 He introduce to all of you about our speaker today which is Mr. Arthur Ken Raymond. Saya baca sikitlah payodata pasal beliau. He started programming back in 1986 and has been involved in the professional software industry in Kota Kinabalu since 1998. He is the co-founder of Persatuan Pengatur Cara dan Pembina ICT Sabah which is a local NGO more well known as Kinabalu Codas and he also call it the Rapsberry Pie and Computer Architecture communities and interested in PHP, Python, Big Data, the Internet of Things which is IOT and Computer Security. Professionally, he currently works in a local software company that known as Digital Heritage Sundarian Berhat and has been one of the architects behind some enterprise level state government projects and he is also an alumni of the International Visitor Leadership Program IVLP that organized by the US State Department during the month of September 2017. During the program, he had the opportunity to examine and understand many different forms of social entrepreneurship in the United States and communicate with his counterparts on technology-oriented approach at the individual, city and state level. So, hello, Mr Attur, how are you today? Hello, Czech City, Nurazila, I'm doing fine today. I hope you everybody is too. Okay, I'm good too. Thank you. And since it is a long journey which is since 1986 until today in tech industry I'm pretty sure that you have a good experience in it. So, can you share to us what kinds of things that yang kami perlu tahu terutamanya student yang dalam bidang IT ini? So, tanpa bau masa dipersilakan di saat untuk menyampaikan perkongsian beliau? Boleh. Terima kasih, Czech City lagi. So, hello ladies and gentlemen, selamat petang and welcome to this pembentangan. Pertama sekali, thank you very much also for University Malaysia Sabah sebab bagi chance juga bikin pembentangan mengenai data. So, tanpa melengahkan masa lagi so, saya share screen saya. So, ini presentation dia. So, sekiranya anda memerlukan salinan presentation ini ada QR code di bawah, jangan risau. Nanti boleh scan QR code, saya pun juga bagi link so, I'll provide the link for the presentation later also because this presentation ada banyak demia link dalam dia juga lah which you might want to look at. So, saya telah difahamkan that the audience hari ni mungkin terdiri daripada computer science, computing informatics ada juga social scientist dan mungkin school of business juga. So, this won't be so technical but it is about data culture and acquisition. So, data culture ni adalah pembudaian data kalau guna Bahasa Malaysia punya. So, pembudaian data ni is lebih kepada pemikiran dan konsep data. Dan juga, bukan juga just kaeda, methodology dan technical tapi pemikiran dia. How do you do, what do you do with that data? How do you deal with data acquisition and so on? And a lot of this boleh diguna pakai semasa FYP anda, semasa tulis tesis ka bikin tesis and also in the industry because as you will soon discover, actually tiada beza antara what you do in your FYP atau what you should be doing in your FYP and what you should be doing in the industry. So, tanpa melekat masa lagi just now, Miss City has already explained who I am so we won't go into here tapi sedikit sebanyak about digital heritage. Sebenarnya, digital heritage the company which I work for is a wholly owned subsidiary of Sabanet Sindirian Berhad which is a government link company di bawah Kerajaan Negeri Sabar dan Sabanet ni we handle 600 plus government sites di seluruh Sabar dan handle lebih kurang 34,000 government accounts tapi kami bukan bikin computer di pejabat-pejabat district office memang kami handle infrastructure atau jaringan network dia di Sabar tapi bukan bagi internet juga ini lebih kepada local area network demia wide area network dan sebagainya sekiranya ada sualan juga jangan takut jangan syai, jangan malu feel free to ask your questions in the chat atau selepas ni ada Q&A juga dan so jangan malulah kalau mau tanya sualan pasal syarikat ke, mau tanya pasal data dan sebagainya. So, let's talk about data first. So data is information about telling a story so tapi this story kan it has to be logical it has to be rational and it has to make sense kenapa? Sebab once you can tell that story that story will lead you to a conclusion so sebenarnya actually ini yang dikatakan data driven decision making mungkin pernah dengar ni banyak kali it's like we must make data driven decisions so the reality is about data is that they are memang ada matlamat dan objektif dia cerita, cerita itu harus rational tapi tujuan cerita itu dibina sebab dia dapat dia membolehkan kami atau kita bikin sesuatu keputusan dengan fakta-fakta dan sebagainya so itulah data so just understand that data is information about telling that story so these are the problems with data mungkin masalah-masalah ini dialami oleh FYP anda semasa pembikinan FYP anda atau juga in the industry so pertama sekali memang sentiasa ada data yang tiada you have missing data or the data is inconsistent so sekiranya anda membuat FYP anda and actually you find out oh saya kena experiment dan sebagainya sebab saya tak dapat cari data ini secara online then this is one of the first problems you will find secondly sometimes you find that data mungkin data tu ada di dalam agency lain dalam jabatan lain dan sebagainya tapi yang masalah dia bila kamu minta data tersebut dia orang bagi tau oh ini kena bayarlah atau oh ini kami tidak boleh share lah tak boleh congsi we cannot share with you so this is the other issue with data that we face today data tu kena silo from that silo sometimes when you do get the data then you find out that data tu is inaccurate atau bias tapi bila kami cakap perkataan bias ni jangan ingat bahawa dia bias secara tersengaja of course there are certain cases di mana data yang anda peroleh itu ada bias because they are on purpose kasi bias tetapi there is also di mana bias tu actually secara tidak tersengaja for example sekiranya anda bikin survey kamu sekiranya anda bikin survey anda ada orang yang belom lagi kena muted hei hei kasi mute tu jangan risau santai-santai sejauh kami sejauh tu mencapai ingin sejauh orangnya kan just in case oh ok sudah muted so just understand that sometimes data is bias but it's not on purpose for example and mungkin ini kes sebenar let's say i nak dapat data bagi b40 di sabar tapi when you think about getting data dari b40 di sabar mungkin your first thought is sepegi tengok agency kerajaan mungkin jabatan kebajikan and then dapatkan senarai daripada dia orang but when you get the senarai tu tak lengkap and then you realize also that biasanya orang yang dapat isi data untuk b40 dia orang ada nombor telefon ataupun dia ada handphone dan sebagainya tapi apa terjadi kepada b40 kalengan b40 yang tak ada telefon yang tak ada mobile number and then dia orang pun tinggal di kampunga yang kena ambil 6 jam keluar masuk guna jalan kaki setiap hari so this kind of bias is because infrastructure lemah ataupun mungkin tidak sempat dapat dia punya data daripada kalengan atau demografi ini selain daripada bias sudah tadi i also said sometimes the data is out of date you get the data but then you realize oh data ni 2019 lah tiada data baru so in this scenario you get outdated data finally and this one yang special sikit bagi sabarlah is Malaysia sometimes data tu ada website pun ada semua tapi tiada internet so without internet tak boleh access data or when you try and get up to date data suddenly you find out oh website error lah tidak boleh dapat data so inilah masalah-masalah yang dihadapi dalam data science so as part of your pemikiran as part pembudaian data you must realize biasanya anda tidak akan dapatkan data yang 100% lengkap dan 100% up to date and this