Category: Analytics

Machine learning is not a single science. It comprises a blend of fields such as analysis, recognition, prediction and decision making. There are several open-source tools available for machine learning out of which R and Python are the most demanded or rather the most popular ones. The main difference between the two languages has been seen in the fields of analysis and data science.

Both the languages provide open source tools and support from a wide variety of libraries for machine learning but because of the high degree of robustness provided by the python packages such as Scikit-learn built on numpy and Scipy, Python is preferred more for machine learning. According to a recent survey, Python had an increment in its popularity and use from 53% to 69% within two years.

Several machine learning courses aim at delivering courses dedicated to R and Python. The question as to whether an individual should learn both languages depends highly on the field of application and interest of an individual. Both languages have highly efficient ecosystems for machine learning tasks.

The difference in popularity and use is because of the comfort of an individual with the programming language, interest and application needs. Also, job opportunities can be one of the deciding factors whether an individual should learn Python or R for machine learning.

Provided below is a comparison of Python and R which could help an individual decide whether they need to learn both languages.

R:

R was developed by the statisticians primarily for analysis. The programming language is based on the mathematical calculations comprising machine learning and hence forms a really important part of the statistics involved in the project. Thus, a project which is largely dependent on statistics should use R as a programming language.

Advantages:

• Highly suitable for data analysis and visualization.
• Support from the libraries
• Highly robust
• Highly suited for exploratory work

Disadvantages:

• Scarcity of expertise in the language due to low learning rates.
• The algorithms in R comes primarily from the third parties and hence, it is not very consistent to build the models.

 Python:

Python came into existence in the ’80s. Today, it forms a core of the machine learning operations being performed by Google. It has extended its roots in the field of artificial intelligence as well and is being widely used in almost every possible domains whether technical or non-technical.

Advantages:

• In contrast to R which provides support for only statistics, machine learning has extended beyond just statistics.
• Python unlike R provides a smooth learning curve and is more consistent than R.
• Huge support from libraries such as numpy, pandas, OpenCV, sklearn, etc.
• Simplicity in the syntax making it easy to learn the language.
• Highly robust models and boosting techniques.

Disadvantages:

• Less support for statistical models due to the non-availability of suitable packages.
• Multithreading is Pyhton is not generally preferred as it is difficult to implement.

From the above comparison, it can be seen that both the languages having their advantages and disadvantages. But the key point that differentiates them is the use and library support. R and Python in machine learning have succeeded in their way. One has left footprints in the field of analytics while the other has emerged victorious in the field of data science.

Conclusion

To choose the right language, the right strategy is needed. For a person stepping into the industry as a fresher, Python is preferred as compared to R because of its simple syntax and ease of learning.

Also, if an individual is looking for a career in the field of data science they should go for Python as the programming language and if they want to handle the huge data-related tasks such as analysis and prediction making, no doubt that R is a better choice.

R is closely related to analysis and Python is closely tied to huge tasks such as object detection, disease prediction, computer vision and so on. Hence, we can conclude by saying that an individual needs to rightly assess their needs before choosing one of them and should master only one trade.

The days of rote learning are long gone. With the development of EdTech and similar technologies used across campuses all around the globe, learning now is more of an experience than a curriculum. As an organization that aims to future proof itself, it is time you adapt and embrace the modern technological interventions.

What Is Experiential Learning?
David A. Kolb, an American Education Theorist defines learning as the process whereby knowledge is created through the transformation of experience. The definition of experiential learning closely follows this.

Experiential learning is the process where candidates learn the course material through experience paired with other traditional forms of teaching.

How to Develop an Environment for Experiential Learning?

Now that you know the meaning of experiential learning, let us explore how as an organization you can develop and foster an environment that promotes the same.

1. Create Safe Learning Spaces

One of the first and most important criteria for developing experiential learning is to develop safe learning spaces, where students have the ability and the freedom to learn better.

An important aspect to understand here is the fact that safety doesn’t entail spaces which are risk-free. After all, students need to have some form of risk evaluation in order to learn. Safe learning spaces can thus be explained as spaces where experimentation is encouraged and failures are embraced without judgment. This will not only allow the students to be more creative, but it will also promote them to be bolder with their assessments, thus fostering a better learning experience.

2. Take Account of Logistical Challenges

While the development and promotion of experiential learning can sound easy to adapt at first, there are several hurdles that you need to consider. Mentioned below are some of the most significant aspects for to you keep in mind:

1. Survey the learning spaces you already have at your disposal and analyze if they will be able to adapt to safe environments to foster experiential learning. Not all learning spaces are built equal and thus you need to analyze and sit down with experts before you proceed further.
2. You need to make sure that all the enrolled students understand the values of the environment, both within and surrounding the learning space and come to respect the same. Without respect for the learning environment, the students will only learn half of the principles and values and thus this aspect needs to be taken care of.
3. If you are considering creating an environment off-campus, security is a concern you need to address. Conduct background checks on all the properties you are taking into consideration and assess with your experts which one suits your purpose the best while taking into account security measures.
4. One of the most difficult challenges of experiential learning is scheduling for all the involved stakeholders. Be it the students or the instructors, scheduling needs to be done months prior so that no other commitments or engagements are affected in the process. This calls for careful planning and effective communication both within and with all the stakeholders to ensure a smooth process for everyone.
5. Another concern for students and instructors is transportation. An approach followed by most companies is to hire an external contractor who will be responsible for all the transportation needs of the stakeholders. This way, there is no concern for parking and other logistical difficulties which might hinder the learning process.
6. Last but not the least, is the concern for privacy. In this world of data leaks and compromises in security, as an organization, you need to maintain and ensure the highest standard of security and privacy for all your students and instructors. Always make use of secure databases and restricted access, so that vital information is safe to round the clock.

Conclusion
Experiential learning is one of the latest trends in the education technology industry and companies who have adapted to it, have reported benefits over other conventional learning methodologies. Now that you know all about experiential learning, go ahead and discuss it with all the stakeholders and make an informed decision on its applicability in your organization.