Hi! I’m Rahul Raoniar a PhD Student in Transportation System Engineering (IIT Guwahati, India). I am a crazy leaner, whenever a new tech launches it drives my brain to learn those techs and related skills. I know it felt like I’m a nerd but yes it is true. During my graduation and M.Tech, I did not have that learning attitude. Why? because I was lazy. I’m still lazy. But what changed now?
I came out of my lazy shell after joining PhD in IIT Guwahati. Yes, the environment pushed me to come out of my shell and learn the latest skills so that I can lead my PhD projects and perform my data analysis fast. The two platforms that really helped me learn the latest technologies and skills are Coursera.org and DataCamp.com. Coursera is a platform where you could learn any stuff from top universities. DataCamp is, on the other hand, an exclusive data science learning platform.
In this blog, I will list some courses that I feel every student should start taking whenever they are free or motivated. Online courses have one advantage that you can allocate your precious time whenever you want. Online learning platforms have a very limited restriction on time management. My mantra is:
Whenever you are free and motivated learn something new
Let’s start with the courses.
Before you begin your first course it would be great if start with the learning mechanisms so that you would get the maximum benefit from new courses. So my first recommendation is to start with “Learning How to Learn” offered in collaboration with McMaster University and the University of California San Diego.
During your graduation/masters or PhD, there would be some moments when you will feel the work pressure which eventually makes you feel stressed. In academics, work stress is very common. So, to deal with this type of situation you need to prepare yourself. The course “The Science of Well-Being” offered by Yale University, would help you to deal with such situations and teach you how to be happy even if in difficult situations. After taking this course you will be a warrior and definitely learn to love your life.
Now, you are a warrior and you are prepared to enter the inquisitive world of academics. The 20th century is the era of tech. To walk parallel to this tech-savvy world you should familiar with at least one programming language. My recommendation to learn a language that is open source and easy to use. This will help you in your academic learning and projects.
It is not necessary that you have to be a science student. Any student from any field should learn and leverage the power of programming language. My first recommendation will be Python or R, whichever you feel easy and comfortable to work with. Though the choice depends on your final aim. For example, a student who wants to design beautiful websites should obviously go for HTML and CSS.
“Learning a programming language gives you wing to fly”
If you felt that python is your preferred choice then go for the introductory course “Python for Everybody” offered by the University of Michigan. This course gives you the test of python and helps you to establish a foothold in the programming world.
If you feel that R is the best suited for your academic work. Then you should proceed with “Data Science Specialization” offered by John Hopkins. This course guides you through R programming to data science journey.
Now you are prepared with study/research tools. The next step is to formulate your study outline. For research students, it is essential to prepare a well-formatted research proposal and walk through it. This includes a review of literature, formulating a hypothesis, prepare a data collection work plan, analyze the data and comes up with some useful insights/outputs.
From my experience, the process is not that straight forward. Thus, to get familiar with the process, I will recommend you to go for “Methods and Statistics in Social Sciences Specialization” offered by the University of Amsterdam.
Once you have the idea of your research outline. The next very important stage is to prepare a data collection work plan. The data collection could involve field data plus Survey data. Preparing a data collection plan and designing a well-formatted questionnaire could be a very hard challenge. From my experience, I could say that data collection is the most difficult part of the research work. Thank God there is course exist to guide you through this process. Go for the “Survey Data Collection and Analytics Specialization” offered by the University of Maryland, College Park and the University of Michigan. This six courses specialization will help you learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data.
Once you prepare with your data collection strategy then the next step will be to perform statistical analysis on the collected data. For that, you need to learn basic to advanced statistics.
The specialization of “Statistics with R Specialization” by Duke university will lay the foundation for basic to advanced statistics. This course help you learn, analyze and visualize data in R and help you create reproducible data analysis reports, make you understand the unified nature of statistical inference, and Bayesian statistical inference and modelling.
One can take another similar course with practical examples related to public health research. The “Statistical Analysis with R for Public Health Specialization” teaches you statistics with practical examples from Public Health research.
Once you get the clear cut idea of basics statistics the next part will be to learn predictive modelling. Predictive modelling using machine learning techniques is the trending topic of the 21st century.
ML will give you power of harry Potter’s Wand
The course “Machine Learning Specialization” offered by Washinton University is one of the hidden gems. This will make you familiar with supervised and unsupervised ML algorithms. The course is full of practical examples and applications. The course tutors are awesome too. They make your understanding better by explaining hard concepts with simple but effective examples and with humour.
If you want to expand your learning horizon in the field of Advanced ML algorithms then go for the “Deep Learning Course” by Andrew Ng.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow.
Once you have concrete knowledge of deep neural network’s functioning the next step is to put your knowledge into action. The “TensorFlow in Practice Specialization” help you learn real-world application of Deep Neural Network using TensorFlow library.
The AI application in the medical field is nowadays trending. If you want to learn AI application in medical research then this is the best course for you. This specialization helps you learn disease diagnosis and patients future health prediction with practical medical datasets.
I know after reading this article you might be wondering with so many questions. You might be thinking about how would I finish that many courses. I know it is not one month task. Learning something new needs dedication and time. Don’t think about the whole courses, just start with one, learn the new stuff, complete it. Allocate a special day and time for new learning. Weekend (Saturday) could be a great option. Once you finish a course then proceed for the next one.
I will leave you with this quote
“No human ever became interesting by not failing. The more you fail and recover and improve, the better you are as a person. Ever meet someone who’s always had everything work out for them with zero struggle? They usually have the depth of a puddle. Or they don’t exist.” — Chris Hardwick