It is quite interesting to note how well the data is being generated daily and how difficult it is becoming to manage the same. The demand for experienced data professionals has been at an all-time high recently, and the deep valley of talent is becoming steep. The industry is unable to churn out an effective number of data professionals with the rise of business numbers due to numerous reasons.
If you believe the latest numbers from the US Bureau of Labor Statistics, nearly 28% of the jobs would require data science skills by 2026. Although the supply is sparse due to the sheer newness of the data industry, speaking of the requisite skillsets, mastery at deploying data science tools is a must-have for any data enthusiast looking forward to a career in the field.
Your career goals may differ from other data science professionals, but when thinking of building a stronger base in the data science industry, core tools are one of the many requisites to sail through. Allow us to take you through two of the most popular and widely used data science tools in the worldwide data landscape.
Among the pool of best programming languages around the world, it is an uphill task to compare the two giants. There has been an ongoing virtual debate since the very inception of Python and R, two of the seemingly popular open-source languages that are comfortably compatible with Windows, MacOS, and Linux. They are great at handling data analysis tasks and are considered the easier languages out of the lot. So, what is the big deal about either side’s supremacy, if at all?
It is time to clear the air! Comparing the two leading programming titans is a task. Let us dig deep into each one of them:
Python
Python is a general-purpose high-level programming language that comes with an intuitive syntax that mimics natural language. You as a data science professional can use Python for a variety of tasks such as data analysis, web application development, automation, and scripting. It offers an easy conversion into a low-level language or machine code that is compatible with your computer recognition systems.
High points of Python
- Easy to understand
- Made for beginners
- Offers object-oriented programming
- Intuitive Syntax allows better team collaboration.
- Easy to comprehend syntax
- Offers the widest range of libraries
- Saves massive time
R
R is a popular statistical programming language that assists in facilitating computing and data visualization. It comes packed with statistical analysis, visualization of data, and data manipulation capabilities to make your tasks manageable.
High points of R
- Targets data reconfiguration and statistical analysis
- Offers a few packages to manipulate data.
- A bunch of tools for data analysis, visualization, and representation
- Open-source languages authorize continuous improvement.
Make a Good choice
Many data science certifications are targeted at leveraging the highest possible expertise in these data tools and skillsets, which allow you to reach your data career goal in no time. Although there is no right or wrong when it comes to making a choice, these quick tips will come in handy:
| Parameters | Python | R |
| Learning Curve | Smoother learning curve | Steeper learning curve |
| Your client | Pick the language that resonates with the company you plan to work with. | |
| Career trajectory | A perfect fit for a longer-term data scientist career goal, aiming at AI, deep learning, and big data | A better fit for statistical calculations and data visualizations |
Conclusion
As there are anticipations of Python’s ranking soaring by another 50% in 2023 and touching yet another record, Python is the choice. A data science profession is a choice and must be leveled based on your core capabilities and interest levels. On the basis of comparison, it can be deduced that Python wins the match. Yet, it is you, the data science certifications you enroll in, and your data career goals that will set the stage for a brighter future. Begin with the best credentials providers around the world to make a marked difference!
Machine learning Intern @Devfi || B.Sc Statistics graduate || C++ || R programming || IBM SPSS || Python || SQL || Machine Learning| ex-IBM
