Unquestionably, the career as a data scientist is one of the most promising options these days and it\u2019s driving a huge number of enthusiasts to become a part of this community. Data science courses are being taken up by people across the globe. In addition, enthusiasts are also making transitions \u2013 coming to data science from different departments. However, the rise in the popularity of the career as a data scientist isn\u2019t only generating lots of opportunities but creating a lot of competition as well \u2013 for both aspiring data scientists and those who are already working in the field. So, these days, the major question is how \u2018to become a top-notch data scientist<\/strong>\u2019 to stand out of the pack.<\/p>\n

Acquiring the skills required to become a data scientist is extremely essential for anyone looking to get a job in that position. But you should understand that data science is a highly complicated field and it requires a lot of skills to become a data scientist. While it\u2019s practically impossible for anyone to have all the skills related to data science field, there\u2019re some skills that differentiate between a good data scientist and a great data scientist. Here, we\u2019re going to discuss the skills which you should focus upon to become a top-notch data scientist<\/strong>.<\/p>\n

Crucial skills to become a top-notch data scientist<\/em><\/strong><\/h3>\n

1- Continued education<\/em><\/h4>\n

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While there\u2019re notable exceptions, in general, data scientists come with a strong educational background that helps them to attain the in-depth knowledge necessary to perform their job responsibilities. Forty-six percent of them come with PhDs while eighty-eight percent hold a Master\u2019s degree at least. The most common fields of study include mathematics, statistics, computer science, and engineering. If you want to become a top-notch data scientist<\/strong>, your education shouldn\u2019t end there. You should try to undertake online training to learn specialized skills that are creating buzz around the data science domain. Then, you can go ahead to get a Master\u2019s degree in any of the fields related to data science. In addition, you should keep on practicing what you learned in a class by exploring data analysis, starting a blog etc to learn more about the topics.<\/p>\n

2- Python<\/em><\/h4>\n

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Python is the most popular programming language in the data science field. In fact, a large percentage of data scientists prefer to use Python as their primary programming language. In data science processes, it can be used for almost every step involved. You can use not only across large datasets but in creating datasets as well. A huge percentage of data scientists across the globe consider Python as the foundation for performing data analysis tasks. Hence, to become a top-notch data scientist<\/strong>, you should try to become a master of this language.<\/p>\n

3- R<\/em><\/h4>\n

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R programming language is heavily used in data science for statistical problem-solving. You can use this language to solve almost any data science related problem and thus, attaining a solid understanding of R is crucial to become a top-notch data scientist<\/strong>. Though R comes with a steep learning curve, there\u2019re lots of great resources available on the web that can help gain adequate knowledge. Alternatively, you can join a coding bootcamp as well to gain knowledge and hands-on experience.<\/p>\n

4- SQL<\/em><\/h4>\n

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Even though Hadoop and NoSQL have become a large part of data science, proficiency in SQL (Structured Query Language) is important to become a top-notch data scientist<\/strong>. SQL is particularly designed to help data scientists to access, communicate as well as work on data. It can also help in transforming database structures and carrying out analytical functions. Concise commands of SQL can not only help to save time but also lessen the amount of programming he\/she needs to perform difficult queries.<\/p>\n

5- Apache Spark<\/em><\/h4>\n

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One of the most widely used big data technologies, Apache Spark is a big data computation framework similar to Hadoop. However, Spark is faster than Hadoop which reads and writes to disk, but Spark caches the computations in memory. To become a top-notch data scientist<\/strong>, you need to become proficient in Apache Spark as it\u2019s particularly designed for data science to help in running complicated algorithm faster. It helps in distributing data processing when you\u2019re dealing with a massive amount of time and thus, saves you time. In addition, it helps data scientists to deal with complex unstructured datasets and it can be used on a single machine or cluster of machines. The strength of Spark lies in its platform and speed, both of which contribute heavily toward carrying out data science projects easily.<\/p>\n

