In today’s world, Data is the hottest topic. Any company which can decipher data insights would be able to solve its business problems and become successful. Because of this, the scope of all the data-related technologies such as Big data, Data Science, Data Analytics, and etc, has dramatically increased. For diving into Data Science, it’s critical to grasp its concepts, tools, and technologies. Mainly, it’s essential that you must learn R, Python, and Hadoop. However, this Python for Data Science course can help.
Table of Contents
1. Easy and Simple
R, Python, and Hadoop are the technologies that can be learned easily. You can effectively grasp these technologies with some focus and perseverance. These are the latest technologies that require a minimum training period. Moreover, We can also learn these by ourselves.
2. Free of cost
It is an important thing you need to know that these technologies are free. It’s undeniable that these technologies are very helpful in the data-driven world. So, learning these free and open source technologies will yield you many opportunities in this career.
3. Accessibility
Unlike other technologies, these three are easily accessible. The installation, modification, maintenance, and updating of these technologies are very simple, it can be done within a few clicks. Readability of Python, easy statistical software development of R, Unique features of Hadoop are all specially designed to make these technologies simple and easily approachable.
4. Platform Independent
All these technologies are platform-independent. This means that programming languages may be utilized on a variety of systems, including Windows, Linux, and others, enabling users to perform their tasks on multiple systems. R and Python programmers are already devising new techniques to cope with bigger data sources across a wider range of platforms, the same as Hadoop.
5. High Scope in the Market
These are trending technologies. Within a few years, the usage and the market standards of these technologies have expanded like never before and sure it will increase furthermore in coming years. Learning and mastering these technologies will be a shortcut towards your career in Data Science or any related field.
6. High-performance
A further fantastic bit of technology you should learn is Hadoop. It offers improved performance and the capacity to store, ability to organize and analyze massive quantities of information. Learn how to use R, Python, and Hadoop to do big data analysis, predictive modeling, and Data Science. These technologies make complex simulations easy. Check out this Hadoop Tutorial to understand better.
7. Community
All these technologies have worldwide communities that connect people from various backgrounds to share their programming experiences or problems. People solve each other’s programming or technical problems in these communities. The latest Anaconda parcel has already received over 300 parcels and has received great feedback from people all across the world in their forum, encouraging them to continue with future shipments.
8. Easy Debugging
With these technologies, we can debug easily and fix problems faster. This is because we have a source code debugger named PDB module for Python programs, debug() function enables users to go through the execution of the R code line by line, and debug scripts of Hadoop provide an efficient mechanism.
9. Statistical Advancements
Because these three languages are the most developed and versatile, every new software development or update invariably must use one of them. Python reads the information considerably faster, R has inbuilt statistical software packages and the Hadoop synchronization is a nice benefit.
10. Always Evolving
Python, R, and Hadoop are evolving technologies. They are the most popular programming languages because they constantly upgrade themselves to offer the best user experience for customers. Additionally, these technologies are easy to upgrade at any time.
11. Applicable in Data Science
For anyone who wants to enter Data Science or any other related field, these technologies are mandatory. They make Data Science applications easy. Python has various libraries that are useful in Data Science, R is helpful in statistics analysis, Hadoop is used to store complex data. In any Data Science concepts or phases, these technologies are required.
12. Job Opportunities
Any business contains an ever data stack that is expanding by the minute. Because of which they recruit trained specialists to manage and preserve their data. A professional in these latest technologies will definitely have plenty of career opportunities to pursue their career. So step up and start learning.
13. Multiple Career Options
This is another crucial reason to learn R, Python, and Hadoop. There are multiple career options available for professionals in these top technologies.
- Positions that are available for experts in Python are Software Engineer, Python Developer, Research Analyst, Data Analyst, Data Scientist, Software Developer.
- Data Scientist, Business Analyst, Data Visualization Expert, Quantitative Analyst are the jobs for R programming experts.
- Professionals in Hadoop have career options such as Hadoop / Big Data Developer, Hadoop Administrator, Data Engineer, Big Data Architect, Big Data Consultant.
14. Lucrative Salaries
These technologies not just open up endless options, but also offer a fantastic salary package. Experts in these technologies can expect lucrative salaries.
In India, the national average pay for a Python Developer is ₹4,50,000 per year, whereas the national average compensation for a Hadoop Developer is ₹5,99,918 per year, according to Glassdoor.com. The average income for a Data Analyst with R skills in India is ₹510211, according to payscale.com.
15. Career Growth
Any professional in these technologies will have an assured career growth because these are the leading and most in-demand programming languages and technologies. Moreover, these technologies are crucial for working with data. As a result, the scope of this career will increase furthermore in the future till the data is in existence.