As<\/em> more and more companies are trying to become data-driven, it looks like each of them will need to employ data science, making the demand for data scientists<\/strong> even greater.<\/span> The world is becoming connected increasingly and a huge amount of data is being generated every single day and businesses are trying their best to make use of this data to rise above the competition.<\/p>\n

However, it\u2019s not easy to become a<\/em> data scientist<\/strong><\/a>. One needs to have an adaptable and definite set of skills. It requires a perfect mix of structured thinking, problem-solving and a lot of technical skills in order to become a successful data scientist<\/strong>.<\/span> If you\u2019re planning to become a data scientist<\/strong>, read on as we\u2019ve put together some essential things that you\u2019ve to keep in mind to become successful in your endeavor.<\/p>\n

1- Good academics<\/em><\/strong><\/h3>\n

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Educational<\/em> qualifications play a crucial factor in being a data scientist<\/strong>.<\/span> Organizations often prefer candidates with a Master\u2019s degree in the field of computer science, mathematics, statistics etc.<\/p>\n

Also, there\u2019re some research-oriented companies that look for data scientists<\/strong> who come with a PhD. So, if you\u2019re just starting out, it\u2019s wise to focus on building your educational qualifications.<\/p>\n

2- Robust programming skills<\/em><\/strong><\/h3>\n

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To<\/em> become a successful data scientist<\/strong>, your programming skills have to be at an exceptional level. Among other programming languages used in the field, Python<\/strong><\/em><\/a> is the most preferred and widely-used one. It\u2019s Python\u2019s adaptability that has helped it gain this position. You can use it for almost every step involved in the process of data science<\/strong><\/em><\/a>. You can work with different sets of data and create datasets.<\/p>\n

Good knowledge of R is also preferred for data scientists<\/strong>. R<\/strong><\/em><\/a> is widely used to solve various statistical problems. However, if you\u2019re not comfortable with programming, it may be a little difficult to master it because of its steep learning curve. If you\u2019re not coming from a tech background, programming as a whole may seem to be extremely difficult.<\/p>\n

There\u2019re several courses offered by reputed institutes that can easily help you get started. Just don\u2019t expect to do super cool stuff from the very beginning because that doesn\u2019t happen. But once you\u2019ve overcome the initial challenges and remain consistent, you\u2019ll surely be able to master them.<\/p>\n

3- Strong business intelligence skills<\/em><\/strong><\/h3>\n

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As<\/em> an aspiring data scientist<\/strong>, you should focus on developing strong business intelligence skills that is one of the essentials of the field. These skills need the ability to communicate your findings to business decision makers. Engaging these people in a manner which captures their attention both logically and emotionally has become imperative for data scientists<\/strong>.<\/p>\n

In any data-driven organization, a massive amount of data is produced on a regular basis that has to be interpreted to decision makers in an easily consumable format. Pictorial representations in the forms of charts and graphs are naturally more consumable to people than just plain numbers.<\/p>\n

To become a successful data scientist<\/strong>, you should have robust communication skills together with the ability to use data visualization and data management tools.<\/span> So, try to become familiar with tools like D3.js, ggplot, Tableau<\/strong><\/em><\/a>, matplotlib etc to be able to represent complex things in a simple manner. It\u2019s also equally important to work on your communication skills. Though these are usually the least talked about skills a data scientist<\/strong> needs, they\u2019re extremely important.<\/p>\n

You can master multiple tools and latest techniques, but if you fail to communicate your analysis properly to the decision-makers of your company or your client, it\u2019ll raise a question on your expertise. One effective way to overcome this is if you\u2019re working as a data scientist<\/strong>, find someone from a non-technical department and try to explain data science terms to him\/her. It\u2019ll help you gauge your progress to a good extent. There\u2019re lots of resources available on the web, so with a good amount of practice, you should be doing good.<\/p>\n

4- True expertise in machine learning<\/em><\/strong><\/h3>\n

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In<\/em> today\u2019s data-driven tech world, machine learning<\/strong><\/em><\/a> has become one of the heavily demanded skills for data scientists<\/strong>. To be proficient to deal with a massive amount of data on a regular basis, focus on learning machine learning techniques and methodologies like ensemble methods, k-nearest neighbors, random forests, among others. You can carry out these techniques further with the help of R and Python libraries.<\/p>\n

