The<\/em> role of data scientist<\/strong> surely involves a lot of great things and those are the reasons professionals from across the globe are striving to step into the field of data science. Businesses, regardless of their field and volume, are looking to recruit \u2018effective\u2019 data scientists<\/strong>. We mentioned the term \u2018effective\u2019 because there is a huge supply of so-called data scientists<\/strong> that often fail to meet the expectations. The continuing media hype around data science has heavily exploded the volume of junior talents over the past few years.<\/p>\n

We aren\u2019t trying to establish that those so-called data scientists<\/strong> lack any key skills, but companies are looking for something more to get some true help, especially in the context of today\u2019s extremely competitive landscape. This post is aimed at throwing some light on what an average data scientist<\/strong> should do to become a good data scientist<\/strong>.<\/p>\n

1- Effective ways to become a good data scientist<\/em><\/strong><\/h3>\n

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Data science<\/em> is a field where constant growth is required and possible. And to become a top-notch data scientist<\/strong> one needs to learn a lot of skills. But the question is, how many of those skills an average data scientist<\/strong> needs to improve to thrive in the field? And is that actually possible? And another key factor is anybody doesn\u2019t have his\/her entire life to keep on improving the skills. So, the journey to become an effective data scientist<\/strong> has to be effective, based on the real-world requirements with justifiable duration.<\/p>\n

Let\u2019s have a closer look at the skills that you need to improve to become a good data scientist<\/strong>.<\/p>\n

1.1- Sharpen your SQL skills<\/em><\/h4>\n

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Expertise<\/em> in SQL is a common requirement, whether your key focus is on machine learning or AI or data engineering. It\u2019s also not a solution to all the problems a data scientist<\/strong> may ever face. But you have to become a master of it in order to comprehend how to access data. If you find yourself stuck with the massive amount of data tooling, chances are you\u2019ll find a SQL. And once you have a robust understanding of the SQL paradigm, most likely you\u2019ll find it much easier to master other query languages that opens up a whole new world.<\/p>\n

1.2- Sharpen your programming language skills<\/em><\/h4>\n

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In<\/em> your journey to become a data scientist<\/strong>, you\u2019ve certainly acquired a good amount of skills on procedural programming languages like Python, R, Java, Scala etc. For most of the established data scientists<\/strong>, Python usually serve them extremely well and for reasons. You can use it for a large number of different things \u2013 from cleaning data and creating deep learning models to accessing the AWS API and building a web application, and many more. In case you don\u2019t want to pick Python, you can always go with any of the other languages that are being preferred by the data science domain. The thing is once you pick the language, you need to become a master of it. Get to know its best parts and build something fun with it. And when you feel really confident enough, you can start mastering another language.<\/p>\n

1.3- Focus on applying theoretical concepts more<\/em><\/h4>\n

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It\u2019s<\/em> always good to have solid grasp of the theoretical concepts running behind the techniques you use as a data scientist<\/strong> sometimes. But when you don\u2019t apply them frequently, most of them remain as theoretical concepts only, which is one of the biggest obstacles to become a good data scientist<\/strong>. It\u2019s imperative your practicing progress should maintain a healthy balance between practical and theoretical. As soon as you master a concept, simply head over to Google and look for a problem or dataset where you can use it and start working on it. You\u2019ll be surprised to see that you\u2019re retaining the concept way better than before. Also, it\u2019s important to remember that it\u2019s simply not possible to learn everything in one go. So, fill in the gaps with practice and your expertise will automatically increase.<\/p>\n

1.4- Start with the answers<\/em><\/h4>\n

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This<\/em> is a common occurrence that when an average data scientist<\/strong> faces a problem statement, he\/she, in most cases, spends the initial time on finalizing and looking at ways to attain their goal, instead of focusing on the goal itself. Here comes the importance of developing a clear understanding of the business cases, without which the chance for the data scientist<\/strong> to come up with a solution that doesn\u2019t meet the client expectation is more. Hence, it\u2019s extremely important to develop a robust understanding of the business use cases to be able to come up with an effective course of action. Also, a structure approach is crucial to become a good data scientist<\/strong>. Without it, your approach will likely be haphazard with chances of losing track of your own work when presented with a complex problem.<\/p>\n