is masalah yang dihadapi oleh keseluruhan orang dalam bidang data science tak kira kalau disabar di Malaysia atau di seluruh dunia so antara let's say data-data not to say data buruklah but data tidak lengkap let me introduce to you some of the problems with data for example saya rasa semua orang disini memang tahu spreadsheet dah guna microsoft excel google sheets liba office dan open office tapi saya rasa anda memang juga terlihat spreadsheet seperti mana yang anda lihat di depan kamu sekarang spreadsheet ini bukan digunakan untuk mengimpan data instead spreadsheet ini digunakan sebagai borang so kenapa kami menggunakan spreadsheet sebagai borang sebab senang ma formatting pun senang tidak payah left right adjust semua semua dalam barisan semua dalam column so yang masalah dia sekiranya spreadsheet digunakan untuk sebagai borang is that data ini senang dibaca oleh manusia tapi susah dibaca oleh mesin susah untuk dianalysakan sebab let's say anda melakukan borang seperti ini dalam syarikat yang kecil besarkah and so on this means that let's say ini invoice tapi let's say syarikat anda bikin 100 invoice maka to process this anda perlu melihat 100 file yang berbeza tak ada cara untuk baca dia dengan menggunakan mesin dengan senang so ini adalah satu contoh pengyala gunaan tools yang sedia ada dalam data science which means sebenarnya actually spreadsheet yang disalah gunakan dia digunakan sebagai borang antara let's say contoh-contoh lain semua ini macam ringkas saja tapi ini adalah masalah yang biasanya kami hadapi semasa kami bikin kerja kami di kerajaan negeri sebab bila kami pergi kerajaan negeri sometimes dia orang bagi tahu kami oh kami sudah ada data tapi bila kami tengok spreadsheet itu haa tekelualah semua dia punya mistik-mistik macam begini so you can see di depan anda ini adalah let's say satu senarai species ikut sungai ikut tahun tapi yang masalah dia bila dia simpan data dia biar tempat kosong yang kenal tanda kuning itu sebab dia ingat oh kalau manusia baca ni senang bah dia nampak osteochilus ni dibawa sungai liwagu bah, scleropages species pun dibawa sungai liwagu bah tapi mana boleh import ni dalam mesin mesin nampak ni tiada sungai so ini mistik pertama kena bersihkan data tu lebih baik kalau si pengimpan data atau data entry person actually isi ni sebelum ni dan let's say mistik number 2 ini also kami biasanya dapat dia orang suka guna merge fields so as you can see di kawasan yang ditanda kuning itu sepatutnya ada barisan yang menganumi nama sungai tapi dia kasih merge sejauh ini sakitlah kena unmerge semua data dia kemudian letak balik dalam system and then mistik number 3 dia kasih campur aduk because dia punya screen kecil so dia kasih campur aduk dia punya column, scientific name dan tahun dalam column yang sama dan dia pecah satu barisan semua ni walaupun dia nampak macam ringkas, macam muda sebenarnya ini adalah pembudayaan data seharusnya setiap barisan mesti stand alone which means dia lebih macam gini tengok setiap barisan boleh berdiri dengan sendiri so sekiranya anda melakukan FYP anda or you're doing data collection in the field tolonglah jangan kasih data kamu dengan merge fields ini adalah data mentah raw data jangan kasih rumuskan dia raw data ini penting tanpa raw data tanpa data mentah anda tak boleh lakukan analysis datang data itu ini adalah satu perkara yang kita akan beritahu jadi apabila anda melakukan data collection pastikan anda menggunakan data entry standardis menghubungi field merge dan sebagainya kerana ini adalah yang penting dan biasanya bila data mentah ini terlari apabila data raw data ia masuk ke salah cara ia menghubungi proses ia merumitkan proses menganalisa dan merumuskan data downstream bila sudah masuk matlab ke bila sudah masuk SPSS atau bila masuk python jadi itu sedikit sebanyak mengenai data acquisition jadi ingat setiap barisan mesti berdiri dengan sendiri dia standar loan, itu poin dia jadi apabila anda mempunyai data apa yang anda lakukan dengan data itu dan ini adalah beberapa perkara yang saya ada yang pertama, anda boleh mempunyai data itu anda boleh berkongsikan data itu tapi bukan hanya anda mungkin anda membuat FYP anda anda rasa bahawa data ini berkongsi online? tapi jagalah pastikan anda tidak berkongsi data dan sebagainya juga ingat bahawa apabila anda mempunyai data yang lain juga mempunyai data jadi mari kita kata anda membuat satu FYP FYP adalah kekerapan student PONTENG CLASS dan anda kata mungkin dia orang anda mempunyai hypothesis student ini lebih banyak PONTENG CLASS bila cuaca panas kerana ekon tidak berfungsi tapi anda faham setiap data mengenai keadaan cuaca di UMS pada waktu-waktu ini jadi jangan risau pergilah carilah dengan google dot dot go kalau mau guna bing juga carilah sumber data lain yang berkongsikan data iklim dan cuaca jabatan meteorologi dan satu contoh di sini data dot gov dot my data gov dot my adalah satu laman web yang dibangunkan oleh kerajaan negara Malaysia ini bertahun-tahun lama sudah di mana jabatan dan kementerian lain dia upload data set dia sendiri so let's say jabatan kaji cuaca dia upload data cuaca dan sebagainya so ini adalah satu sumber tapi ingat sometimes tak dapat sumber rasmi atau sumber rasmi itu ada silo dan tidak dapat itu data so sometimes you may have to cari data daripada sumber-sumber yang tidak rasmi for example semasa di pesatuan kinabalu coders itu memang kami let's say ada satu website nama dia haze dot sabar dot i o anda boleh melawat lawan web ini sekarang dimana kami ada satu sensor jerabu dkk and then setiap 5 minit so data demnya api which is dalam unit parts per million dalam bentuk 2.5 dan 10 micron particulate matter and data ini boleh di download secara pecuma daripada website haze dot sabar dot i o so ini satu contoh yang ringkas so memang ada sumber-sumber lain anda kena praktislah kung fu google cari sini cari sana untuk mendapatkan data yang sesuai let's say project anda sama ada dia project f y p atau juga dalam industri because seperti mana saya kata lebih awal lagi sering kali you cannot find 100% data lengkap daripada satu sumber you have to dapatkan daripada pelbagai sumber so ini maksud dia remix remix ini bukan music remix lah kasih scratching tapi macam itu juga because sometimes you have triple sources satu daripada sumber rasmi satu lagi dari sumber tidak rasmi remix ini kamu ambil kedua-dua data set campur aduk hubung kait and then from there you can do your kaeda statistics methodology dia demnya confidence interval dan sebagainya supaya anda boleh merumuskan ke semua data set dalam satu data set yang lebih besar dan lebih mantap inilah maksud dia remix satu lagi contoh mengenai data remixing adalah current.sabar.io sekiranya ini kali pertama anda pernah dengar website ini this one ada contoh disini so current.sabar.io ini sebenarnya ada ada demiah example disini so let me buka web browser saya ini hast.sabar.io so yes you can see it live ni per meter 3 so this is hast.sabar.io but from here if you look at sbsb sbsb adalah lembaga electric sabar so apabila ada pemotongan bekalan electric disabar ni memang dia post notice penutupan kecemasan macam gini di depan anda yang masalah dia ini satu masalah data dia orang tidak post ini gambar so as you can see it is picture of text senang dibaca manusia tapi tak boleh dibaca dengan masin, boleh dibacala tapi mesti bikin programming sini sana so ini adalah satu sumber data juga walaupun dia gambar so untuk membaca data ni kami menggunakan bagi student-student comm science kami guna OCA optical character recognition dibaca itu gambar kemudian kami extract demia text daripada gambar tersebut kemudian masukkan dalam database dan menghasilkan satu website ni yang nama dia karan.sabar.io yang memaparkan semula semua data yang dibaca daripada twitter