If you want to become a data scientist<\/strong>, it\u2019s important to understand that no one can become that overnight. Becoming a data scientist<\/strong> is a journey and may seem to be a challenging one if you don\u2019t know what the criteria are to become one. Data scientists<\/strong> are expected to (and they do indeed) know a lot.<\/span> From computer science, machine learning<\/strong><\/em><\/a>, data visualization to mathematics, statistics and communication \u2013 you\u2019d need to master a lot of things.<\/p>\n In this post, we\u2019ve categorized all the skills that are required to become a data scientist<\/strong> into three major categories to help you get a solid understanding of them and proceed with your career. Let\u2019s have a look at them.<\/p>\n Curiosity<\/em> is an extremely crucial skill that will encourage you to continue putting your effort throughout the journey of becoming a data scientist<\/strong>.<\/span> It\u2019ll also help you understand what questions need to be asked when you\u2019re diving into a new dataset.<\/p>\n Your first attempt will rarely succeed, but if you keep mining deeper, surprising things may come up. There isn\u2019t any single way to increase curiosity. Ideally, you should give yourself space and time to learn or do projects outside of your regular work to keep yourself inspired and curious.<\/p>\n To<\/em> become a good data scientist<\/strong>, you should have a good problem-solving intuition. When you\u2019ll be working as a data scientist<\/strong>, knowing how to solve a problem which is defined for you won\u2019t be enough. Instead, you\u2019ll need to find and define the problems first.<\/p>\n Similar to curiosity, there isn\u2019t a single way to develop this skill. Some aspiring data scientist<\/strong>s nurture this skill by learning how to code, for example.<\/p>\n You<\/em> need to have a robust understanding of both statistics and mathematics to become a data scientist<\/strong>.<\/span> The better you understand them, the better you\u2019ll be in using them at work.<\/p>\n Remember that it\u2019s not all about being a statistician or mathematician. Instead, it\u2019s about using the basics of them as a foundation for business analytics.<\/p>\n Usually,<\/em> data scientist<\/strong>s hold a Master\u2019s degree or a Ph.D. in computer science, statistics etc that offer them a good foundation to connect with the relevant technical points that encompass the requirements of becoming a data scientist<\/strong>. Here\u2019re the most important and common technical skills that you should focus upon.<\/p>\n Python<\/span><\/em><\/strong><\/a> is one of the most in-demand languages for data scientist<\/strong>s.<\/span> Because of its versatility, Python can be used for almost every step involved in the work of data scientist<\/strong>s. This popular open source language is beginner-friendly and comes equipped with a lot of support resources.<\/p>\n Similar<\/em> to Python, R<\/strong><\/em><\/a> is almost a must for any data scientist<\/strong> position. This programming language is particularly designed for data science<\/span>. Though there\u2019re lots of great resources available for getting started with R, it comes with a steep learning curve.<\/p>\n Even<\/em> though Hadoop has become quite popular in the field of data science, it\u2019s still expected that an aspiring data scientist<\/strong> will be able to make use of SQL quite efficiently.<\/p>\n SQL<\/strong><\/em><\/a> or Structured Query Language can help you to perform different tasks in a database, apart from letting you transform database structures and carry out analytical functions.<\/p>\n Knowledge<\/em> of Hadoop<\/strong><\/em><\/a> platform is preferred to become an adept data scientist<\/strong>. Having experience with Pig or Hive is also a good selling point when it comes to job opportunities.<\/p>\n As a data scientist<\/strong>, you may encounter situations where the volume of data you hold exceeds your system\u2019s memory or you need to transfer the data to other servers.<\/span> With the help of Hadoop, data can be conveyed quickly to different points. You can also use Hadoop for data filtration, data exploration etc.<\/p>\n Today,<\/em> big data is everywhere and there\u2019s a dire need to capture and preserve whatever data is being produced. That\u2019s why big data analytics has come to the frontline of today\u2019s technological domain.<\/p>\n As a data scientist, <\/strong>it\u2019s crucial that you\u2019ve adequate knowledge about frameworks to process big data.<\/span> Apache Spark<\/strong><\/em><\/a> is steadily becoming the most widely used big data technology across the globe. The data processing engine is similar to Hadoop with the main difference being that it\u2019s faster.<\/p>\n Apache Spark is particularly designed for data science to help the data workers run complicated algorithms faster. It also helps data scientist<\/strong>s to handle complex, unstructured datasets.<\/p>\n The<\/em> business world generates a huge amount of data frequently. This data has to be translated into a format which will be easy to understand. Pictures in the forms of graphs and charts are easily understood by people compared to raw data.<\/p>\n As a data scientist,<\/strong> you\u2019ve to be able to visualize data by using data visualization tools like Tableau<\/strong><\/em><\/a>.<\/span> Tableau is also an analytics platform that is powerful and easy to use. If you\u2019ve not heard of it, you can enroll in a course run by an online university or institution to learn the basics.<\/p>\n It\u2019s<\/em> crucial for a data scientist<\/strong> to be proficient in working with unstructured data.<\/span> Unstructured data refer to the undefined content, which doesn\u2019t fit into database tables. Some examples can include blog posts, videos, video feeds, and social media posts, among others. These are heavy texts that are lumped together and the process of sorting them is complex because they aren\u2019t streamlined. Ability to work with unstructured data would help a data scientist<\/strong> untangle insights which can be valuable for decision-making processes.