- The key skills she uses everyday
- The challenges she faces and how she tackles them
- Advice on courses and for potential Data Scientists
Nisha: How did you first get into data science?
Kajal: Two years back, I got to hear from my friends how trend is changing towards Data Science and it is in hype. This is when I completed my graduation from University of Delhi in Computer Science and decided to pursue my post graduation in Data Science from Central University of Rajasthan. The curriculum defined for data science course in this university was indeed good. It helped me to make my fundamentals strong. So, It wasn’t that difficult for me to understand other online resources and practical implementations.
Nisha: What are the key skills that you use every day as a data scientist, and how did you develop them?
Kajal: One programming language is a must. So most of the time, I used Python. Statistics and mathematics are the basics that one should understand. Data Extraction, Data Exploration, Data Cleaning, Visualization to better understand the business problem, Data modeling, Fine Tuning the parameters in order to get good accuracy. These are the few most required skills in every data science project. I developed these over time by doing complete end-to-end projects. Other than these, one may get to work on SQL & databases and big data frameworks like Hadoop.
Nisha: What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
Kajal: I think starting stages are really time taking and difficult. Once, we get the right data, it is not that difficult to apply the modeling part and get the required insights out of the data. So, getting the right data is really really important. In order to get data for the successful modeling part, The steps involve:
- Researching in the right direction as per given business problem and their requirements
- Define Data Collection structure- what parameters will be necessary to scrape in order to satisfy the business problem
- Data Scraping, Data Pre-processing, EDA, and so on as per the given problem.
Since I worked majorly on top of building the NLP pipeline. So, I think dealing with textual data is a bit complicated as compared to other types of data.
Check out what books helped 20+ successful data scientists grow in their career.
Nisha: How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
Kajal: Domain knowledge helps you to think in the right direction on a given project. So, yeah it’s important that you must acquire it by researching a bit. You don’t need to be an expert at it but yeah if you know a little bit, it will eventually help you to proceed in the right direction and finish the project in a lesser time by satisfying the business requirement. Although, In many cases, there will be a domain expert if there is a high requirement to understand the domain deeply first.
It is very very important in order to keep going and acquire new skills daily irrespective of how difficult they might seem.
Nisha: 3 words that best summarize how you learned ML and data science:
- Inquisitive Mind – (Zeal to Learn & bring change) If you are curious to know new things daily and experimenting with those. You ask really good questions and have a thought that whatever you are learning can change the coming world and you are a part of that change. It will keep you pushing forward.
- Perseverance – It is very very important in order to keep going and acquire new skills daily irrespective of how difficult they might seem.
- Remembrance – Most important aspect is to remember things as well, as we are learning new things consistently. In order to achieve that, I reflect back on whatever I learn daily. One may start with writing things down after the completion of one project as well either in the form of a report or some article.
Nisha: Courses: what courses/programs have you taken that have significantly contributed to advancing your career in data science?
Kajal: Coursera provides great catalogs of courses on Data Science. I did some of the courses from this platform that eventually gave me practical experience as well through their assessments. One of the great courses provided by them is Machine Learning by Andrew NG which helped me to advance my ML modeling skills.
Nisha: What advice would you give to someone who wants to get into data science today?
Kajal: I would advise them to get started. Don’t waste any further time thinking. There is a lot of online end-to-end content nowadays which will eventually help them to get started.
- Having a knowledge of any programming language out of Java, Python, R is the basic step, and most importantly Python, which is the most used language in the industry nowadays.
- One may start with any basic project on Kaggle if one doesn’t know how to start and where to start. They can learn basics & fundamental concepts by researching/googling their queries while doing basic projects. Kaggle is a good place for collaboration, So beginners will gonna learn a lot by asking good questions.
- Other than that, I prefer to read articles on Medium, Analytics Vidhya, KD Nuggets to brush up on my skills.
- Another benefit of doing projects in beginning is that you are directly learning practical implementation and concepts around them by reading articles/journals or whatsoever comes as a googled result and by doing projects, you are adding value to your Resume already.
- On top of these suggestions, I would strongly suggest having a good online presence and presenting whatsoever project you have done online because I have seen people with good skills but still not able to get hired. So, Online presence and proof of skills that you have acquired are really important for recruiters.
That is why I choose my career in Data Science.
Nisha: What inspires you about working in Data Science?
Kajal: I have seen few projects in Data Science which is really inspiring in terms of how they are transforming this new era and making this world a better place to live by solving real-world problems using Data Science skills. I even worked on one of the inspiring ideas that are to conquer fake news and predict what is fake news or what is real news out there.
Another inspiring idea was to build a system that will translate sign languages into speech and vice versa for specially-abled people.
Even I am a very passionate person who always wanted to use herself as a resource to bring a good change in this world. That is why I choose my career in Data Science.
A young data scientist, who wishes to explore the different ways that Artificial Intelligence can help benefit the longevity of human life and conquer terminal illnesses. I would also like to explore peoples opinions on Artificial Intelligence and what they believe it brings or does not bring to the table. There are so many unanswered questions, which I would like to get more insight on.