Also, it\u2019s extremely important to understand that the datasets you usually work with in machine learning competitions are usually clean and they\u2019re different from what you\u2019ll be working with in real-life projects. In real-life projects, you\u2019ll have to deal with unclean and messy data. It\u2019s a difficult part and eventually becomes a part of your routine. There\u2019s one thing you can do to overcome this hurdle is by reaching out to working data scientists<\/strong> and knowing about their experience. Of course, having a great score in a competition can greatly help you in measuring your learning progress, but the employers will want to know how you can leverage your knowledge in a real-life scenario.<\/p>\n

5- Don\u2019t try to learn multiple things at once<\/em><\/strong><\/h3>\n

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This<\/em> is a common mistake done by many aspiring data scientists<\/strong>. There\u2019re lots of tools used in the data science field and these people tend to focus on multiple things once at a time but they often end up mastering none of them.<\/span> Ideally, you should pick one tool and get a solid understanding of it. For example, if you\u2019ve started learning Python, don\u2019t try to focus on learning R before you\u2019ve mastery over Python.<\/span> There\u2019re lots of resources available that can help you learn each tool. So, take help of them and keep your patience.<\/p>\n

6- Study and practice as much as you can<\/em><\/strong><\/h3>\n

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Despite<\/em> what you may find in various posts, it\u2019s never easy to become a data scientist<\/strong>. You\u2019ve to devote a whole lot of quality time to become a successful one. You can always start with simple things and develop on that but you\u2019ve to make sure that you spend extensive and quality time studying and practicing.<\/p>\n

Data science<\/strong> <\/em><\/a>as a field is huge so there\u2019re certain areas that need to be studied over and over again.<\/span> You should understand that without adequate practice, your study will never get complete. With practice, more questions keep on coming up and you\u2019re compelled to study again to clear them out. Also, there\u2019re lots of existing concepts to learn in the field and there\u2019re new ones coming up almost regularly. So, you\u2019ve to keep yourself abreast of industry trends and changes. Visit forums for data scientists<\/strong>, read articles, books to make sure you stay on the same page with those happenings.<\/p>\n

7- Join a community<\/em><\/strong><\/h3>\n

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Data science<\/em> communities can greatly help in throughout your journey of becoming a data scientist<\/strong>.<\/span> As we\u2019ve discussed earlier that data science is a huge and difficult field, chances are that you\u2019ll burn out quickly and may spend a huge amount of unnecessary time attaining your goal.<\/p>\n

However, with buddies and mentors, you could sail through this. Don\u2019t go months of wasting time on a concept that someone could have helped you understand in a few hours. In data science communities, there\u2019re people who\u2019ve already done what you\u2019re trying to do now. On the other hand, you shouldn\u2019t look for help too fast when you haven\u2019t tried well. Remember that there\u2019re lots of things that you can learn from your own study, research and mistakes.<\/p>\n

8- Do projects<\/em><\/strong><\/h3>\n

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To<\/em> become a successful data scientist<\/strong>, you need to implement your learning. Take up real-life projects and try to understand the architecture behind them. In the data science field, hands-on experience matters a lot and large organizations often prefer applicants with this. In this context, communities can again help you to a good extent. If possible, try to collaborate on projects with fellow members. It\u2019ll not only give your learning a boost but will also help you in establishing your expertise as a data scientist among your peers.<\/p>\n

With the growing competition in the business world, the task of finding an effective data scientist<\/strong> has become difficult these days. As you can see, a data scientist<\/strong> needs to have a mixture of multidisciplinary skills like the ones discussed above. So, if you want to hold the century\u2019s hottest job someday, start your journey now if you haven\u2019t already. Just remember that becoming a data scientist<\/strong> isn\u2019t an easy goal to accomplish, you need to remain consistent and stay focused to become successful.<\/span><\/p>\n

Final takeaway<\/em><\/strong><\/h3>\n

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While<\/em> certifications and degrees are surely valuable, relying on them solely may not be able to take you much further. This is because understanding a data science project lifecycle, dealing with deadlines, handling clients etc \u2013 all are valuable parts of becoming an effective data scientist<\/strong><\/em><\/a>.<\/p>\n

So, try to apply your knowledge outside the classroom whenever possible. For example, you can maintain a blog where you consistently write about your analysis, post them on data science forums and ask for feedback. This\u2019ll help you learn a lot and will benefit you greatly when you look forward to advancing your career as a data scientist<\/strong>.<\/p>\n