Data Science is the hottest job around the globe and is fast gaining traction among millennials and Gen Z. It is also one of the highest-paying jobs in the industry. Currently, foreign universities and institutes are rolling out data science and analytics undergraduate and graduate programs in order to churn out a steady stream of job-ready professionals.
But before applying for MS in the US, UK or Canada, there is a set of criteria candidates need to fulfill in order to make the cut. These are the entry requirements that are set by these universities.
For example, the University of Edinburgh’s Business School expects incoming students for MSc Analytics to have a strong background in linear algebra, calculus, probability, statistics, and computer programming. In a similar vein, Data Science Institute of Columbia University New York expects applicants of MS in Data Science and Certification of Professional Achievement to have a clear understanding of linear algebra, probability, statistics, Python, Java, C+, among others. As the competition for making the cut in foreign universities intensifies, students need to factor in more than just their GRE scores to get admissions in top-ranking universities.
In this article, we list down entry requirements of foreign universities look for MS applicants:
1| Statistical And Mathematical Knowledge
When you are making an investment of $60k USD, you need a strong stats/Math background to survive in the MS. The statistical and mathematical background is a must if one wishes to pursue a career in data science and analytics. Before joining the data science course at a university, one can prepare for the core concepts of data science which are linear algebra, calculus, probability, and statistics. While learning machine learning models at the university, it will be easier for you to understand the concepts of conditional probability, priors and posteriors, and maximum likelihood.
2| Build Data Intuition
Before joining any institute, one can participate in predictive modelling competitions. There’s Predictive Modeling -NMIMS Competition in Kaggle which will help in understanding the data modelling process and also hone their skills. This will push you ahead in your data science and analytics career. Online courses are available where one can learn how to prepare for these competitions. Building intuition about the data from Coursera is one such example.
3| Knowledge of Algorithms
Whether it is the algorithms of data structure or the basic algorithms of machine learning, students are expected to have a basic knowledge of these algorithms. For instance, one must have an understanding of the notations — the best and worst cases of data structure algorithms which shows how quickly or slowly an algorithm runs, the pseudocodes of machine learning algorithms and its working process, etc. An understanding of these details will help you to stay ahead in your journey.
4| Strong Programming Skills
This is an important skill and a strong knowledge of programming language is very important for doing all computational problems. A good starting point is Python, dubbed as the most popular ML programming language.
5| Knowledge of Tools
Along with an understanding of statistics and programming languages, knowledge of important tools which are being widely used in the data science projects and statistical operations are a plus for someone who wants to put a solid grip in this domain. Some of the top data science tools are Apache Spark, BigML, Tableau, Jupyter, Matplotlib, ScikitLearn, Natural Language Toolkit (NLTK), Weka, TensorFlow, Pandas, ggplot, among others.
To the fact, the admissions are getting tougher and eligibility criteria are racked up. It’s not just a GRE/GMAT score, grades or work experience that counts nowadays, skills like intuitive, communications, quantitative, among others are also being counted in order to get admitted into a renowned institution.
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A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.