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How to improve your analytics talent

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Data Analytics is one of the most sought-after skill sets today, with students and professionals alike aspiring to be enabled with the necessary skills to derive data-driven business insights in their careers. It also helps organisations attain a competitive advantage over others.

Data Analytics is not limited to mathematicians, statisticians or IT professionals with programming skills. The need to analyse data has become so elementary today that a professional in any business is expected to know the necessary skills. While professionals today are aware of the need to be trained, some are unaware of how to embark on a career in analytics.

For students and professionals who aspire to be data-driven, here’s a list of options one can choose from to specialise in Data Analytics skills.

Data Science specialisation:
Those who are aspiring to become data scientists, data scientists looking to expand their knowledge of various tools and professionals who want to use analytics more effectively can take this up. Here, one will start by learning the fundamentals of analytics, including business statistics, and then go on to learn the most widely used tools in analytics.

Big Data specialisation: There are not many people with Big Data specialisation. Hence, there is a good scope for people with this skill. During the course, one will learn the basics of Big Data and then move on to the latest big data tools and technologies. This field covers everything that is expected of an expert Big Data professional.

Data Visualisation: The specialisation is useful for aspiring and current data scientists looking to build expertise on data visualisation. It is also useful for those who need to share their analytics results with a wider audience. The students will learn how to tell a story with the available data and how to communicate it effectively. One will learn powerful visualisation techniques using popular data visualisation tools.

Machine Learning (ML):
This path is recommended for any aspiring or current data scientist. One will start by learning the basics of Machine Learning and go on to learn the most popular ML techniques like neural networks, support vector, random forests etc.

Machine learning is considered as the future of analytics. As datasets grow in size, ML is playing an increasingly important role in analytics.

HR Analytics:
All existing and aspiring HR professionals looking to fast track their careers can consider this. One will start by learning how to perform complex analyses using Excel. Later, the student will move on to the application of analytics within the HR domain. There are case studies on all popular HR aspects like attrition prediction, training effectiveness etc.

Business Analyst (Finance specialisation):
All existing and aspiring finance professionals looking to fast track their careers and data scientists who want to build expertise in financial analytics can opt for this course. One will start by learning the fundamentals of analytics including business statistics. He or she will then learn the popular analytics tools for finance professionals and application of analytics in finance through hands-on case studies.

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