By Vamsidhar Ambatipudi, Associate Professor, Group Leader –Management, BITS Pilani Work Integrated Learning Programmes (WILP) division
In 2006, a data scientist, Clive Humby, was the first to use the phrase, “Data is the new oil.” In the last two decades, data seems to have become the new gold and the new oxygen as well. Businesses throughout the world are betting their future on their ability to sift through mountains of data and extract relevant knowledge.
Experts in the art and science of mining actionable insights from this data Manthan are in high demand in the industry. In response to this increasing demand, many prestigious and leading universities have begun offering master’s degrees in data science and business analytics to students and working professionals at various levels. While there are a number of user-friendly licensed data analytics tools, many of them may be quite expensive and do not address all of the necessary functionalities. In contrast, open-source programming-based tools like R and Python are becoming beloved tools worldwide for analytics and are expanding their market share at rocket speed. However, many working professionals and MBA students still seem to have some phobia of learning these technologies.
To begin with, why learn R and Python?
As mentioned earlier, both these languages are open-source. They can be used for developing sophisticated business applications. The sky is the limit for the number of functionalities that can be built into them. A basic understanding of the coding framework makes you comfortable, using all these functionalities in no time. New functionalities can also be contributed by people like us to R and python repositories, by creating different libraries. Functionalities developed by thousands of researchers across the world can be used for free.
For example, a marketer can perform very advanced and sophisticated sales forecasting, product-mix optimisation, customer segmentation, brand positioning, churn modeling, campaign analysis, and various other functions with just a few lines of code by using many existing libraries. A finance professional can forecast market prices, evaluate various trading strategies, assess the risk of investments, etc. by reusing the functionalities available in the public domain. What’s more, there are hundreds of free-to-use libraries available for each and every professional working in any domain.
Stages and steps through which one may overcome the phobia of learning R and Python
Knowledge of how to learn should come before attempting to learn anything new. Needless to say, an appropriate frame of mind and strategy are critical for successful learning as well.
Having taught analytics subjects for a long period time of time to many experienced
professionals (who were novices in programming), I wish to share some of my key insights,
which may be helpful for especially those who have such a phobia of learning programming tools, such as R and Python.
Stage 1: Foundation Stage — Getting the basics right
Set up the environment: This is the first step. There are numerous tutorial videos on internet that explain the process of installing R and Python environments on your computer. Both these programming languages are very easy to install. In some cases, you can avoid installing them on your computer and you can even work on the cloud environments like Google Colab as well.
One line of code at a time: Unlike compiled programming languages, such as C, C++, and Java, analytics programming tools like R and Python are interpreter-based languages. You can write one line of code and execute it to check the output, correct the error, and then recheck it. You can write simple one-liner arithmetic and logical functions, execute them, and taste the first success of your coding skills.
A simple tutorial on the internet can give the basic one-liner codes in any of these programming languages. Acquire knowledge and experience little by little and reward yourself for each milestone. Remember that even the most enduring learnings in our life, such as learning to eat and walk, started with small and basic steps as well.
Beginner-friendly resources: Look for tutorials, online courses, or books aimed at beginners that give easy-to-understand explanations and examples. There are some interactive tutorials as well. If you have enrolled in any work-integrated learning programs, you can also request an instructor to share the beginner coding tutorials in R and Python. These materials aim to ease you into programming by progressively introducing key ideas.
No substitute to practice: All of us have experienced many times in our lives that any learning without continuous practice is always short-lived. If you have decided to make a career in analytics, you have to take your time out frequently to practice the one-liners and simple projects that you initiated. The objective of this exercise is to ensure that the basic framework of programming does not go away from your mind. The more you practice it, the better you’ll become at it, just like any other talent. It is through repetition that muscle memory is formed and knowledge is solidified.
Learn from your mistakes
It’s important to keep in mind that making errors is an inevitable part of the learning process. Don’t let the errors deter your determination. Think of them as chances to grow and learn. Examine the problems, look for answers, and hone your ability to solve them over time. The more mistakes you make, the closer you may get to mastery. It takes dedication and patience to master programming. Recognise that this is a continuous process and that your success may likely occur in stages.
Stage 2: Building on the foundations
Read and understand existing projects
Once you are comfortable with the basic framework and functionalities of a programming
language, pick up any existing project in your domain of interest from online resources like
GitHub or Kaggle. They are the hub for thousands of solved projects. Copy the entire code into your programming environment. Try to run the code line by line, observe the output at each stage, and understand the functionality that is being executed by each line of code.
Change the dataset and execute similar functionalities, again and again. This approach will reinforce your learning of the needed functionality. Run a few projects of your interest in a similar fashion. You will be able to get a hold of a good number of functionalities in your domain. Working on real-world projects helps you retain information and shows what you can do with your newfound programming knowledge.
Hone your Googling skills:
Yes, this is a very essential element in taking your coding skills to the next level. Many times, you will encounter various errors, while running the existing codes as mentioned above. Some of these errors are very common, such as the unavailability of a library or version mismatch. Search for these errors on Google. The online support community in the form of “Stack overflow” among many others, to clarify your errors and programming queries is not just vast, but also quick.
Refer to the documentation of different libraries
A library in R or Python is a set of functionalities developed by some researchers and are shared as an open source. Many of these libraries have robust documentation as well. When you come across one functionality while reading the existing projects as mentioned above, explore the library that it belongs to and get the documentation for that functionality. The documentation covers the syntax and the description of different parameters, along with a few examples on how to use the functionality. Explore the other functionalities present in that library. Explore one by one by copying from the example and build your knowledge on the functionalities in an incremental manner.
Join coding forums on LinkedIn
Participate in online forums, social media groups, or in-person gatherings, to network with other beginners and experts. Having a group of people to turn to for advice and encouragement, as well as somewhere to talk about your successes, is invaluable. You’ll see that plenty of people have been where you are, and they have lessons to share. Always share your work with experts and get their advice.
To conclude, one can certainly overcome their phobia of programming and become proficient in learning coding-based analytics software with a combination of positive outlook and regular practice, and by taking a constructive and methodical approach towards continuous learning. Needless to say, enjoy the journey, be patient, and don’t hesitate to ask for assistance.
