In reality, finding a \u201cgood\u201d data scientist is difficult. Note the use of \u201cgood\u201d here which is used to highlight the fact that there can be people who may possess some of the skills related to data science but may not be the ideal fit in the role of a data scientist.<\/strong><\/em><\/a><\/span> And the irony is that even the employers looking to hire data scientists sometimes may not fully understand data science.<\/p>\n There\u2019re still some job advertisements that describe a business analyst role and a traditional data analyst<\/strong><\/em><\/a> role while marking it as a \u201cdata scientist\u201d position. In this post, we\u2019re going to help you out with regard to data science essentials<\/strong><\/em><\/a>, so that you can obtain the right skills to become a data scientist.<\/p>\n Put<\/em> simply, data science refers to the process of capturing data, analyzing it, and obtaining actionable insights from it.<\/span> The key demand of this profession is to have a lot of varied skills in order to become a coveted data scientist. Let\u2019s have a look at them.<\/p>\n The<\/em> first element in this list of data science essentials<\/strong> is standard education, if not higher. You should note that a huge percentage of data scientists come with a Master\u2019s degree and a significant percentage of them have Ph.Ds. Though there may be notable exceptions, usually a strong educational background is required to develop the required depth of knowledge necessary to become a good data scientist<\/strong><\/em><\/a>.<\/strong><\/em><\/span><\/p>\n So, ideally, you should have a Bachelor\u2019s degree in statistics, mathematics, computer science etc. A majority of aspiring data scientists earn a Master\u2019s degree or a Ph.D. after that apart from undertaking different online courses to develop special skills.<\/p>\n Programming<\/em> skill is the second most important element among data science essentials<\/strong>. Regardless of the company or industry you\u2019re interviewing for, you\u2019re most likely going to be presumed to have a good understanding of one or more programming languages.<\/p>\n Python<\/strong> <\/em><\/a>is the most widely used programming language required for data scientist roles.<\/span> Because of its versatility, Python is involved in almost every step of the data science processes. You\u2019re also expected to have good knowledge of writing and executing complex queries in SQL (Structured Query Language).<\/p>\n Good<\/em> grasp of statistical skills is crucial for becoming a good data scientist.<\/span> You must have an idea of statistical tests, distributions, and maximum likelihood estimators, among others. While the knowledge of statistics is essential regardless of the company type, it becomes extremely crucial for data-driven organizations where stakeholders rely on the support of data scientists to make business decisions.<\/p>\n No<\/em> data science essential<\/strong> compilation can be complete without mentioning machine learning as one of its elements. Unfortunately, a significant percentage of data scientists aren\u2019t proficient in machine learning<\/em> <\/strong><\/a>techniques and areas like reinforcement learning, neural networks etc.<\/p>\n If you want to be a true data scientist and stand out of the pack, you must know different machine learning techniques like decision trees, supervised machine learning, logistic regression etc. Having expertise in these skills will help you solve various data science problems, which in turn can help in the prediction of crucial organizational outcomes.<\/p>\n Data<\/em> visualization is another key element of data science essentials<\/strong>.<\/span> In reality, a huge amount of data is being generated by the business world, which needs to be translated into an easily consumable format. As a data scientist, you must have a solid understanding of how to visualize data using data visualization tools like Tableau, ggplot etc.<\/p>\n These tools will help you translate complex results from the projects to a format, which will be easy to consume. Data visualization offers businesses the opportunity to directly work with data, grasp insights quickly, and act on new business opportunities to rise above the competition.<\/p>\n Often<\/em> known as data munging, data wrangling refers to the process of mapping and converting data from the form of a single raw data into different formats.<\/span> Usually, the data analyzed by data scientists is quite challenging to work with and is messy in nature. Some of the imperfections often found in data include missing values, inconsistent string formatting etc.<\/p>\n You need to understand that data is foundational to machine learning<\/em>. So, if the data is dirty and\/or contains unreliable or meaningless information, the algorithms will not be able to derive any valuable pattern. If the data isn\u2019t cleaned as well as prepared the right way, the machine learning models may start making wrong decisions that can severely affect the business\u2019s revenue. And that\u2019s why data wrangling has been incorporated in this list of data science essentials<\/strong>.<\/p>\n Another<\/em> crucial element that features on the list of data science essential<\/strong>s is having a robust understanding of the sector you\u2019re working in to understand what business problems your organization is trying to solve. When it comes to data science, being able to discern problems that are crucial to solve for the progress of the business is immensely important.<\/p>\n Communication<\/em> skill is one of those data science essentials<\/strong> that can distinguish a good data scientist from an average one<\/span>. As a data scientist, you need to have the ability to report your technical findings with the ultimate goal that they\u2019re comprehensible to your non-specialized partners, who can be anything from members of the decision making team to those in the sales and\/or marketing team. You must make the data-driven story convincing enough while communicating it properly to your audience.