But is it actually possible to become a\u00a0data scientist<\/strong>\u00a0for anyone?<\/span> Of course, one can learn some tools used in the data science field and call himself or herself a\u00a0data scientist<\/strong>, but that\u2019s actually far from the truth. However, it\u2019s possible for everyone to become a\u00a0data scientist<\/strong>\u00a0these days, indeed with a robust plan. In this post, we\u2019re going to discuss the\u00a0data scientist<\/strong>\u00a0learning path following which anyone can become a part of this sophisticated, smart and glamorous league of professionals.<\/p>\n Before<\/em> delving deeper into the learning path, let\u2019s have a quick look at what a data scientist<\/strong> actually does. Data science is a complex field and it involves lots of different skills that contribute toward making the position even more important. In its simplest form, a data scientist<\/strong><\/em><\/a> can be considered as someone who has the ability of capturing and analyzing a massive amount of data in order to reach a conclusion.<\/span> They perform this through different high-end tools and techniques. Essentially, a data scientist<\/strong> looks for meaning in huge amounts of data.<\/p>\n The key reason behind the emergence of the data scientist<\/strong> role is the need to understand the usually messy and huge amounts of data captured by fast-growing companies. These companies are trying to glean actionable insights about their business as well as customers with this data, but the professionals needed to perform this task are in short supply.<\/p>\n Another factor that contributed heavily toward increasing the demand for data scientists<\/strong> is that business leaders of today not only just want to know what happened, but they also want to know what\u2019s happening, what\u2019ll happen in the future, and how will it impact their business operations.<\/p>\n As<\/em> we\u2019ve already discussed that it\u2019s almost impossible for companies to manipulate and make sense of the data they capture on their own. As a result, many organizations are more than willing to pay an attractive salary for a good data scientist<\/strong>. With an excellent number of high-paying job opportunities, data science has become the field to be in at the moment. The data science field is growing and it\u2019ll continue to do so for the foreseeable future.<\/p>\n Whether you\u2019re a working professional looking to step into the field, a student planning for your future or are belong to a different background like the non-coders<\/strong> league, there\u2019re ways to become a data scientist<\/strong> and it\u2019s never too late to start your journey.<\/span> Regardless of your present exposure to data science, here\u2019re the skills you need to have to succeed in your endeavor.<\/p>\n Mathematics<\/em> is a subject of which lots of people are scared of, but if you want to be a successful data scientist<\/strong>, you\u2019ve to get your concepts cleared on things like probability, linear algebra etc. Put simply, probability refers to the measure of how likely something is going to happen.<\/p>\n In the data science field, there\u2019re lots of events that cannot be predicted with complete certainty. So, concepts like Bayes Theorem, probability distribution etc are much needed to perform data science. Linear algebra deals with vector spaces. It\u2019s crucial to understand different ideas behind different techniques of linear algebra like Time Series, Clustering, among others to understand their applicability.<\/p>\n Statistics is a crucial part of analyzing and interpreting the data. A lot of statistical concepts are used to perform data science, so a good understanding of them is essential.<\/p>\n More<\/em> and more employers of data scientists<\/strong> are looking for candidates who\u2019re conversant with programming languages like Python<\/strong><\/em><\/a>, R, Java etc. A good understanding of these languages is a must to succeed as a data scientist<\/strong>. You should understand that this isn\u2019t about being an excellent coder but it\u2019s all about being comfortable with different programming environments to be able to work with data as and when required. If you can demonstrate the expertise to adapt to the changes in the technological landscape, it\u2019ll surely be considered as a good advantage.<\/p>\n It\u2019s<\/em> a field that provides computers with the ability to make decisions based on earlier data or previous experience. It\u2019s a group of algorithms that use machine power to derive insights for you. To become a good data scientist<\/strong><\/em><\/a>, you should have a good understanding of neural networks, adversarial learning, reinforcement learning, supervised machine learning, logistic regression, decision trees, among others. In the<\/span> data science field, different machine learning<\/strong><\/em><\/a> skills are used to perform different activities. So, it\u2019s wise to be familiar with them.<\/p>\n Once<\/em> you\u2019ve done working with your data analysis, you\u2019ll need to convince others to adopt your insights. Being visual creatures, it\u2019s typically much easier for humans to consume the information by examining a graph or chart than by going through the numbers.<\/p>\n As a data scientist<\/strong>, you\u2019ve to be able to visualize data with the help of data visualization tools like Tableau, ggplot, D3.js etc. These tools help you to convert complicated results from your findings to an easily consumable format. With data visualizations, organizations can grasp insights quickly to act on different business opportunities.