is Python Used In Automation And Machine Learning?

Automation and machine learning are two different aspects of working with algorithms and Artificial Intelligence. While automation replaces human efforts in accomplishing a task, machine learning allows computers or any other device to take inputs from humans and perform accordingly.

Talking specifically about data science and analysis, Python is an acclaimed programming language used to achieve the goals of automating several processes or facilitating machine learning and deep learning capability with regard to data collection.

For business analytical applications, Python helps in creating solutions that assist in preserving data in a set format, supports automation, and helps businesses and data analysts equally to conduct predictive analysis of the data. For data-centric businesses of today, AutoML is their true asset.

What Is The Need To Automate Exploratory Data Analysis?

Exploratory data analysis is the technique of data exploration to understand several aspects of the data. A crucial step before conducting any machine learning or deep learning task.

Data scientists carry out exploratory analysis to explore, identify potential loopholes and sum up the fundamental capabilities of the data. Exploratory data analysis is persistent on information sources to ensure appropriate responses in a set format. This helps data scientists to find information patterns, spot loopholes/dead ends, and test speculations.

Data is invited from several sources - from the web, smartphones, web-empowered gadgets, and IoT sensors. Although the flexibility of data collection sources has made consumers give instant responses, this has given rise to a large data set. But the explosion of data is not enough until there is a way to scrutinise the entire for proficiency and profitability.

Time Composition

As a matter of fact, more than 60% of the time allotted to data science projects goes into data collection, data analysis, feature selection, feature engineering, etc. As ‘data’ is a crucial part of the entire workflow. Because data is valuable - more importantly, sorted data is valuable, there are a lot of activities like cleaning the data, handling missing grids, handling outliers, and more - as important as they are tedious.

Several Python-based libraries like Pandas, NumPy, Keras, TensorFlow, AutoViz, etc., if integrated into the project, can automate the tasks to exponentially save time and effort.

What Makes Python An Exceptional Choice For Data Analysis?

It is near impossible to find a perfect match as Python for Data Sciences. One single language that helps in automating the tasks as well as helps with descriptive, predictive, and perspective analysis.

Here Is Why Data Scientists Love Python:

  • Versatile usage - Python helps with data mining, data processing/modelling, and visualisation - the three most important aspects of data analysis.
  • Active Community - Python is supported by an active community of developers and designers edging towards perfection. The community resolves issues and offers the most comprehensive platform for any business solution.
  • Agility - Python is the way of rapid product development. Due to its agility, the programming language is used to make any scalable product - including business analytics and data science projects.
  • Extensive Libraries And Full-Stack Frameworks - Python has several libraries, each highly capable of accomplishing complex tasks and adding value to the project. The libraries are open source which significantly helps in lowering the cost estimations.

The Only Challenge With Python

Python is a general-purpose language and not dedicated solely to data analysis. Python development companies must hold the expertise to make the best data science solutions paving the way for success through data.


Python is the best choice for data science and business analytical tools. Leveraging AI capabilities like automation and machine learning can make the solutions more advanced and quick. Data scientists love Python because of its immense capabilities and the value it brings to the table.

Explore the immense potential of Python in data science, business, and analytical tools with guidance from Certified Python Experts at Dotsquares. Let’s talk about your requirements.

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