<\/p>\n While<\/em> the above ones are the effective elements that form a list of data science essentials<\/strong>, there\u2019re some other things that you must keep in mind when you\u2019re trying to develop these. Let\u2019s have a look at the following thoughts.<\/p>\n If<\/em> you don\u2019t have your fundamentals right, it\u2019ll be almost impossible to become a good data scientist.<\/span> From mathematics to statistics to programming language, and everything in-between \u2013 whatever skills you develop, gain a solid understanding of the basics at first.<\/p>\n The frontline of this field is encountering new challenges on a regular basis and mastering only some high-end concepts isn\u2019t going to give you a competitive edge. With just a handful of people who\u2019re not willing to take the shortcuts, such investment to develop a robust foundation has become extremely critical.<\/p>\n Try<\/em> to become really good at what you\u2019re doing. As a result, you\u2019ll be able to establish a robust knowledge framework across domains. Though it\u2019ll still take a lot of time and effort, the difficulty level will be lower when it comes to transferring the learning into an entirely new domain.<\/p>\n Such transfer can go on as you hop across domains and your knowledge coverage will exponentially multiply. In order to reach that level, you must be an expert, which is an actually difficult task. Remember that great data scientists are able to handle almost any challenge.<\/span> They\u2019re fast learners and are excellent at mapping unrelated challenges into their own domain of expertise to resolve them with ease.<\/p>\n This<\/em> is something that you may not be able to develop even if you master all the elements mentioned in the above data science essential<\/strong> list. At their core, good data scientists are good problem solvers. You can solve a well-formed problem with ease because you can get plenty of training and you know how to do it.<\/p>\n But if we ask you why you should solve the problem, giving the right answer may seem to like a challenge because there\u2019re not many places that train you for it. In this scenario, self-exercise would probably be the best option. For example, you can pick a topic randomly once in a while and try to think of a solution in your own way. It\u2019ll not only keep your mind active but nurture your problem-solving skills too while making you more curious.<\/p>\n As<\/em> an aspiring data scientist, you must try to develop all the skills mentioned in the above list of data science essentials<\/strong>. But even before you start your journey toward developing these skills, you should ask yourself whether you\u2019ve got the right passion to become a data scientist.<\/span> You need to understand that if you aren\u2019t curious about data and\/or aren\u2019t passionate about business, it\u2019ll become extremely difficult for you to accomplish your end goal of becoming a data scientist. Put simply, if your curiosity doesn\u2019t interest you, probably no skill can help you succeed. <\/span><\/p>\n And finally, data science is a rapidly changing field and you\u2019ve to adapt and improve constantly. That being said, you should remember that the crucial elements of a data science essential<\/strong> list today may change tomorrow. You\u2019ve to be prepared to embrace uncertainty and that\u2019s probably the biggest essence of embracing the role of a data scientist.<\/strong><\/em><\/a><\/p>\n1- Data science essentials that you need to master<\/em><\/strong><\/h3>\n
![\"Data](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Data-science-essentials-that-you-need-to-master-300x200.png\")
<\/p>\n1.1- Education<\/em><\/h4>\n
![\"Education](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Education-data-science-300x193.jpg\")
<\/p>\n1.2- Programming skills<\/em><\/h4>\n
![\"Programming](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Programming-skills-data-science-300x200.jpg\")
<\/p>\n1.3- Statistical skills<\/em><\/h4>\n
![\"Statistical](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Statistical-skills-data-science-300x218.png\")
<\/p>\n1.4- Machine learning<\/em><\/h4>\n
![\"Machine](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Machine-learning-300x150.png\")
<\/p>\n1.5- Data visualization<\/em><\/h4>\n
![\"Data](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Data-visualization-300x200.jpg\")
<\/p>\n1.6- Data wrangling<\/em><\/h4>\n
![\"Data](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Data-wrangling-300x200.jpg\")
<\/p>\n1.7- Robust business acumen<\/em><\/h4>\n
![\"Robust](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Robust-business-acumen-data-science-300x200.jpg\")
<\/p>\n1.8- Excellent communication skills<\/em><\/h4>\n
![\"Excellent](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Excellent-communication-skills-data-science-300x200.jpg\")
<\/p>\n2- Some crucial things to keep in mind<\/em><\/strong><\/h3>\n
![\"Some](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Some-crucial-things-to-keep-in-mind-data-science-300x212.jpg\")
<\/p>\n2.1- Become a master of fundamentals<\/em><\/h4>\n
![\"Become](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Become-a-master-of-fundamentals-data-science-150x300.png\")
<\/p>\n2.2- Earn expertise<\/em><\/h4>\n
![\"Earn](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Earn-expertise-data-science-300x200.jpg\")
<\/p>\n2.3- Develop problem-solving skills<\/em><\/h4>\n
![\"Develop](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/Develop-problem-solving-skills-data-science-300x169.jpg\")
<\/p>\n3- Wrapping up<\/em><\/strong><\/h3>\n
![\"wrapp](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/wrapp-data-science-300x157.jpg\")
<\/p>\n