How to Learn Data Science Smartly in 2023

Data science is considered the fastest-growing field in current times. Many professionals & students are currently interested in transitioning to this domain. However, learning and moving into a new profession is challenging. It requires structured steps and a solid plan to efficiently crack the domain. So, here we have presented a detailed roadmap that will help you accomplish your goal.

Step 1: Start with any spreadsheet tool like excel or google sheets. Carry out data manipulation, draw graphs and try to find insights from any dataset of your choice.

Step 2: Move to any programming language, be it R or Python. The task is to perform the same analysis in R or Python that you did in the spreadsheet tool. You'll come across some libraries like dplyr, ggplot2, etc. in R and Numpy, Pandas, etc. in python. These libraries will help you in data analysis. 

To understand and master these libraries for data analysis, look over the internet, and you'll find a lot of tutorials for the same. Pick any one or at maximum two resources and start learning & implementing. In this process, you'll learn programming language as well as data analysis.

The best way to utilize the maximum from the above two steps is to always ask a lot of questions from the data. Then try to discover the answers with the help of excel, R and Python. This way, you'll not only learn the tool but would develop analytical thinking too.

Step 3: Now start studying statistics. Topics like conditional probability and Bayes theorem should be focused on. Then move to probability distribution, hypothesis testing, and statistical tests. The trick to master statistics is, first try to grasp basic ideas of multiple topics and then start solving the numerical problems. Then implement the learnings like probability distribution, hypothesis testing, and statistical tests in Excel and any programming language.

Congratulations! you have completed 50% of the journey and you are ahead of most of the beginner aspiring data scientists.

Step 4: Now comes the machine learning part. You’ll come across jargons like supervised and unsupervised learning, EDA, data preprocessing, and so on. But, don't get disheartened so easily. Start exploring why there is a such classification of topics, and what steps should be followed. Initially, don't try to understand everything, try to get an idea of the bare essentials. There are plenty of ideal resources out there for machine learning. Stick to a few of them.

Reaching this stage will roughly take anywhere between 3-4 months to 1 year. Now you are ready to work on quality and end-to-end projects. You can apply for internships or even jobs. If you want to study further, you can pursue higher studies in a good institute for data science.

Never get too hung up on completing the topics. Try to understand the why, when, and how of everything you are learning. The reason is, if you'll try to finish things in a short timeline, sooner or later you'll face issues in understanding the fundamentals of topics and you'll feel the need to revisit the topics. Hence, learn slowly but consistently.

It is said that "Little strokes fell great oaks"

All the best for your journey.