feed sbsb so daripada website ini senang tak payah login dalam twitter tak payah login dalam facebook dengan pemaparan data ni anda boleh pergi kepada karan.sabar.io dan terus tengok, oh hari ni sekarang ada di KK ya ada pemotongan elektrik sana menggatal, sana kampung rampayan dan sebagainya dan anda juga boleh melihat notice-notice line sebentar, i accidentally terback keluar daripada dan daripada data asas daripada data mentah yang kami petik daripada twitter dan facebook kami dapat menghasilkan lebih banyak analisa mengenai data tersebut, for example berapa purata tempo setiap pemotongan bekalan, sekarang bulan august purata dia lebih kurang 9 jam, nah tengok 9 jam oh kena potong elektrik sekarang kalau di seluruh sabar, berapa banyak dia orang post dan tweet setiap hari berapa banyak gangguan harian berapa banyak tempo yang diberikan antara notice pemotongan sehingga pemotongan yang berjadwal dan sebagainya so semuanya data bukan ssb yang bagi ini adalah data yang kami hasilkan daripada analisa data mentah pemotongan dan seterusnya di dalam karan.sabar.io mungkin anda boleh lihat di depan anda demia graph di sini setiap jalur adalah satu district dan setiap jalur vertical bar bermakna hari itu ada pemotongan elektrik so when you look at this data data yang sudah dirumuskan you can actually see tawau banyak juga, macam setiap hari ada satu pemotongan nampak keke juga keke setiap hari untuk 180 hari in total, mesti ada satu pemotongan somewhere in keke so this is what you do with data you tell stories bukan just kumpulkan data your thesis too is a story the project that you are working for mungkin untuk raja negeri mungkin untuk syarikat lain actually sebenarnya kamu kumpulkan data so that you can tell a story with that data and this is the most important part why you are doing data you are using that to tell stories so back again to the presentation back again to the present so that's a little bit about remix remixing data so without further ado because we have passed the halfway point of this presentation, then other Q&A let's talk about something close to us all and also to our negeri dan negara here is a COVID-19 case study something that not to say Sabanet yang bikin but something that I also personally did semasa COVID-19 just like the last year bulan oktober semasa bulan oktober last year one of the major difficulties yang dialami oleh hospital-hospital di merata Sabar is demnya pesakit-pesakit COVID-19 dia orang kena masuk hospital so these are called referrals semua pesakit-pesakit COVID-19 is referred to hospitals across Sabar and of course, di Sabar we talk about west coast and east coast so ini adalah mengenai west coast on the west coast ini adalah masalah yang dialami semua kes COVID-19 perlu dilaporkan ke Queen Elizabeth Hospital so this one memang is SOP daripada KKM dan Jabatan Kerajaan Negeri Sabar at the start of it all sebelum kami masuk all they had was a google form now, ingat google form tu dia guna google form sejak and of course you are seeing the actual google form di depan anda so the problem is this when we started or when they started kebangiakan proses Allah secara manual guna kertas sahaja so unfortunately they had no choice tak ada masa untuk bangunkan system sebab banyak pesakit datang banyak covid positive datang especially bulan oktober tahun lalu so all they could do is bikin google form so memang dia extremely basic lah tapi was better than the manual process masuk data and then boleh guna google sheets kasi maintain, boleh export ikut district dan sebagainya unfortunately bila problem of covid-19 berlarutan extend extend sentiasa ada case baru masuk dan memang dia akan where reporting pun start failing and then tidak boleh track berapa patient masuk beribu-ribu record masuk dalam system ni so to solve this problem they approach community kami not sayaz jala tapi kena balu coders untuk tolong so tadi bahagian kotak biru ni was the original system so what we did was tanpa membazirkan masa atau do it what we did was we created an entire infrastructure based on nothing guna google google forms, google sheets google app scripting seperti disenarakan sana google app scripting ni bagi mereka-mereka yang minat google app scripting guna javascript sejauh so kalau kenal javascript boleh guna google app scripting tu guna google mail untuk hantar notice kepada setiap doctor yang hantar data guna google app lah ada submission boleh hantar email dan guna google data studio untuk menghasilkan dashboard-dashboard yang dapat digunakan oleh setiap doctor dan setiap hospital untuk memantau pesakit-pesakit COVID-19 di seluruh West Coast Sabar so that's why the point that I'm trying to make here is ini bukan, you can say ini adalah sistem yang complex dan rumit tapi sebenarnya notice demia tooling tak guna tak guna banyak programming tak guna banyak data analysis tools yang khas, tak guna matlab, tak guna SPSS dan sebagainya the point is here yang penting ni bukan tools yang kamu guna tapi is the pemikiran there you must know by mana data ni pump dari A ke B how do you process that data siapa yang perlu bikin pemandawan all they need to know is berapa banyak pesakit masuk hari ni janganlah, bikin sistem yang rumit-rumit atau yang complex atau data analysis yang complex dia seharusnya fit the purpose and function of the system which is untuk pemandawan so actually tidak banyak code actually the only code yang ada yang nombor 7 ni, minimal code for this bits, is untuk bahagian ni ini dia because dari segi data yang sudah masuk dari system kami dapat menghasilkan live real time maps menggunakan Google Geo Coding dan juga sedikit javascript sedikit leaflet ini semua open source ni ada link-link di bawah ni sekiranya anda minat nak belajar nak bikin map macam gini ada link-link dia di bawah ni slide tapi bukan dia main heat map lah heat map ni private sikit but the reality is we can use this to monitor growing residential clusters across the west coast of Sabar and this one is bukan system yang khas di bikin untuk data storage, data procedure sedikit javascript, sedikit Google Sheet sedikit Geo Coding yang penting again is not kamu tune programming bukan expert programming, it's just that oh, this data I think boleh guna pakai for alamat kan I guna alamat ni saya boleh jadikan dia GPS dan dari GPS to preserve the patient privacy kami plus minus 100 meters supaya we will know exactly di mana rumah dia and then coordinate GPS tu digunakan bikin heat map and from there boleh kenal pasti cluster sama dia, they are growing or shrinking across Sabar and then demnya mobile app dia actually this one, you can use app sheet ini secret dia a lot of people yang FYP dia bikin app janganlah bikin app sejauh sebab ada tool sudah right app sheet ni boleh diguna pakai bikin app tanpa programming all you have to do is link app sheet tu dengan Google Sheet dan terus boleh jadi app sudah baca Google Form dan bikin form sendiri dalam demnya mobile app which means no programming required for simple data actually boleh guna app sheet so app sheet tu free if you are just doing it for 10 users so ini later if you like to know more about it tanya-tanya saja lah tapi the point is here just with Google Sheets just with Google Forms you can acquire useful data and tell important stories so don't think you have to be expert in everything don't think you have to be expert in MATLAB, in SPSS or any other specialized tool that exists out there don't think you have to be an amazing programmer you don't have to start programming from 1986 because all this about data is permikiran is the thought process involved with data not the actual technical side although that too is a discussion into itself so that's the case study here are some of the lessons and data