5 Tips to Kickstart your Career in Data Science and Analytics

Data Science
Source: PlatinumSol.com

Data science and analytics is more than a passing trend; it is the way to define not just a successful business, but almost all verticals associated with a business. If we look at numerical figures, in India alone, there are 50,000 positions related to data science and analytics, and compared to the global estimates, India contributes to only 12 percent of the global jobs in the domain. Further, as more projects get outsourced, these figures are only expected to grow. Therefore, it is not surprising that students and professionals alike are getting enrolled in data science courses to be a part of this new wave of 0s and 1s that is making all the difference. Irrespective of whether you’re a college student eager to step in or a professional looking to upgrade your skills, following are some comprehensive tips that will gear you up for a career in data science and analytics:

  1. Build foundations in statistics and machine learning:

 An aspiring data scientist is always expected to have some familiarity regarding all the latest machine learning methodologies and statistical models used in industries. Interested students can start from the fundamental theories of hypothesis testing, normal distribution, and central theorem. Once the basics are understood, they can then move on to mastering advanced skills like decision trees, linear regression, cluster analysis, logistical regression, generalised additive models, and the like. By building a foundation in statistics and machine learning, students will find it easier to master other aspects of the domain. 

  1. Develop expertise in programming:

As far as the field of data science and analytics is concerned, open source languages like Python and R are the most in-demand by recruiters. The internet is full of both free and paid resources that enable people to become proficient in both these languages. Further, those who already possess coding skills in object-oriented languages like C++, Java, will find it easy to connect with Python’s functional libraries and syntax, thus making their shift into data science a bit easier. However, when it comes to working with and developing statistical models, R has greater leverage over Python.

There is no definite answer for which of the two programming languages is better, as both R and Python have indispensable advantages of their own. While R is the preferred language of statisticians in academia, Python is the preferred language of those who deal with generating predictive data models. While a thorough understanding of either has countless benefits on their own, an operational language of SQL along with developing VBA models can set you apart from others if you apply for a data scientist position.

  1. Focus on practical applications instead of focusing on theory:

While data science courses and training give students a fair idea of the theoretical aspects of the topics they learn, it is essential for them to focus on the practical applications as well. This would not only help them understand concepts but would also give them a deeper understanding of how these concepts can be applied in reality. For starters, students can practice practical applications by working on open data sets available on various online portals. This goes a long way in helping students to develop a familiarity with data manipulation that lasts for a long period of time.

You can also take some pointers by checking out online portals where solutions are posted by people who are currently working in the field. In this manner, you can get an inside look at how real-time problems in data science can be handled. To keep yourself updated about the latest trends in the field, you can also follow various blogs maintained by data scientists and experts who frequently post about recent developments and advancements in the area.

  1. Understand the need for data science and analytics in business: 

Since data science and analytics is all about crunching numbers and manipulating data, it is understandable if as a data scientist you’re tempted to limit yourself to just numbers. However, business analysts are also expected to understand what use their insights would bring to their company’s business. As a data scientist, you are expected to take into account factors like the crucial customer and product information to make predictions that can drive insights for the company. If you understand how your predictive models can impact scenarios like customer attrition and retention, the interaction of products with a customer, how to generate engagement tools, then you’re well in your power to start your very first project as a data analyst.

  1. Network with the right people:

While learning how to be a good data scientist should always be the focal point of learning, it is also crucial for a person to network with the right people who can introduce them to interesting opportunities. You can achieve this by taking part in conferences and events in the data science and analytics industry, and by attending famous hackathons and meetups in your area. Several online portals conduct meetups and annual conferences that solely pertain to the field of data science and analytics. When it comes to starting a career in an industry that is so much in demand, it always helps to grow your network with people who can get you the opportunity you need to boost your career.

A career in data science and analytics can be intimidating, especially if you’re just at the beginning of your journey. An aspirant who wishes to enter this industry can face a number of doubts at every turn; these may include questions about which techniques one should focus on and which tools to learn. While there is no end to learning when it comes to a field as diverse as data science and analytics, it always helps to keep in touch with industry experts who can give you insights about what’s new and upcoming in the field.


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