6- Hadoop platform<\/em><\/h4>\n

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Having experience with Pig or Hive is considered a strong selling point and thus, important to become a top-notch data scientist<\/strong>. Data scientists may encounter situations where they need to send data to other machines or the volume of the data they\u2019ve exceeds the memory of the system, this is where Hadoop helps them. Hadoop can be used to convey data to different points on a system quickly. In addition, it can be used for data filtration, data exploration, data sampling, and summarization.<\/p>\n

7- Machine learning<\/em><\/h4>\n

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Though a significant number of data scientists aren\u2019t proficient in areas and techniques of machine learning, a solid understanding of them is needed to become a top-notch data scientist<\/strong>. Machine learning techniques like logistic regression, decision trees etc help one to solve various data science problems which are based on predictions of key outcomes. Advance machine learning skills like learning methods (supervised learning, unsupervised learning, and reinforcement learning), natural language processing, time series, computer vision, adversarial learning etc can help a data scientist stand out of the pack.<\/p>\n

8- Unstructured data<\/em><\/h4>\n

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Ability to work efficiently with unstructured data is crucial to become a top-notch data scientist<\/strong>. Unstructured data refers to undefined content which doesn\u2019t fit into database tables. These include blog posts, videos, customer reviews, video feeds, social media posts etc which are heavy texts lumped together. Sorting unstructured data is difficult as they\u2019re not streamlined. By working with unstructured data, data scientists can untangle insights which can help in effective decision making.<\/p>\n

9- Data visualization<\/em><\/h4>\n

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A huge amount of data is being generated frequently by the business world and this data has to be translated into a format which will be easy to understand by average people. As people understand pictures in forms of graphs and charts more than raw data naturally, it\u2019s the responsibility of a data scientist to visualize that data with the help of different data visualization tools like Tableau, Matplotlib, ggplot etc. These tools help data scientists to convert complicated results from their projects to an easily comprehensible format. Data visualization enables businesses to directly work with data. This lets them grasp insights quickly and act on business opportunities to gain a competitive advantage.<\/p>\n

10- Business acumen<\/em><\/h4>\n

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A robust understanding of the particular industry you\u2019re working in is crucial to become a top-notch data scientist<\/strong>. It\u2019s also important to be able to discern the problems critical for the business and identify new ways the company should be leveraging the captured data. To perform this task efficiently, data scientists need to understand how the problems they solve can impact the business.<\/p>\n

11- Communication skills<\/em><\/h4>\n

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When it comes to hiring an elite data scientist, companies look for someone who can fluently and clearly communicate their findings to non-technical teams like Sales or Marketing, apart from having the above skills. A data scientist has to enable the business to make useful decisions by arming it with quantified insights. He\/she also needs to understand the requirements of the non-technical teams in order to appropriately wrangle the data. Effective data storytelling is another key requirement to become a top-notch data scientist<\/strong>. A data scientist must know how to develop a storyline around their findings to make it simple for everyone to understand. For example, presenting a table of data isn\u2019t as sharing the findings from that data in a storytelling format.<\/p>\n

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

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These days, there\u2019re lots of events, coding seminars, data science meets, hackathons etc organized by leading organizations to groom talents and scout for the best as well. Participation in those events not only helps you to broaden your knowledge horizon to encounter real-world challenges but also to network easily. Workshops and data science bootcamps greatly help you in taking your skills to the next level and give you a competitive edge as well. You need to have a solid understanding of the majority of the above skills to become a top-notch data scientist<\/strong>. And to learn and sharpen those skills, you need to pick a premier institute which offers best courses on data science topics. These days, the marketplace is flooded with lots of data science courses. A significant number of training academies offer lucrative discounts on those courses. However, it\u2019s much more than pretty packages or hefty discounts to choose the right course for yourself. In order to succeed in your journey to become a top-notch data scientist<\/strong>, you should have a basic knowledge of the courses you\u2019re planning to undergo, their individual offerings to be able to compare them and take your pick, and a clearly chalked out career plan.<\/p>\n