over time Number one jangan ingat tadi nampak hit map cantik dashboard pun cantik but the reality is all the managers, all the bosses your lecturers mesti ada dashboard projek tidak lengkap but the reality is you spend all your time 80% of your time preparing data the boring part of data science tapi penting you spend 80% of your time preparing your data begin formula, begin analysis palm dari A ke B ke C ke D akhirnya lepas tu then you spend 20% of your time begin dashboard cantik so remember this walaupun dashboard glamour tanpa data asas tanpa data mentah you will not have your dashboard glamour so jangan lupa perngimpan data and the data scientist behind it actually you are here 80% of your time in data prep this is one of the most important things that's why sometimes in kerajaan negeri or kerajaan negara i'm sure sama juga ada banyak boss-boss dia minta demo dashboard but when you look carefully at the organization tak kira jabatan kementerian ataupun SMI small medium industry you suddenly realize dashboard kami boleh bikin tapi tiada data itu masalah dia tiada orang yang simpan data dengan betul dan sebagainya so anda sebagai student-student or as viewers of today the audience please be aware that data collection is really important and you shouldn't give the data collectors don't think they play a small part they play the biggest part another thing that we learn about best practices is sedaya upaya keep your data open reduce gamia barriers to data you might think oh Microsoft Excel ni they are free install on my laptop jadi they are free Microsoft Excel is not free for those in government kami kena bayar Microsoft Excel so imagine betapa frost kami when we go to an agency and they say oh semua data kami dalam Microsoft Excel and the problem is of course kami boleh bayar sudah kami ada Microsoft Office but everybody thinks Microsoft Excel is free because it's pre-installed it is not if you want to use Microsoft Excel officially for your data right actually you have to pay it's the same thing that let's say if for those who have ever heard of Tableau reporting Tableau bukan free mahal gila but the thing is semua guna ni ma glamour guna Tableau itulah the experts that are using it's the same thing like saying designers who come back in talks mesti guna Photoshop the point is you don't need glamorous tools to do data so just remember try to avoid things that dikatakan price gated which means wall gardens is proprietary ini semua keyword tapi ingat memang menggunakan tool-tool pecuma dia ada cabaran dia juga I'm not saying semua senang what I'm saying is guna lah ni sebagai peluang untuk menjadi lebih creative, lebih resourceful because when you use free stuff right you think more you think of better solutions rather than just kena spoon feed and this is another thing of course spoon feed senang lah you feel better and so on tapi free ni cabaran dia would teach you new things so why not and then for managers jangan jadi tool-elit jangan kata mesti guna Microsoft Office mesti guna Macintosh ini susah sebenarnya actually it should be sebaliknya managers sepatunya lebih open which means that I can accept open office I can what buy because they should know better so hopefully you guys understand jangan jadi tool elitist that's why sumber tebuka ni open source bagus because it teaches us to be what and of course jangan lupa lah even though ada banyak format CSV2 is one of the most important format comma separated values everybody, labour office open office and so on boleh guna CSV semua ni about open data and so on there is the open source open data handbook ada link dia disini so if you like to know more you can also read it so we're coming to the end just for those yang nak masuk deeper and deeper into this because data science ni besar powerful just to let you know lah Google sheets formulas are powerful actually Google sheets formulas are more powerful than Microsoft Excel formulas so here's the thing if you like to cuba cuba right you should experiment with Google sheets formula link disini pergi kepada documentation just to let you know Google sheets can become a database you can for those yang tau SQL atau SQL language you can query Google sheets dengan menggunakan a variant of SQL Google app scripting bagus di pelajari not because pro Google Google app scripting guna JavaScript so if you belajar sedikit Google app scripting anda akan pandai juga not just begin Google app scripting anda juga pandai JavaScript so why not kill two birds with one stone or kill more birds with one stone and do both because at the end of the day anda akan belajar JavaScript and then finally if you want to practice dengan menggunakan basa pengatu caraan yang sesuai untuk data and it can be an alternative to MATLAB SPSS and similar toolkits yang penting guna pandas pandas adalah nama library yang ada dalam Python yang boleh digunakan as a statistical package and of course for those yang mau tinggi tinggi lagi you can always do R language yang huruf R the R language that's an even deeper stage into data but for now kami cakap pasal Python lah so ada sedikit bonus ini if you want to know how that data can be used for machine learning I won't cover it so ada link kepada teachable machine sini di sini anda boleh bikin AI dan deep learning dalam browser anda sendiri so I won't go into this but you can learn about it here in this link and if you just want to practice Python tapi anda malas mau install Python di atas laptop anda atau jubitan notebook dan sebagainya you can also do it online with Google Collab Google Collab free lah buat sementara waktu lah I don't know how long you'll be free tapi it's been free for the past 3 years you can use this to practice a dikit Python in closing ladies and gentlemen just remember data ni is a thinking person activity your role in data science tak kira bidang mana is you need to connect the dots between knowledge silos because actually data tu di merata tempat di mana-mana sejak dapat data in fact just to be clear what I mean about terms of data oop belum lagi in terms of data is sebentar I want to actually buka semia slide tadi tapi tak dapat mana tu one moment so what do we mean about that data as thinking person online di kaggle.com you can find data sets for almost everything heart and disease data set tom and jerry cartoon data set toronto sabai banyak ni guna search sejak on kaggle.com and then you can find all sorts of code that you need see fruit and vegetable, pizza price predictions and so on demia models dan sebagainya semua ni dalam Python atau R and so on on kaggle for those yang tak pandai git inilah masa kamu pandai git because MOH sudah publish demia data for covid-19 dengan menggunakan CSV online on github so why not use github and then from here you see let's say under minat maybe you're in social science tapi kamu minat pasal covid-19 here is some data that you can use already, just update everyday daily update so that's why data ni di merata tempat cuma dia kena silo your job as data scientist is to take everything and bring them together so when you bring them together you break down the knowledge silos you make more data, useful data out of it tell better stories with it so in your organisation tak kira orang yang paling atas atau orang yang paling bawah the data collectors versus the data collectors versus the the managers and so on want to see dashboards you need to keep them in the loop make sure that the data collectors know why they're collecting the data make sure they can see their own progress so that they're motivated to collect the data in the correct way and of course the managers must also recognise that in order to have your beautiful dashboards in order to do your data driven decisions you must