<\/p>\n A<\/em> significant number of data scientist<\/strong>s aren\u2019t proficient in machine learning techniques<\/strong><\/em><\/a> and its different areas like reinforcement learning, neural networks, adversarial learning etc. To strengthen your position as a data scientist<\/strong>, you should focus on learning techniques like decision trees, supervised machine learning, logistic regression etc.<\/p>\n Deep learning<\/strong><\/em><\/a> has become a heavily talked about subject these days as it solves lots of limitations of traditional machine learning approaches.<\/p>\n These<\/em> non-technical skills can also be referred to as personal skills and are critical for becoming a data scientist<\/strong>. Let\u2019s have a look at them.<\/p>\n To<\/em> become a good data scientist<\/strong>, you should have a solid understanding of the industry you\u2019re putting your effort into. You\u2019ll also need to know about the fundamental elements that form a successful business model. Or else, you won\u2019t be able to channel your technical expertise productively.<\/p>\n You won\u2019t be able to perceive the business problems and potential challenges, which need solving in order to help the business sustain and grow. That\u2019s why it\u2019s extremely important to know how businesses operate so that you can channel your efforts in the right direction.<\/p>\n Businesses<\/em> looking for a good data scientist<\/strong> are actually searching for someone who can fluently and clearly communicate his or her findings to a non-technical team like the sales team or marketing team.<\/span> As we\u2019ve already discussed, a data scientist<\/strong> has to be able to help the business in decision making by understanding the needs of non-technical teams in order to wrangle the data properly.<\/p>\n Data storytelling is another required communication skill for a data scientist<\/strong>.<\/em><\/a> You should be able to develop a storyline around the data in order to make it easier for everyone to understand. You should also remember that most decision makers don\u2019t want to know what or how you\u2019ve analyzed; rather, what they only want to know is how it can impact the business in a positive manner.<\/p>\n It<\/em> isn\u2019t possible for a data scientist<\/strong> to work alone.<\/span> You\u2019ll need to work with product designers and managers to develop better products, with company executives to form strategies, with server software developers to improve workflow, and with marketers to create better-converting campaigns, just to name a few.<\/p>\n Put simply, you\u2019ll have to work with almost everyone in the company, including your clients. So, to become a successful data scientist<\/strong>, it\u2019s important to prepare yourself for teamwork.<\/p>\n Hopefully<\/em>, the above read will let you understand the key skills that you\u2019d need to become a data scientist<\/strong>. If you\u2019re ready to get completely immersed in learning the above skills and more, consider joining a bootcamp<\/strong><\/em><\/a> offered by a reputed institution to move a step closer to becoming a data scientist<\/strong><\/em><\/a>.<\/p>\n Finally, you must remember to always stay updated. Follow the latest updates in the field of data science by reading articles, news, browsing relevant sites and forums, groups etc. Look at the present and upcoming trends in the field and try to identify the place where you want to fit in and then prepare yourself accordingly.<\/p>\n1- General skills<\/em><\/strong><\/h3>\n
1.1- Curiosity<\/em><\/h4>\n
![\"Curiosity](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Curiosity-data-science-200x300.jpeg\")
<\/p>\n1.2- Problem-solving instinct<\/em><\/h4>\n
![\"Problem-solving](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Problem-solving-instinct-data-science-200x300.jpeg\")
<\/p>\n1.3- Statistical and mathematical knowledge<\/em><\/h4>\n
![\"Statistical](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Statistical-and-mathematical-knowledge-data-science-300x205.jpeg\")
<\/p>\n2- Technical skills<\/em><\/strong><\/h3>\n
![\"Technical](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Technical-skills-data-science-300x200.jpeg\")
<\/p>\n2.1- Python<\/em><\/h4>\n
![\"Python](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Python-data-science-300x169.jpg\")
<\/p>\n2.2- R<\/em><\/h4>\n
![\"R.](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/R.-data-science.jpg\")
<\/p>\n2.3- SQL<\/em><\/h4>\n
![\"Structured](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Structured-Query-Language-data-science-300x157.jpg\")
<\/p>\n2.4- Hadoop<\/em><\/h4>\n
![\"Hadoop](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Hadoop-data-science-300x107.png\")
<\/p>\n2.5- Apache Spark<\/em><\/h4>\n
![\"Apache](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Apache-Spark-data-science-300x160.jpg\")
<\/p>\n2.6- Tableau<\/em><\/h4>\n
![\"Tableau](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Tableau-data-science-300x168.jpg\")
<\/h4>\n2.7- Unstructured data<\/em><\/h4>\n
![\"Unstructured](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Unstructured-data-data-science-300x200.jpeg\")
<\/p>\n2.8- Machine learning and deep learning<\/em><\/h4>\n
![\"Machine](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Machine-learning-and-deep-learning-300x265.jpg\")
<\/p>\n3- Non-technical skills<\/em><\/strong><\/h3>\n
![\"Non-technical](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Non-technical-skills-data-science-300x200.jpg\")
<\/p>\n3.1- Robust business acumen<\/em><\/h4>\n
![\"Robust](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Robust-business-acumen-data-science-300x200.jpeg\")
<\/p>\n3.2- Strong communication skills<\/em><\/h4>\n
![\"Strong](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Strong-communication-skills-data-science-300x287.jpeg\")
<\/p>\n3.3- Good teamwork<\/em><\/h4>\n
![\"Good](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Good-teamwork-data-science-300x200.jpeg\")
<\/p>\nFinal takeaway<\/em><\/strong><\/h3>\n
![\"final\"](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/final-blockchain-300x225.jpg\")
<\/p>\n