{"id":1484,"date":"2021-05-16T18:00:41","date_gmt":"2021-05-16T18:00:41","guid":{"rendered":"http:\/\/www.nusbizadclub.com\/bizcare\/?p=1484"},"modified":"2021-05-17T13:30:00","modified_gmt":"2021-05-17T13:30:00","slug":"business-analytics-internal-networking-webinar-10-march-2021","status":"publish","type":"post","link":"http:\/\/www.nusbizadclub.com\/bizcare\/ay20-21-panel-discussion\/business-analytics-internal-networking-webinar-10-march-2021\/","title":{"rendered":"Business Analytics Panel Discussion Webinar 10 March 2021 Recap"},"content":{"rendered":"\n<p>On 10<sup>th<\/sup> March, we had connected on Zoom with 3 guest speakers from DBS, Accenture and Lynx\nAnalytics who gave us insights about the data science industry and their lives\nas a business analyst.<\/p>\n\n\n\n<p>We learned some insightful pointers from Andrea Woo\n(Analytics translator at DBS), Richard Kang (Data Scientist at Lynx Analytics)\nand Oscar Sia (Business Integration Arch Analyst from Accenture) about the\nroles each of them held. <\/p>\n\n\n\n<p><strong>Sharing session pointers:<\/strong><\/p>\n\n\n\n<p>Beginning with who is an Analytics translator, they are the\nones who interface between the business and data scientists. They turn the\noutput from analytics models into business actions by interacting between\nfunctions and the analytics organization. <\/p>\n\n\n\n<p>Furthermore, the day-to-day activities of an Analytics\ntranslator involves: <\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Designing for data i.e. data instrumentation \u2013\ndefining success metrics and knowing what data points to collect and ensuring\nthat the necessary instruments are placed within the system.<\/li><li>Bringing speed to insights with dashboards \u2013\ndesigning prototyped and developed dashboards for monitoring purposes and\ncreating self-service capabilities within dashboards for users to access data\nreadily to derive the right insights.<\/li><li>Weaving analytics into operations \u2013\nunderstanding business requirements and identifying opportunities to introduce\nanalytical solutions for process improvements. <\/li><\/ul>\n\n\n\n<p>Some of the job scope also involves doing a lot of\nprototyping and helping to improve the infrastructure of clients. One works on\ncase creations for the client and building one\u2019s own cases, apart from a bit of\ndata engineering as well.<\/p>\n\n\n\n<p><strong>Some key insights from the sharing:<\/strong><\/p>\n\n\n\n<p>&nbsp;\u201cData does provide\nthe wrong solution from time and time. Understanding context and finding the\n\u2018right\u2019 insight is critical.\u201d &#8211; Andrea<\/p>\n\n\n\n<p>\u201cWe are quick to assume data modelling would be more of what\nwe do but actually a lot of time is spent mainly in trying to get the right\ndata.\u201d &#8211; Richard<\/p>\n\n\n\n<p>\u201cOne problem faced is that as a data scientist, not\neverything you do will involve data science. Recently there is a hype is over\nsocial networks and frauds. That has very little to do with data science\nitself. Actually, it involved a lot of application creation. I had to do a lot\nof front-end work as well, using Java Script, HTML etc, which was one of the\ntough things. To be going from end to end, which is what is we do at Accenture was\none of the challenges I faced.\u201d&nbsp; &#8211; Oscar<\/p>\n\n\n\n<p>\u201cData science is not just about the data (the math and stat\npart) but it is the whole infrastructure. \u201c &#8211; Oscar<\/p>\n\n\n\n<p><strong>QnA:<\/strong><\/p>\n\n\n\n<p><strong>Is it difficult for a business student with no tech background to take on a tech role?<\/strong><\/p>\n\n\n\n<p>Richard: Doesn\u2019t matter if you have the background or not.\nIt\u2019s about whether you are willing to learn. One colleague was actually from a\npsychology background so it is definitely possible to come into the field\nwithout a tech background.<\/p>\n\n\n\n<p>Oscar: Take one step at a time. Instead of entering a direct\nengineering role as a business student, maybe you can take a data science\napplication role, focusing on business and new case creation first. <\/p>\n\n\n\n<p>Do I need a masters in data science to be a data scientist?<\/p>\n\n\n\n<p>Richard: Don\u2019t need a masters in data science. I don\u2019t have\none. Most colleagues who have masters are ones who don\u2019t have a bachelors in\nit, so taking masters in it helps them pivot into the data science field.