1.5- Practice repeatedly and create a feedback loop<\/em><\/h4>\n

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One<\/em> of the most effective ways to become a good data scientist<\/strong> is to keep on practicing. This can be working on non mission-critical, mundane tasks and even can drag your productivity down initially, but it\u2019ll force you to become a master of the fundamentals. It\u2019s a fact that the more challenges you face, the chances for breaking down complex nuances and know the mechanism to attain your goals is more. Therefore, keep on frequent and regular practicing and try to determine the strategy, improve your problem-solving skills, and develop a clear understanding of client expectations to higher your overall chances of becoming a good data scientist<\/strong>. You should also try to review codes written by other people and fix small bugs if possible. When writing your own code, try to make it more readable for others so you can get feedback from them. In this context, it\u2019s imperative that you try to partner with senior data scientists<\/strong> in order to receive actionable and timely feedback. You should keep in mind that one of the biggest traits of the people, who\u2019ve a solid growth mindset, is that they\u2019re usually not ashamed of accepting what they don\u2019t know and they constantly try to fill the gaps by seeking feedback.<\/p>\n

1.6- Be prepared for continuous learning<\/em><\/h4>\n

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The<\/em> domain of data science evolves at a fast pace. It means, the tools and technologies are in demand now, may not remain so in the future. So, continuous learning and upgrading yourself is crucial to stay up-to-date with the industry trends and to become a good data scientist<\/strong>. Also, there are a significant number of tools and technologies appear in the tech domain as a whole. Try to learn the relevant ones and put them to your use. For example, you can learn the Jupyter Notebook<\/strong>. You can consider it as a living online notebook and allows students and faculty to weave computational information such as statistics, code, data etc together with the help of narrative, graphs, and multimedia. You can use it to open up your data, share your stories behind your computations, and enable future innovation and collaboration. With a Jupyter Notebook<\/strong>, you can also play around and experiment with code.<\/p>\n

1.7- Study consistently<\/em><\/h4>\n

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One<\/em> of the biggest things that differentiate a good data scientist<\/strong> from an average one is the former\u2019s consistency in studying. Some data scientists<\/strong> tend to get distracted easily and take frequent breaks. In reality, trying to get back into the right track becomes extremely difficult at that time. To avoid this, you need to set goals for yourself and chalk out a solid plan on how to study consistently. The thing to keep in your mind is that if you want to become a good data scientist<\/strong>, you\u2019ve to be ready to put in the time and effort. If you keep finding excuses continually not to do this, data science may not be the ideal field for you.<\/p>\n

1.8- Work on your communication skills<\/em><\/h4>\n

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Though<\/em> communication skills are one of the aspects usually overlooked by data scientists<\/strong>, it\u2019s absolutely critical if you want to climb the ladder up. You can master multiple tools and learn all the techniques, but if you fail to explain your findings to your client in a digestible way, you\u2019ll fail to become a good data scientist<\/strong>. In order to improve your communication skills, try to explain some of your findings to a non-technical person and see how you can articulate the problem. There\u2019re lots resources available on the web that can help you greatly in this regard, but again practice is of key importance as well.<\/p>\n

2- Parting Thoughts<\/em><\/strong><\/h3>\n

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You<\/em> should understand that you\u2019ll never become a good data scientist<\/strong> within just a couple of months or a year, regardless of how much you learn and how hard you work. It isn\u2019t simply a feasible goal. But you\u2019ve to keep on striving to continue improving your skills and growing. Finally, it doesn\u2019t heavily matter from where you start your career as a data scientist<\/strong>. What actually matters is how you\u2019re trying to progress in your career and what steps you\u2019re following to become a good data scientist<\/strong>. Follow the above tips and you\u2019ll surely be able to attain this goal if you\u2019ve the motivation to do it the hard way.<\/p>\n