Kuldip Bawa

I am also in the process of transforming myself into a data science professional, and each step that I have taken towards this goal has been difficult and, at the same time, very rewarding. As the world gets more data-oriented every day, I was interested in finding out how companies can make wiser decisions, how the trends are anticipated, and how we can derive insights out of the raw information, and that directed me towards data science. I became interested out of curiosity. I used to always wonder: how does the recommendation system work, how do apps know in advance what I would do, or how do businesses know in advance what I would need? That interest was transformed into action as I signed up for my first course that teaches Python, statistics, and the basics of data analysis. I have been slowly gaining my foundation since that time. Currently, I am being introduced to some important concepts such as data cleaning, exploratory data analysis (EDA), statistical modelling, machine learning algorithms, and data visualisation. I use such tools as Python (Pandas, NumPy, Matplotlib, Seaborn), SQL, libraries Scikit-learn and TensorFlow. I am also working on Jupyter Notebooks to exercise full-stack data projects, data importation, to simple predictive modelling. Real-world problem solving is one of the most thrilling aspects of learning data science. Be it housing prices prediction, customer churn analysis, or handwritten digits classification- every project seems to be an occasion to use theory in real, useful tasks. I am also doing a petite portfolio assignment to demonstrate my advancement and practical abilities. Another thing that I have learned during my learning journey in data science is the value of critical thinking and patience. Data is not always clean, and the first time models are not always good. But that is where the learning occurs, in iteration, experimentation, and posing superior questions. The technical skills are not the only area where I am improving my knowledge, though. I am also starting to get a good sense of how data relates to business goals. That is not to say that one should simply build a model; it is about ensuring that the model is built to address the correct question and is valuable. I also make sure not to fall behind by following data science communities, reading case studies, and attending challenges on Kaggle. I also have understood that the education in this sphere is constant: it is not necessary to know how to use a certain tool perfectly, but to remain flexible and eager to learn when the technological environment changes. Later, I want to become a specialist in such directions as machine learning or NLP (natural language processing), or even responsible data use and AI ethics. I want to be able to use data to make decisions that actually matter, whether I am in the healthcare industry, the finance industry, or the tech industry. In the meantime, I am engaging in learning, practising, and developing as a data science enthusiast. Each dataset that I analyse and each model that I optimise is bringing me one step closer to making my passion my profession.