have raw data so with that ladies and gentlemen that's a little bit about data culture and about the acquisition process and some of the problems we face so thank you so much for your time kalau ada hands-on i can show a bit and there's also Q&A sekarang so back to you, Chic City alright, nice thank you for the informative and interesting sharing Mr Ata so now we say Q&A la so kalau mana yang ada soalan tu boleh tinggalkan soalan di chat box kalau mana-mana yang malu untuk bertanya tu mana la tau malukan sebab orang lain nampak dia punya pertanyaan boleh letak situ privately dengan saya la sendiri so saya diberitahu ada pehak Sabanet juga yang hadir pada hari ni thank you pehak Sabanet dan welcome makasih sebab hadir dan sangat-sangat dihargai so kita take to dulu la bagi rehat sebab penat bercakap tadi banyak betul so kalau mana yang ada soalan tu boleh tinggalkan di chat box ya ada senapak ni ada link cek in cek out is it for karan.sabar.io miss jen iya karan.sabar.io sekiranya anda nak data daripada karan.sabar.io jangan lupa just check on the page sometimes biasanya la for sites yang data terbuka the footer usually ada swapan notice dan swapan statistics boleh nampak screen saya yang swapan notice bagi karan.sabar.io la some sites dia share data terus dalam bentuk jason terus boleh baca process dan import dalam database so just untuk makluman anda la kalau ada link kalau petangian itu mengenai link tersebut saya kenal ni dalam demia chat demia yang pyfi ni pyfi by the way is visitor tracking data so actually this is something interesting just to supaya semua orang tahu apa itu pyfi sementara saya cari demia data terlebih dahulu let me just show satu contoh data bentar continue asking questions saya akan what in again the first two ah okay there's a question ni hi Mr. Arthur from your experience except from all the challenges you stated what else the challenge you think face by the person who starts or pursue their job in a data driven environment okay so I'll be bluntly honest um with you Daphne um the problem is about data driven environments right now is in Malaysia not just sabar there's very little appreciation for data actually a lot of people and I'm not being political when I say this critical of any one of politics or whatever there are a lot of managers and politicians today who talk about data but fundamentally don't understand what the value of data is um and again I'm not being critical or political when I make this statement is just the reality we face many people give lip service to data but they don't appreciate the true value of data the time and money that is spent to collect that data because as again I mentioned earlier the problem is a lot of higher ups bosses wherever you find they want dashboards but they don't realise that to have dashboards you must sometimes have years and years of historical data but when you look back at the company and this is an actual challenge the company or the agency you find out they've never been storing data so without the raw data the base data how are you going to deliver the dashboard for them because there's no way to get the data so that's why they don't understand that there's that fundamental there but here's the time that we change this as students as employees and so on this is where we push awareness of that data culture you must have a data culture in your organisation from the bottom up not the top down otherwise the top can keep on talking but nothing gets done the bottom must start collecting data in a proper way and getting it out a good example is the MOH data for COVID-19 if you thought about it a true data driven organisation would have already done this months ago but MOH has only just started recently sharing this data actually I think less than a month ago again I just want to clarify I'm not criticising I'm stating the actual fact if you were truly data driven you would have done this in the first one month or two months of COVID-19 something like March 2020 but apparently it took us almost one year and a half before somebody finally had this brilliant idea let's share the basic data because there's nothing private or confidential about it and other people can use it and you see that's the challenge so I hope that gives you some idea of that other challenge that challenge is not technical the challenge is thinking mentality and has always been in fact even when we do other programming systems that are not related with data the challenge is always convincing the bosses or the staff right why is this better why it should be done this way because you can show as much proof as you want but they'll just keep saying whatever and it works why do we have to change and therein lies the problem it's a question of mentality I hope that answers the question for the challenge okay now salan send private dengan saya first dia tanya when I search for data how to know if the data is reliable and should we just assume ah okay this is a good question actually how do you know data is reliable okay there are few cases for this so I'll address each of these cases case pertama data itu datangnya dari pada sumber rasmi for example data.gov.my this one usually is easy because the data is from a primary source bermakna dia datang terus dari kementerian dan jabatan so it's safe to assume that data itu adalah betul but of course as you know they always say assuming makes an ass of you and me, sorry ada bahasa perancis tadi but the reality is in mathematics, in statistics you make all sorts of assumptions and assumptions too is bukan assumptions yang saja saja bikin assumptions they are official assumptions so therefore dalam case pertama if data tersebut datang dari pada sumber rasmi you can more or less confirm that you can use it as is boleh diguna pakai but let's say anda mengambil data daripada karan.sabar.io atau hast.sabar.io atau daripada sumber-sumber lain yang sekunda punya secondary sources tertiary sources or whatever you cannot ascertain whether the data is accurate or not this is why they are statistical methods things like confidence intervals you can also do something else you tak payah trust semua data tak perlu percaya why not verify the data kalau boleh there is this phrase trust don't trust verify how can you verify? satu contoh let's say kami ambil contoh karan.sabar.io tadi so tadi sudah saya bagi tahu karan.sabar.io ni patik daripada sbsb dia masuk website kemudian di paparkan but of course somebody may raise the question karan.sabar.io ni betul tak? is it really true mungkin data ni is fake data dia tidak patik daripada sbsb so of course this is satu way untuk menimbulkan ketidakpastian dalam data but there is an easy way to confirm the easiest way to confirm is pigilah karan.sabar.io and sometimes we as data persons can make it easier see notice it says kotakina balu 1520 ini mungkin fake data ni but we made it a bit easier tengok other 2 symbols ini so sometimes the website gives you the link to the primary source ini dia punya primary source dia dia punya gangguan berkalan elektrik so that's why you don't have to just trust atau percaya dengan semata-mata percaya sahaja look for ways to verify the data walaupun dia datang daripada sumber-sumber lain so this is probably the most important thing so primary source can trust harap-harap lah even that also sometimes you actually analyse the data second case you may be coming from unofficial source yang tidak rasmi look for ways you can verify the data bukan sejak mau kasih verify beratus-ratus punya disini lah what i mean is secara rawak pick a notice see that this notice matches apa yang ada pada dia punya sumber sumber primary dia the primary source and in the third case di mana data tu memang susah untuk dikenal