How to start learning Data Science for admissions in top schools abroad?

Question

ayush sharma · Accepted Answer

When you are looking to learn data science for admission in top schools abroad, you have to consider the basic topics. The school interview sessions are divided in two parts. First they just conform and cross check your documents  and then they ask you a few questions. Out of those questions, not all are related to your subject i.e. data science. Therefore, you don&rsquo;t need an expertise on the subject, just the basic topics will do.
In a way, the school judges your ability to perceive and react. The purpose of these interview sessions are not to judge your knowledge. That&rsquo;s why whatever study abroad expert you ask, they will tell you to work on your personality features first. Because it&rsquo;s all about how you present yourself there. You have to make an impression that is impactful. Otherwise, there are a lot of candidates to choose from in top schools abroad.
Coming back to the topic, there are few names that you should be familiar with. Data science has become more and more popular now. It is one of the most sought-after master courses in universities abroad. So learning a few basic fundamentals of data science can help you in many ways. These topics are -
PythonSQLlinear regressiondata visualizationdata libraries
To  answer your question about how to start learning data science, I suggest you start with these topics. Python is like the most familiar language in data science. On the other hand, SQL and linear regression are directly related to data science and it's one of the starting things a data science master program includes. Going through these topics will build a foundation of data science. Then you can answer some basic questions in this subject.
Any data related topics like visualization, library, configuration are a good starting point. These topics give an idea of what is next to come in a data science course. Plus, these objects have their own expertise. You can literally find tons of jobs, say, under data visualization only. Go through these topics and let me know how it works out.
You actually don&rsquo;t have to master these topics either. Just the basic information will do. In the interview session during your admission process, the university will already have your qualifications. They ask questions to see if you are capable of performing in certain situations or not. Just be confident and take your time.

Yash Mothia · Answer

Entering the field of Data Science, especially if you're aiming for admission to top international schools, requires a strategic and informed approach. The journey to mastering Data Science begins with a solid foundation in mathematics and programming. Key areas to focus on include statistics, algebra, and calculus, coupled with programming skills in languages like Python or R.
Step-by-Step Guide to Learning Data Science:
Basic Mathematics and Programming: Start with online courses in basic mathematics and introductory programming. Platforms like Coursera or edX offer courses tailored for beginners.
Advanced Specialized Learning: Once you have the basics down, advance to more complex topics such as machine learning, data visualization, and big data analytics. This can be achieved through specialized courses or bootcamps.
Practical Application: Apply your learning by working on real-world projects or internships. This practical experience is crucial and highly valued by admissions committees.
Certification and Portfolio Development: Earning certifications from recognized platforms can bolster your application. Simultaneously, build a portfolio of your projects to showcase your expertise and practical skills.
Research on Specific Admission Requirements: Different schools have varied requirements. Research each school&rsquo;s prerequisites and tailor your learning path accordingly.
Incorporating these steps into your preparation not only gears you towards a Data Science career but also enhances your profile for top-tier academic institutions. Remember, the field is constantly evolving, so continuous learning and staying updated with the latest technologies and methodologies are crucial.