How Good Should One Be at Python to Learn Data Science?

No doubt, data generated every second across the world is increasing at an alarming rate. And there is no suspicion that this data will continue to increase as we are getting more and more involved in the digital world.

As long as there is data, there will be increasing demand for data workers across the world including data scientists, data analysts, data engineers, and more. We will discuss Data Science in this article and why professionals prefer Python for Data Science.

The arena of Data Science is still evolving and almost 46% of these jobs list Python as the top of the required skills.

Quick Pick:

137,630 is the number of job openings for Data Scientists that will be created in India by the year 2025.

Oh Wow!! You will have an added advantage if you know Python in and out.

Let us go through the overview of Data Science followed by the importance of Python for Data Science.

What is Data Science?

The field of study that particularly deals with enormous amounts of data using the latest tools and techniques usually to identify hidden patterns, and unseen information, in support of decision making, is referred to as Data Science.

Typically, various complex machine learning algorithms are used to develop predictive models that can be used for Data Science. The data science lifecycle comprises the following steps:

  • Data capturing include data acquisition, data entry, signal reception, and data extraction
  • Maintaining the data include data warehousing, cleaning, processing, and architecture
  • Processing of data includes data mining, modeling, clustering, and summarization
  • Analysis of data includes predictive analysis, confirmatory/exploratory, text mining, Regression, Text Mining, and Qualitative Analysis
  • Communicating includes data visualization, data reporting, decision making, and Business Intelligence processes

Now that you have an idea of what Data Science is, let’s look at the reasons why Python is essential for Data Science.

Python for Data Science

Python is an interpreted, open-source, high-level language that particularly offers an object-oriented approach to programming. It is the most popular language among data scientists across the world. Python provides excellent functionality that enables data scientists to deal with statistics, scientific functions, and mathematics.

Python is rendered incredibly versatile and productive because of its robust deep learning frameworks that are available with Python APIs as well as scientific packages. The evolving deep-learning frameworks in Python are consistently upgrading.

Machine Learning, which is an essence of Data Science, also uses Python for training machines. Python is used when it comes to sentiment analysis and natural language processing as it provides a massive collection of libraries that enables you to solve business issues properly and develop strong systems, and data applications.

Some of the important features of Python include:

  • Python is a flexible language that allows you to build websites and script applications that are never developed before
  • It is accessible that makes the program work easily
  • Python enjoys a low-learning curve as it is known for its readability and simplicity. For the same reasons, it is an ideal coding language for beginners. The best feature of Python is that you can write fewer lines of code to get the tasks done than in other languages.
  • Python enables you to work in interactive mode with which you can test your codes easily
  • You can easily extend the code written in Python by appending new modules that have already been compiled in other languages.
  • You can embed your code written in Python into applications that make it a programmable interface.
  • A code written in Python can be run on any platform that includes Windows, Unix, Linux, and Mac OS.
  • Python is open-source which implies that it is free to use. Also, it can be ported to various platforms easily.
  • Python has a massive following and is well-supported. It is extensively used in industrial and academic circles which implies that there are numerous analytics libraries available.

With an increasing number of Python users, more professionals share their experience and contribute their support which ultimately makes Python a largely supported language.

Some Popular Python Libraries for Data Science

Some of the Python libraries you should know well in order to work as a Data Scientist are:

  • NumPy: Numerical Python or NumPy provides you with mathematical functions to work with large-dimensional arrays, metrics, and linear algebra.
  • Pandas: Pandas is one of the most common Python libraries that is used for data manipulation and analysis. You can manipulate huge amounts of structured data with Pandas. This library is specifically designed to simplify the processes of data wrangling, data manipulation, visualization, and aggregation.
  • Scikit-Learn: it is a Python library meant for machine learning. It provides different algorithms and functions to be used in machine learning. It is built over SciPy, NumPy, and Matplotlib. Scikit-learn makes it easy to perform data mining as well as analysis. It helps you to implement important algorithms to solve real-world problems on large datasets.
  • Matplotlib: another important library designed for data visualization, Matplotlib provides you with different methods to visualize data effectively. It enables you to make pie charts, line graphs, histograms, and other features quickly and easily. You can customize any figure entirely with matplotlib. Some of the interactive features of Matplotlib include planning, saving, and zooming so that you can store your graph in graphics format.
  • SciPy: Scientific Python or SciPy is one of the most widely-used Python libraries in the field of Data Science. It provides excellent functionalities for scientific computing and mathematics. It contains sub-modules designed to perform operations on linear algebra, interpolation, integration, optimization, FFT, ODE Solvers, signal and image processing, Statmodel, and other popular tasks of data science and engineering.

Conclusion

The demand for data scientists as well as data analysts will grow by 1000% in the coming years. Python is one of the most valuable parts of a data scientist’s toolkit that is tailor-made for executing repetitive tasks.

To make or transition your career in the field of Data Science, you can take up an online training course that allows you to learn on your own schedule, provides training from industry experts, and gives you career guidance as well. Simplilearn has power-packed solutions for making a career in Data Science.

Read also: How Good Should One Be at Python to Learn Data Science?

Zaman Lashari
Zaman Lashari
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