pasti mana sumbernya dan tiada cara untuk verification well this is where you need to compare bandingkan data tersebut dengan sumber-sumber atau dataset lain this one you don't have a choice if you're really unsure about the confidence of the dataset that you have you need to look for other better data sources walaupun tidak lengkap and then bandingkan antara dataset-dataset yang sedia ada for example dalam contoh ii bagi tadi jabatan kebajikan senarai B40 maybe senarai B40 tu tak lengkap dari jabatan kebajikan but you can get another data source let's say daripada JPN Jabatan Pendafkan Negara senarai semua IC untuk satu district then you at least know jumlah orang lebih kurang for that atau daripada Banci Malaysia then kamu bandingkan the number of B40 disana versus B40 daripada jabatan kebajikan and sometimes as a data scientist you may be a case you have to decide actually which one is the better dataset daripada kajian sumber-sumber sumber-sumber dia dan juga daripada demia margin of error for each dataset so this is a choice that you data scientists will have to do as you become more experienced sometimes you have to decide when you're faced with incomplete data what do I do and dia bergantung kepada case for example if my objective saya is bagi bantuan B40 so I would rather take sebagai leader lah I would rather take yang overestimate bukan underestimate, why walaupun dia buzzier do it ini bermakna kalau saya overestimate the number of B40 orang tidak akan kelaparan so these are hard choices that sometimes data science cannot answer dan dia kekurangan data so these there is no easy solution all you can do is lepas the problem is over atau mula meredah is then you work kenapa data tidak lengkap bolehkah saya implement systems atau bikin kajian sepenuhnya yang dapat melengkapkan data yang hilang atau yang data yang tiada so this too is also part of the data dan secara pro-active ambil langkah-langkah untuk melengkapkan data data set tersebut so I hope that menjawab sualan juga sedapah ada sualan juga lagi itu ada lagi sualan private ah boleh-boleh okay dita-nya I read about data is the new all will there be a data world it is already a data world to to be honest so actually pasal about the data world data world bagaimana data world okay so I think this one I can also answer Giana siews question because Giana siews question also ask I would like to know what do you think about the job market for data scientists in Malaysia the one tadi jawab sudah add a little bit more and then pasal data world for your information the world is already a data world when you look at google when you look at facebook when you look at amazon actually their primary goal is selling and buying data and it's your data they're buying and selling how they do it is advertisements or maybe like food panda grab food how do they know what's your favourite food food panda right so the system we always suggest to you makanlah makan ni daripada western apestaka the idea here is it is already a data world a data driven world but here's the problem and this is a bit about the job market although the world is data driven a lot as I mentioned earlier a lot of companies right have specific roles atau jawatan bagi data scientists kurang there's not a lot of job scopes data science biasanya data science is part of a bigger job scope for example data science is useful in cyber security but data science is also useful let's say in insurance agents so you won't actually see data science post but you might see stuff like actuary for insurance company maybe in a company you will see cyber security and one of the job scopes is analysis of vulnerability data so although there is a data science role you seldom find it except in big companies like IBM A Asia and so on where there is such a thing as a chief data scientist but in smaller companies even like Sabanet chief data scientist I too am not a data scientist officially I am a project consultant but we handle a lot of data and when it comes when we deal with individual projects like for example biodiversity data project management data foreign worker data and so on all of this require a degree of data science sorry I don't want to use the word degree bukan ada data science degree it requires penguasaan data science because inside you'll be doing statistical methods sometimes it's just begin graph but sometimes you analyze the data for problems like cyber security how can you predict network usage over time maybe in terms of in terms of other data sets like tadi I said B40 you'll be analyzing it to say oh berapa percentage B40 so that we can sediakan berapa banyak bakul makanan dan sebagainya so hopefully that sort of answers the job market for data scientist so don't just look for data scientist you have to be at least skilled in something else as well closer to your discipline like if you're computer science or computing informatics you might not your first job may not be data science because maybe also tidak cukup pengalaman but just understand that if you handle programming if you handle data basis eventually you go into data science as well it is your data world as well it is a data driven world but it doesn't mean secara rasmi it is not officially data but everybody uses data and data is very important let's see now ada soalan lagi ni say how do we handle the people who gave us the correct data and proceeded to tell us to use in store even the data clearly has error especially if the people has higher order hmm ini susah yes they such a thing as correct and write data so far nasib baiklah in my official line of worker we've never had to do something like this in other words use incorrect data because everybody I tell you it's bukan diorang juga we all blur sotong pasal data everybody is blur about data I don't think you're an expert on data in fact the best way to approach data sets is to think I don't know anything about this data so let's do some standard statistical analysis and methods on that data but let's just say this case comes up la somebody demands that you use use this data set so okay I can use the data set but maybe if I see this data set looks incorrect right then I would definitely put in the small the margin of error for this data set is say plus minus 20% and then I'll also put down the confidence interval appears to be 80% only because biasanya if you do a lot of statistics right a preferred confidence interval is 95% to 99% so if you look at the data and you think the data is incomplete right you should perform a confidence interval calculation and then kamu letak bisana what's my confidence interval so let's and I'm quite certain right the boss-boss yang minta dia orang tidak faham confidence interval dia orang tidak faham by mana mau baca I will put it there normally that if I can I will chari data set from other sources as well bikin perbandingan right there inside the document so I can always say oh we have received this data set we use this data set standard here are the confidence intervals and margin of error for each data set then based on these 3 data sets our conclusion is blah blah blah so that's the way I would do it la let's say if the boss insist mau guna that data set but I'll let you know la kami tak pernah dapat case macam gini di mana sama ditolas guna data set orang lain which is actually good la because nobody is that expert to tell this kind of things right now about guna data set ini so it hasn't happened yet ah okay somebody ada sualan from your experience what are the tips and points to be competent in storytelling of data ah actually the sun's enough so what you can do is pernah dengar pitching as in pitching try and say let's say you have an idea or a concept and then kamu pitch yang