<\/p>\n An<\/em> overall analytical mindset is required to do well as a data scientist<\/strong>.<\/span> Essentially, these people need to spend a huge percentage of their time in discovering and preparing data. So, as a part of that league, you\u2019ve to be able to raise questions about data. Keep on updating your knowledge by reading relevant resources to be able to channel your thinking in the right direction.<\/p>\n As<\/em> a matter of fact, most organizations that work with data depend on their data scientists<\/strong> not only to mine huge datasets but also to communicate the insights to decision makers. An effective data scientist should not only come with the ability to work with complex, massive datasets but with the understanding of the intricacies of the business he\/she works for.<\/span><\/p>\n Having good business knowledge allows him\/her to ask the right questions and come up with actionable solutions which are actually feasible for the business. In the context of data science<\/strong><\/em><\/a>, being able to understand which problems are crucial to solving for the business plays an extremely important role.<\/p>\n There\u2019re<\/em> different ways to become a data scientist<\/strong>, but it\u2019s completely impossible to become one without a college education. At the very least, you\u2019ll need a Bachelor\u2019s degree to pursue further study. Also, if your goal is to land a leadership position, you should try to earn a Master\u2019s degree or Ph.D.<\/p>\n There\u2019re three main ways to become a data scientist \u2013 the traditional way, self-learning, and by attending a reputed school (like Magnimind Academy<\/strong><\/em><\/a>) that offers data science prep course<\/strong> and data science bootcamp<\/strong>. If you look at the traditional way, it may not be feasible for everyone to go back to school to complete a Master\u2019s degree, both in terms of time and increasing educational cost.<\/p>\n If you consider the self-learning method, you can obviously learn many things but one of the major drawbacks of being self-taught is that your knowledge may not be complete and you may not be aware of that. Also, you won\u2019t be able to measure your learning progress through this method.<\/p>\n Coming to the third option, institutes that offer data science degrees have become quite an obvious choice to aspiring data scientists<\/strong>. Even if you\u2019re coming from a different background, the non-coders<\/strong> group, for example, data science prep course<\/strong>s offered by these schools are sufficient enough to provide you with the necessary skills based on which you can move forward to attend a data science bootcamp<\/strong><\/em><\/a>. Another major advantage of attending these schools is that they let you step into the field in a much shorter span of time (usually data science bootcamps come with the duration of 6-12 weeks) and around the one-fifth cost of attending a 2-year Master\u2019s program. So, if you have the basics right like having a college degree, analytical bend of mind and mindset to put your best effort in, you can surely become a data scientist<\/strong> by attending one of these institutes.<\/p>\n Regardless<\/em> of the path you prefer to take to become a data scientist<\/strong>, it\u2019s always crucial to keep some things in mind. For example, finding a mentor, working on increasing your network, visiting data science conferences, meetups etc play important roles in establishing yourself in the industry.<\/span><\/p>\n In addition, as different technologies will come and go in the field, it\u2019s important that you keep on learning continuously about new tools and technologies to stay on the same page with industry trends and remain in demand.<\/p>\n1- What\u2019s a data scientist?<\/em><\/strong><\/h3>\n
![\"What\u2019s](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/What\u2019s-a-data-scientist-300x131.png\")
<\/p>\n2- How can anyone become a data scientist?<\/em><\/strong><\/h3>\n
![\"How](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/How-can-anyone-become-a-data-scientist.png\")
<\/p>\n2.1- Mathematics and statistics<\/em><\/h4>\n
![\"Mathematics](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Mathematics-and-statistics-data-science.jpg\")
<\/p>\n2.2- Coding<\/em><\/h4>\n
![\"Coding](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Coding-data-science.jpg\")
<\/p>\n2.3- Machine learning<\/em><\/h4>\n
![\"Machine](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Machine-learning.jpg\")
<\/p>\n2.4- Data visualization<\/em><\/h4>\n
![\"Data](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Data-visualization.jpg\")
<\/p>\n2.5- Analytical mindset<\/em><\/h4>\n
![\"Analytical](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Analytical-mindset-data-science.jpg\")
<\/p>\n2.6- Strong business acumen<\/em><\/h4>\n
![\"Strong](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Strong-business-acumen-data-science.jpg\")
<\/p>\n3- Different ways to become a data scientist<\/em><\/strong><\/h3>\n
![\"Different](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Different-ways-to-become-a-data-scientist.png\")
<\/p>\nIn conclusion<\/em><\/strong><\/h3>\n
![\"conclusion\"](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/04\/conclusion-225x300.jpg\")
<\/p>\n