Do You Need to Learn Python for Data Science?

23-Apr-2021

It is a well-established fact that Python is a very significant programming language. It is being used both for getting a theoretical and practical insight into the Data Science course.

As a popular coding language, Python is widely known for its simple and ready-to-use Syntax, easy comprehension, and many readily accessible libraries that make the learning and development of various technical and digital software easy. 

When you opt to learn Python with Data Science course, you cardinally know that Python is an uncomplicated language that has a simple syntax, and it will be easier for you to develop and design products using Python.

Not just this, it comes with a comprehensive statistical model which helps organizations and developers create, develop, and evolve more efficiently and effectively in a higher fast-paced technological environment! Yes, Python already has us glued! Let’s know more about how significant Python is. 

Is It Vital to Learn Python for Data Science?

It is very much crucial to brush up on Python for pursuing a certification or a degree in Data Science. Notably, a Data Science certification or a right accredited degree from a reputed educational institution is a must for any learner/student who is looking to become a Data Scientist.

In the next 5 years, the future of computer science will be completely dominated by Python as the best accessible and understandable language. Hence, it is vital and essential to learn python for Data Science.

Also, it will showcase its relevance for Quantum computing, and help tame and authorize AI and ML tools in the 21st century. 

Once, a learner has understood the importance of Python for his growing and expanding career in a technological and scientifically advancing world, s/he can:

• Warm-up his fundamentals/basics in Python.

• Brush up on NumPy, SciPy, Pandas, and Matplotlib. These are the scientific libraries that Python has.

• Seep into Data Visualization, Data modeling, and Data mining techniques and tools.

• Start using Python programming for automating reports and expansive web development. 

What is the Best Way to Learn Python for Data Science?

The best way to learn Python is by enrolling yourself in a Data Science course! After enrolling, the mentors will gradually guide you about the Python language.

At Careerera, the dedicated faculty teaches and helps you gain the right insight and gives you proper training. As an educational platform, Careerera offers the best possible learning experience. Indeed, at Careerera we boast of a world-class faculty that helps you achieve the best.

To learn this popular coding language, a learner must have a track to follow. The following points will give you a great insight into the learnings from a Data Science course that uses Python - 

• Working on advanced and essential topics and skills in Python coding.

• Taking practical assistance and knowledge and implementing it in daily, routine projects.

• Practicing coding and programming.

• Learn on widely used statistical models and mathematical automation.

Which Version of Python is best for Data Science?

Ideally, today, most developers and Data Scientists use Python 2 for developing and creating automated software or an artificial machine. However, many scientists and scholars believe that the future of Data Science is dominated by Python 3.

The companies will soon switch to Python 3 for more advanced and modern work. We believe that in a changing demand and supply setup, updating modern skills is a must!

Agree? It’s time that you supply what the organizations and interviewers demand from you! The students must get upskilled to Python 3 so that they do not lose sight and control over what the new world and novel nature of technology demands!

Fact: Big companies like Facebook and Instagram have already switched to the newer versions that come with faster runtimes! Data Scientists at Facebook use Python 3.4 version. Similarly, developers at Instagram use Python 3 for a better coding and programming experience.

Where Can I Learn Python for Data Science?

Many Python courses are available online. You can enroll yourself with the best in the industry, Careerera itself!

After learning the basics, students can enroll themselves in these highlighted and popular in-demand programs and courses -  Python for Data Science, PGP in Data Science, Data Science Professional Certification, DSF - Data Science Fundamental, DSAP - Data Science Analytics Professional, and many more. 

Choose Careerera, and be the leader in Python with Data Science. It’s time that we enlighten minds with the right knowledge and skill-set!

👉 Check our PGP in Data Science course online

How long does it take to learn Python for data science?

Data science enthusiasts and data science often are stuck with the question - How long does it take to learn Python for data science?

While there is no fixed formulated time frame, the duration of learning Python for data science depends entirely upon the individual. 

There are numerous estimates regarding the length of time it takes to learn Python. In the case of data science, experts believe that three months to a year of constant practice is required. It all relies on your intended timeframe, the amount of free time you have available to learn Python programming, and your learning pace.

It can take anywhere from five to 10 weeks to learn Python programming essentials, such as object-oriented programming, basic Python syntax, data types, loops, variables, and functions. Finally, your prior expertise with programming languages, web development, data science, and other relevant subjects determine how long it takes to learn Python.

Institutes like Careerera offers online data science courses with Python or Machine Learning with data science courses ideal for both beginners and already working professionals. Aspiring individuals can get access to the basic and straightforward methods of Python. The courses are usually 30 to 34 hours, which are entirely self-paced and can be scheduled according to convenience.

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