pitch tu bukan panjang bukan ucapan atau cerama panjang satu dua minit sejak this gives you the ability to take a complex concept a system, an idea and then bikin dia dalam 3-4 ayat sejak dalam satu dua minit this makes you a competent storyteller rather than kamu bercerita panjang or ini project saya nak bikini panjang-panjang ini objektif la ini methodology ini kajian dia let's say ambil tesis kamu cu and i ask you kamu dibagikan dua-tiga minit sejak untuk explain your tesis so fikir-fikir dulu and then explain it in just 3-4 ayat pitch ni penting and mungkin anda pernah dengar ni on the entrepreneurship side bar that kamu masuk when you're talking to an investor kamu tidak ada banyak masa you won't have half an hour to describe your idea so if you can practice pitching it helps you with your storytelling because it's the same thing with data kamu tengok data, wah graph besar ada beribu-ribu record berjuta-juta record but if you know how to pitch you look at the data, okay this is our conclusion this data tells us there are 80% poor people we should sediakan 14,000 baku makanan end of story if you want to mourn war, tanya sualan of course that's an oversimplification but the idea is for better storytelling you must pandai merumuskan apa yang sedia ada supaya dia fit the required time frame so let's say anda dibagi 5 minit sejak then okaylah have a short pitch a 5 minit chat and everything else letak dalam say graph that proves your point jangan letak graph atau bacaatka or whatever untuk percantikan presentation make sure everything tells a point of your data story that way you get your point across clearly so sometimes you have to sacrifice smaller points kena buang but just so you know because style to list thesis is not that suitable for internal company presentations and so on because internal presentations is lebi what's your point cepat and then balik kerja so maybe the style to list thesis is lagi sesuai kalau mau tulis proposal if you're suggesting a project to somebody else lah just so you know but in terms of data storytelling it's not really technical skill that's required but pandai merumus pandai pitch so if there's anything else that you remember from this just belajar pitching then senang sikit mau merumuskan data I hope that is useful to you ada soalan lagi? ada soalan lagi yang private ni pun boleh tahan ramai juga ni soalan dia banyak soalan daripada how to know apa yang perlu ada dalam data set istiata how do you know what is inside a data set ok so my understanding daripada soalan ini how do you know what's inside a data set is sometimes that we start project right we don't know what data that we're supposed to store so that's the way I'm going to interpret this question so kami blur one good example or satu example is let's say tadi when we are doing a study kekerapan pelajar UMS ponteng class then we not apa factor that contributes kenapa UMS student ponteng class so think about it lah apa di dalam data set you might want to do a survey tapi tak tahu apa mau masuk dalam two survey so you might okay I should survey pasal transportation the UMS I should survey pasal iklim dan cuaca tadi that one example mungkin semua survey keadaan sama ada jerabu ka pollution ka apa sama ada tenaga electric dan sebagainya so sometimes when you look at this susah because then you suddenly rise data set kau berniat column semua simpan so what you can do is look at other data sources yang ada like other studies this is part of your literature review semasa bikin your thesis memang ada literature review literature review bukan maksudnya tengok thesis university tengok thesis sejauh orang lain literature research should be expanded into general research example if you are doing pelajar ponteng skola why not just look at what other people talk about in forums, in other universities or colleges and say what other factors that contribute to it use this to easy come here data set to kenal pasti apa feel-feel, medan-medan yang penting then there is another case where I was di bagi satu contoh food recommendation so a lot of people ingat food recommendation is semua isi berapa banyak calorie apa nama makanan dan sebagainya tapi there is one way easy to do research tengok sejauh food panda dan grab food because food panda, grab food more fun semua is a food recommendation engine yang sudah ada you don't have to reinvent the wheel tengoklah apa orang lain bikin so when you look at food panda you realize they don't just store they don't store they don't store nutrient or nilai calorie instead they store or any western any Malaysian, any fusion ada tag dia then they store harga berapa banyak mahalka, muraka so don't just look at other people's thesis as part of your research bila bikin FYP look at other systems and then jangan takut when you do this research tulis dalam thesis kamu bikin analisis daripada sumber ini we dapatkan dataset kami sepatunya ada ciri-ciri ini kami perhatikan dalam food grab dan food food panda dan grab food dia orang store data macam begini dan daripada ini this is where you do synthesis you take apa yang orang bikin sudah apa yang kamu kaji juga melalui survey sama ada face-to-face masa pandemik atau via VC kami bikin synthesis maka dataset kami yang kami akan simpan dan kumpul data adalah berdasarkan research kami sebelum ini so expand your horizons gunalah kung fu dalam google and so on google around look for what other people are doing tak semestinya secara rasmi i mean look at other apps look at other systems cari di github for example mungkin ada orang bikin aplikasi sudah and then cari lagi dataset di tempat-tempat macam kegel for example so in this scenario then maybe you will find something useful so hopefully that answers sedikit sebanyak mengenai bagaimana nak kenal pasti apa yang perlu diletakkan dalam dataset so there is also one more reason that I forgot to mention as well sebab if kamu bikin dataset asal anda hanya mengikut keperluan sendiri then your dataset is already bias because they ikut just pemikiran sendiri what you have to do is get other people to contribute to that thought that communication is important because that way they will reduce bias as they say in maths correlation does not imply causation but if your dataset is let's say kekerapan ponteng sekolah disebabkan transportation issue this can lead to bias because it just so happens kemungkinan pada masa tu semua bus breakdown but it might be sebab lain orang ponteng bah so in this way you may accidentally introduce bias because dataset anda there is only base on your perception dia terkongkong hanya kepada medan-medan yang anda fikirkan so always bagus untuk perluaskan dataset anda walaupun ini memperbangiakan data dia mengurangkan kejadian bias dalam data back to you Mr.T alright lagi sealainya data nya hello Mr.Ather apa yang kita perlukan untuk mempunyai kerja yang berlainan dengan data apa yang kita perlukan okey apabila keadaan keadaan keadaan keadaan dan ekspertis jadi untuk biar anda tahu seperti yang saya cakap sebelumnya apa-apa bonis anda tidak perlu tahu semua mereka tentu-tentu mempunyai penggambaran bahawa apa yang anda mempunyai takkira sosial takkira perniagaan komsains tapi anda patut tahu penggambaran sedikit sebabnya penggambaran yang penting adalah anda mempunyai dengan kisah-kisah yang bermakna saya perlu membuat A 1, 2, 3, 4 ini sangat seperti kemungkinan yang anda mempunyai dengan matematik atau kemungkinan kemungkinan yang lain membuatkan masalah membuatkan kemungkinan jadi idea tentang kemungkinan adalah penggambaran membantu dan terutamanya jika anda belajar seperti Python, penggambaran general atau R tidak begitu, PHP Bolela tetapi PHP tidak begitu sempurna untuk perniagaan data Python adalah penggambaran yang hebat untuk