View Post<\/a><\/p>\n\n\n\nAn advantage of learning to code in Python is that it\nis an Object-Oriented Programming language which, in layman terms, means that\nobjects are considered data structures containing their own customized\nfunctions. This allows programmers to reduce complexity in code and make\nchanges without crashing the program. In the course, we learnt to make objects\naround a Point-of-sale System, with methods to add\/remove products in a\ncustomer\u2019s cart and a method to shop products, while on the back-end this acted\nas an inventory management system. Object orientation can also be adapted to\napplications in other fields such as Finance and Supply-Chain management \u2013\nvirtually speaking, the possibilities are endless. <\/p>\n\n\n\n
Python boasts great integration capabilities with\nother programming languages such as C, C++, Java and even parse HTML using a\nlibrary called BeautifulSoup. It features data structures such as tuples,\ndictionaries and lists which are particularly useful in interpreting data-interchange\nformats like XML and JSON. In the course, we learnt how to interface MongoDB,\nan online database which stores data in JSON, using Python commands. This can\nbe used to conveniently store and retrieve records from online servers. <\/p>\n\n\n\n
Another impressive feature is how fluently Python\nintegrates various API services such as Google Maps, YouTube, and Twitter among\nmany others. Along with extensive support libraries, this feature can be used\nto derive meaningful data that aids business decision-making. In the course, we\nlearnt how to analyze comments from a YouTube video using polarity-based\nsentiment analysis. This was conducted through a Python library called VADER\nwhich uses natural language processing (NLP) techniques to gauge sentiments\nfrom social media text. Unlike more advanced NLP techniques, VADER is\nrelatively simple to use and caters social media lingo like emojis and\nrepetitive letters. Using this technique, for example, a business can judge how\nits latest video campaign is perceived on a scale of -1 to 1, and\ncorrespondingly make better decisions for the future.<\/p>\n\n\n\n
In the last project of the course, we were introduced\nto machine-learning algorithms in regression modeling. The idea was to train\nthe model on the \u2018train data\u2019, validate its parameters and then test its\nefficacy on the \u2018test data\u2019 with added hyper-parameterization. The project task\nwas to predict the price of a car using a database of features and car prices.\nMachine-learning techniques such as Lasso, Decision Tree, Random Forest, Ada\nBoost and Gradient Boost were used to find the best performing model which had\nthe highest overall R-square. <\/p>\n\n\n\n
Python for Business Intelligence turned out to be one of the most enriching experiences of my journey at Hanken! Not only has it opened new avenues for my research, but it has given me a hands-on experience of how businesses can leverage Python to make data-driven decisions in the fast-paced world of today.<\/p>\n\n\n\n
If you have any questions on studies or life at Hanken just let me know at sohaib.ahmed@student.hanken.fi <\/p>\n","protected":false},"excerpt":{"rendered":"
Hello everyone! I hope you all had a nice, sunny summer despite the rather upsetting situation at hand. Today, I am going to share one of the exciting things I learnt during the summer \u2013 coding in Python and its application in business! Python for Business Intelligence is an introductory course in Python, part of … <\/p>\n
Continue reading “Summer Course Experience – Python for Business Intelligence”<\/span><\/a><\/p>\n","protected":false},"author":14,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[7,18,17],"class_list":["post-606","post","type-post","status-publish","format-standard","hentry","category-nb-blog","tag-economics","tag-online","tag-python","entry"],"_links":{"self":[{"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/posts\/606","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/comments?post=606"}],"version-history":[{"count":3,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/posts\/606\/revisions"}],"predecessor-version":[{"id":676,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/posts\/606\/revisions\/676"}],"wp:attachment":[{"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/media?parent=606"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/categories?post=606"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.hanken.fi\/nordicbrilliance\/wp-json\/wp\/v2\/tags?post=606"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}