<\/p>\n\n\n\n<p>Oscar: Even though you don\u2019t need it, a masters in data\nscience will definitely be helpful. Theory-wise you actually learn a lot. It\ncan be sometimes too much to learn on your own and you might not know where to\nstart so doing a masters will be really helpful.<\/p>\n\n\n\n<p>Andrea: Apart from what is already said, there are also many\nresources online these days to learn from or use supplementarily, such as\nDataCamp which is a learning platform for learning to code with Python. Some\npeople I knew who&nbsp; had a background in\narts even converted to data science with the help of these online courses. <\/p>\n\n\n\n<p><strong>What programming languages are essential and how proficient must I be to work in the industry?<\/strong><\/p>\n\n\n\n<p>All: Really important ones include Python and R. SQL is\nimportant to know as well. SQL is easier to learn on the job compared to Python\nand R which has online learning courses out there for you to pick up.<\/p>\n\n\n\n<p><strong>How was your recruitment process?<\/strong><\/p>\n\n\n\n<p>Richard: Very technical recruitment process. One experience had\na free style and no fixed structure. A few data scientists came in to ask \u201cwhat\nyou are good at\u201d because the data science field is quite broad as well. The\ninterview itself was quite technical, where I was given a sample problem and\nasked to describe the process that I would take to achieve the required\nresults.<\/p>\n\n\n\n<p><strong>How does one showcase their data science skills?<\/strong><\/p>\n\n\n\n<p>Richard: Traditionally people would say Kaggle and Github,\nbut a lot of times you work with very clean data and that\u2019s not reflective of\nthe real world. Best way to show your data science skills is to build up on\nyour technical language and know what you\u2019re talking about.<\/p>\n\n\n\n<p><strong>Any Internship-securing advice?<\/strong><\/p>\n\n\n\n<p>Oscar: Work on yourself \u2013 best advice as a student. As an\nintern, people also want you to learn and look at you as a person. I was in NUS\nstatistics society as Vice president and part of Conjunct consulting. It helps\nto get internship offer. <\/p>\n\n\n\n<p>Richard: Alternatively, you can go for hackathons and\ncompetitions. Hackathons are also very good on your resume.<\/p>\n\n\n\n<p>Andrea: You can also start on personal projects if none of\nthe above work for you. One of the interview questions is about any interesting\npersonal projects you have worked on. Another way is to work your way to your\ndesired role through networking \u2013 starting off in a role that may not be as\ndesired but in company you want and then use networking to work to where you\nwant to be eventually. <\/p>\n\n\n\n<p>Overall, the session was quite a learning experience in\nfinding out what goes in the data science industry and the different roles in\nit, along with touching upon key skills to work on to enter this field. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>On 10th March, we had connected on Zoom with 3 guest speakers from DBS, Accenture and Lynx Analytics who gave us insights about the data science industry and their lives as a business analyst. We learned some insightful pointers from Andrea Woo (Analytics translator at DBS), Richard Kang (Data Scientist at Lynx Analytics) and Oscar&hellip;&nbsp;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[38],"tags":[39,31,19],"class_list":["post-1484","post","type-post","status-publish","format-standard","hentry","category-ay20-21-panel-discussion","tag-ay20-21-panel-discussion","tag-ba","tag-business-analytics"],"_links":{"self":[{"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/posts\/1484","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/comments?post=1484"}],"version-history":[{"count":3,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/posts\/1484\/revisions"}],"predecessor-version":[{"id":1506,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/posts\/1484\/revisions\/1506"}],"wp:attachment":[{"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/media?parent=1484"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/categories?post=1484"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.nusbizadclub.com\/bizcare\/wp-json\/wp\/v2\/tags?post=1484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}