{"id":12146,"date":"2019-03-12T13:22:50","date_gmt":"2019-03-12T07:52:50","guid":{"rendered":"https:\/\/www.reachivy.com\/?post_type=post&p=12146"},"modified":"2024-05-13T19:04:17","modified_gmt":"2024-05-13T13:34:17","slug":"make-a-successful-career-in-data-science-5-starter-tips-for-students","status":"publish","type":"post","link":"https:\/\/www.reachivy.com\/blog\/make-a-successful-career-in-data-science-5-starter-tips-for-students","title":{"rendered":"Career in Data Science: 5 Tips for Students"},"content":{"rendered":"Reading Time: <\/span> 3<\/span> minutes<\/span><\/span>
\n

\"Data
\n\u201cData is everywhere\u201d is a modern business place version of the otherwise religious phrase, \u201cGod is everywhere\u201d. At the rate that data is growing, it is predicted that by 2020, the accumulated volume of big data will increase from 4.4 zettabytes to 44 zettabytes \u2013 about tenfold. With these numbers and a healthy growth rate, it is slated to become the next business place God, as big companies scramble to hire data scientists and analysts.
\nAt this point, it might seem lucrative for you to opt for a career in the Data Science industry and maybe tap into this growth. With that thought fresh in your head, you must be wondering how to get started.<\/p>\n

Tip for Starting Out and Building a Successful Career in Data Science<\/strong><\/h2>\n
    \n
  1. Pick a Language or a Tool (SQL, Python, R, etc.)<\/strong><\/strong>Picking a language or a tool is an absolutely necessary step. This is because your work primarily revolves around these! For beginners, it would be prudent to pick up one of the mainstream languages\/tools (SQL, Python, and R among others) and start there as they build their way up. While you\u2019re learning, do not waver or go from language to language \u2013 give it time so you can truly master it. Data science, like any other profession, needs you to have the patience to ensure success and steady growth.<\/li>\n
  2. Pick A Role and Complete A Course<\/strong><\/strong><\/strong>Clich\u00e9, right? For good reason, however. To get in on any industry, you need to first know what role you are going to play there, specific to your particular skill set. Being a generalist is neither the most desirable quality in the industry nor is it quite possible for you. Contrary to the layman\u2019s belief, a Data Scientist is not the only role you can take up in this industry. There is the Data Analyst, Data Architect, Machine Learning Expert and so on.<\/span><\/span>
    \nOnce you have researched and picked a role that might suit you, the next natural step would be to go on and specialize in that. How? Complete a course that will put you on track and equip you with the knowledge, skills and qualification.<\/span><\/li>\n
  3. Do More Than Just Your Homework Projects<\/strong><\/strong><\/strong>While you are out there aspiring to be a data scientist, it is easy to fall into the trap of feeling competent by merely completing the requisite homework projects your coursework demands. However, these do not count for anything come interview time. To set yourself apart from just any other run-of-the-mill Coursera graduate, it would be wise to take up some of your own projects and work on real data sets. This will not only set you apart but also expose you to the real-world problems that are tackled and answered by those working in Data Science.<\/span><\/span>
    \n“It is important to not get overwhelmed by the challenge of starting down this new road. Do one thing at a time, go as far as you can, and you will be able to see further.”<\/span><\/li>\n
  4. Follow the Right Sources and Build Your Knowledge Base<\/strong><\/strong><\/strong>To be successful in any field, the first step is to be proficient in it. To attain proficiency in any field, it is important to know what is going on. To know what is going on, you need to follow the right people and the right sources \u2013 maybe look up some popular\/successful data scientists, follow data science blogs and reading sources such as KDNuggetsNews. This brings you up to speed with regards to all that has, is and will happen in the world of data science.<\/span><\/span><\/li>\n
  5. Network Within the Field<\/strong><\/strong><\/strong>While proficiency counts, it is also important to know people. Attend industry events, make friends and make a start \u2013 even if you are not proficient yet. Be it Data Science or Building Parks \u2013 you need to know the right people to learn from and be assisted by in times of need.<\/span><\/span>\n


    \nIt is important to not get overwhelmed by the challenge of starting down this new road. Do one thing at a time, go as far as you can, and you will be able to see further.<\/span><\/p>\n

    ReachIvy<\/em><\/a>\u00a0<\/span>\u00a0sincerely hopes that this article serves as a critical tool to increase your knowledge base. For\u00a0study abroad consultation<\/a>\u00a0<\/em>\u00a0<\/span>or\u00a0career counselling<\/a>\u00a0<\/em>\u00a0<\/span>with ReachIvy,\u00a0<\/i>Submit<\/a> <\/span>a Query\u00a0now! Also, review our\u00a0resources<\/a><\/em>\u00a0<\/span>\u00a0to access our free premium content<\/em><\/p>\n<\/li>\n<\/ol>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Reading Time: <\/span> 3<\/span> minutes<\/span><\/span>\u201cData is everywhere\u201d is a modern business place version of the otherwise religious phrase, \u201cGod is everywhere\u201d. At the rate that data is growing, it is predicted that by 2020, the accumulated volume of big data will increase from 4.4 zettabytes to 44 zettabytes \u2013 about tenfold. With these numbers and a healthy growth rate,…<\/p>\n","protected":false},"author":7,"featured_media":16948,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"categories":[298,441,457],"tags":[],"class_list":["post-12146","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-career-guidance","category-soft-skills","category-technology"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/posts\/12146","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/comments?post=12146"}],"version-history":[{"count":5,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/posts\/12146\/revisions"}],"predecessor-version":[{"id":16949,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/posts\/12146\/revisions\/16949"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/media\/16948"}],"wp:attachment":[{"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/media?parent=12146"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/categories?post=12146"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.reachivy.com\/blog\/wp-json\/wp\/v2\/tags?post=12146"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}