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Which is easier, Python or data science?

Data science is a broad field combining many disciplines to solve complex business problems and make data-driven and better decisions for the future. This field includes machine learning, mathematical modeling, programming languages, and statistical methods for extracting meaningful insights from raw and unstructured data. Python is a flexible and free programming language that helps data scientists perform various tasks and operations efficiently. As Python is a beginner-friendly programming language, novice data scientists use it to make a successful career for themselves. However, learning Python programming language and data science requires persistence, hard work, and determination to succeed as a data scientist. If you are bothered by questions like which is easier, Python or data science, read this guide below.

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Is learning data science worth it?

With the advent of the Internet and technological developments across diverse industries, data science demands, and growth are rapidly taking place in the current economy. Students are driving toward data science courses to grab high-paying job opportunities. A lot of data is being extracted, sourced, and exchanged daily, which requires to be appropriately managed. This is why companies and organizations require qualified and certified data scientists who can organize data, and whose data is meaningful insights from it. The average annual salary of a data scientist in India varies between INR 11 LPA to INR 25 LPA depending on their experience, location, and company. Learning data science is worth it as it will help you learn the dream job and grab a lucrative salary.

Is Python easy to learn?

Many programming languages are used in data science. Data science aspirants need to choose a programming language that is easy to learn and use. Compared to data science, Python is easy as it is a beginner-friendly programming language with many uses in data science. Python programming has grabbed popularity because of its 

  • Simplicity
  • Versatility
  • Flexibility 

Apart from data science operations, this programming language is also used for machine learning and web development applications. Python is an essential toolbox for data scientists, which helps them to carry out repetitive tasks and data manipulation. Python helps to make the work of data analysts more rewarding and exciting. This programming language is not complex and comes with solid community support. 

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Is it difficult to get into data science?

The data science domain is incredibly difficult for professionals. This field is rapidly growing and encompasses a lot of concepts, systems, software, practices, and techniques to gain success. If you’re interested in making a career in the field of data science, then you need to get started by learning Python programming language and then gradually move to complex topics like machine learning, deep learning, NLP techniques, etcetera. Suppose you want to position yourself as a data scientist. In that case, enrolling in a data science course curriculum will benefit you as you can learn Python programming skills and other concepts of data science in a short duration. The best way to get into data science is to develop your technical skills in programming language and learn how to present and communicate your findings to stakeholders effectively. Acquiring technical skills will enable you to understand how data science operations occur and how to use data effectively to fulfill different purposes. You can make a promising career if you understand the two core areas of Python programming language and data science entirely and have a strong foundation in these two areas.

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Why is Python easy to learn?

Anyone can learn Python programming language because it is simple and easy to understand. Python, an open-source or free language, utilizes a community-based model for its development. Python programming language can run on different environments like Linux and Windows. It can also integrate easily into different platforms. Some of the popular open-source libraries for Python include the following

  • Data manipulation
  • Statistics
  • Data visualization
  • Natural language processing
  • Machine learning, 
  • Mathematics etc.

It comes with a relatively low and gradual learning curve. As this programming language comes with ease of learning abilities, it is a fantastic tool for beginner programmers or novice data science professionals. So if you want to learn something creative for your career, then learning Python will be an ideal choice. It is suitable for developers, programmers, and data science professionals to carry out their operations smoothly. Python provides programmers and data scientists with the advantage of using a few lines of code to fulfill their tasks. Therefore data science professionals and programmers need to spend less time dealing with coding to fulfill their job obligation.

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What makes learning data science difficult?

Data science is a vast interdisciplinary field that is tricky and challenging to learn. Data science’s core fundamentals and elements include statistics, computer science, and mathematics. The mathematical concepts include 

  • statistic theory
  • probability 
  • linear algebra.

Computer science concepts include software engineering and algorithms. Apart from this, data science aspirants also require proposals for domain knowledge and programming skills. 

Data science is a vast and interdisciplinary domain 

Data science encompasses various disciplines like 

  • Computer science.
  • Mathematics
  • Machine Learning
  • Statistics
  • Information Technology, Etc. 

Data scientists are required to encompass a broad understanding of all these fields as well as possess skills and programming languages like Python, mathematical skills, statistical skills, and knowledge about how to work with a vast amount of data and perform regression analysis. This makes the data science domain challenging to get into.

  • Data science is iterative.

Getting started on data science projects is challenging because professionals need to try things repeatedly to conclude. They must perform a thorough analysis and invest much time and effort to derive meaningful insights from it. In addition, the data science process has multiple solutions or interpretations.

  • Data Science requires collaboration.

Data scientists must work collaboratively daily with other professionals such as software engineers, data analysts, executives, managers, etcetera. This field requires collaboration as data is available not only in numeric form but also in the form of images, audio, and text. Data science professionals are required to understand raw, structured, and unstructured data to answer critical business problems.

  • Data science requires innovation and creativity.

Data Science Placement Success Story

Data science requires innovation and creativity for professionals to succeed in this field. Data scientists must be creative in their work to develop logical solutions for their business and enable the company to stay ahead of the competition. 

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