perniagaan statistik, perniagaan data dan sebagainya lebih daripada belajar guna Matlab, SPSS saya tidak mengatakan Matlab, SPSS dan perniagaan latihan matematik adalah takkira perniagaan mereka adalah berguna tetapi bagaimana anda boleh belajar boleh pergi ke kod akademi boleh pergi Udemy dan sebagainya tetapi penggambaran adalah perniagaan tetapi Tablo SPSS itu hanya perniagaan anda boleh belajar mereka pada masa ini tetapi penggambaran adalah sesuatu yang akan membantu untuk seluruh kehidupan anda itu adalah satu perniagaan kemudian mempelajari bagaimanapun seperti contoh MOH data ada kepandai guna Git jika anda tahu bagaimana untuk menggunakan Git saya akan tahu bagaimana untuk menghubungi data dari data set jadi ini adalah kecil-kecil-kecil yang jika anda mempunyai kerana saya dapat mengawal data dalam cara yang saya mahu saya dapat menghasilkan dengan cara yang saya mahu dan saya dapat lakukannya sekarang, kepadaan yang susah dengan sains data anda mesti mempunyai penggambaran fungsinya saya mengatakan saya cuba menjelaskan lebih kelihatan seperti mempunyai bagaimana kepandai guna bagaimana penggambaran random bagaimana populasi, kepandaian standard Sisihan Piawai semua ini ini adalah tentu-tentu bahan-bahan dari sains data kemudian sedikit mempunyai bahan-bahan data bagaimana bahan-bahan data berfungsi tadi saya mengatakan kenapa ada barisan mengapa setiap roh mesti mempunyai standar sebenarnya itu perkara yang paling penting anda perlu tahu tentang bahan-bahan data tetapi sebenarnya jika anda tahu tentang penggambaran tahu tentang bahan-bahan data tahu tentang pengetahuan pengetahuan pengetahuan tidak semestinya rumit bahan-basis pengetahuan akan bekerja jika anda memperkenalkan bahan-bahan data dan kemungkinan untuk melihat bahan-bahan data dan memperkenalkan bahan-bahan data jadi apabila saya mengatakan bahan-bahan data mari kita kembali ke Kaggle.com saya rasa bahan-bahan saya masih memperkenalkan bahan-bahan data untuk menggunakan persembahan anda ke dalam industri untuk bahan-bahan data adalah ke Kaggle.com mereka mempunyai kompetisi dan memperkenalkan bahan-bahan data seolah-olah bahan data untuk contoh, tadi kita mengatakan bahan-bahan bahan-bahan data mungkin mereka mempunyai bahan-bahan data bahan-bahan data bahan-bahan data dan mengambil satu dari bahan-bahan data atau dua atau tiga guna python, guna excel, apa-apa pun yang anda mahu lakukan cuba mengerasakan bahan data dan kemudian, ada pilihan makanan tadi kita bercakap tentang algoritm pilihan makanan mungkin ini adalah bahan-bahan data pilihan makanan, pilihan pilihan pilihan makanan dalam dockx tapi ada CSV juga dan kemudian kita dapat melihat bahan-bahan data mereka melakukan dengan gendak dengan sarapan dengan kalori, ayam dan sebagainya jadi apabila anda memperkenalkan dalam bahan-bahan bukan untuk memperkenalkan dan memperkenalkan tapi hanya memperkenalkan dan melihat bahan-bahan data anda belajar perkara baru tentang bahan-bahan data jadi saya harap ini membantu sedikit dalam keadaan tetapi lebih penting anda perlu memperkenalkan jangan harap Universiti Sahaja akan mengajar anda ini Universiti Akademik tidak dapat mengajar anda ini anda harus memperkenalkan ini dari keadaan dan memperkenalkan diri dalam perkara seperti kerja komuniti untuk itu, anda boleh memperkenalkan sendiri atau jika anda lebih suka memperkenalkan sendiri dengan 3 keadaan keadaan seperti Kodakadami, Udemi atau Institut Kodakadami bagi contoh jangan harap Universiti Akademik akan memberikan anda ini kerana mereka tidak dapat Universiti Akademik sedikit kecil bukan untuk berkong-kong jangan memperkenalkan lihat keadaan untuk contoh yang anda boleh menggunakan jika anda tidak menangis pada data di kagel Mohh.gov.mai anda juga boleh memperkenalkan Pytone guna Google Collab dan sebagainya saya harap ini membantu dalam keadaan saya adalah programa saya selalu bercakap dari segi program-sikit Programming terlalu penting. Okey. Sebenarnya banyak lagi soalan. Tapi sebab masa menjemburui kita. Saya rasa ini soalan laslah ya. Dah Sabanet S&P related to data science and what its requirements. Okey. Saya blan sikit. Sabanet does have internship program but it won't be data science specific. Because Sabanet ini, Syarikat Sabanet ini basically there are three major departments lah. Boleh dikatakan. Satu is systems. Another is network. Another is software. I'm from the software side. So software ni actually is in a subsidiary yang digital heritage lah. Yang di-mention tadi. We won't offer data science internship. Memang tak ada. Because we expect our interns to be general purpose. So yes, you will be exposed to some data semasa internship if you're accepted lah. So Susar sometimes more aligned schedule. So just understand that we cannot accept many interns. Sometimes, memang internship to talk. But if you're interested, if you go to sabadashnet.com website Kamila boleh cari with Google and look around on the website. Ada tu, there's an email somewhere there for potential interns to write in to our HR department. Bukan saya yang accept interns is our human resource department based on company needs at the time. But just to note, we don't offer data science. But I'll tell you this lah. No matter which department you go in samada systems, network atau software you will encounter data science problems. Because everything needs data. For example, kalau systems that's about server and server rack. Bukan just install server. Bukan just install windows. You also have to think about server reliability. How hot does the server run? The acorn working or not? What does the temperature sensor say and so on? Same thing also for networking. Walaupun switch router, what about network traffic? Cyber security incidents dan sebagainya also requires things like data science can help. Analysis of cyber security incidents and so on. And of course software, not surprising lah. Because software, we build internal systems and external systems for government agencies or private enterprises. We handle a lot of client data. Some of it is private and confidential. Those are the things that you won't be exposed to if you join as an intern. But maybe some basic data like processing, the mere test orientation project management and so on. We'll be exposed to that kind of data. So I hope that gives you an idea of that. I think a lot of companies they also won't offer data science interns lah. We just minta intern. Tapi the interns so long as they treat the interns well you will be exposed to data science during internship or data science techniques and methods during internship. So back to you, Med City. Pul brapas tu loh. Okey, cerita tinggi lah. Right. Okay. Mr. Ata, kalau macam mungkin boleh join rambutan diskot untuk jawab persoalan-persoalan daripada mungkin student-student nanti saya bagi boleh someone send person lah mungkin send link rambutan diskot di chat box? Teriak. Okey. Yang lain pun boleh join diskot tu untuk tanya-tanya soalan lah. Nanti saya join mana-mana soalan yang I will join dalam mungkin 10 minit kerana jarang seguna diskot kena cari lagi username password. Ya, Mr. Ata. So mana-mana soalan yang saya tiada ajukan hari ni, saya minta maaf banyak-banyak sebab betul-betul masa mencemuri kita. So, guys, jangan lupa untuk merasakan tawaran dan tanya-tanya. Jadi untuk menonton tanya-tanya soalan ini anda boleh pergi Facebook persatuan Masih Suafakuti Komputeran dan Informatik dan menonton tanya-tanya. So nanti akan ada di Hunter juga recording insya Allah di grup grup yang kamu ada sekarang lah. So, kita selesai tanya-tanya soalan ini. Bolehkah anda berada di kamera anda? Dan mari kita mempunyai gambar bersama. Okey, mari kita. Semua syai itu. Semua syai. Wah lama saya tidak login dalam diskot. Saya adalah pemain kejadian. Kadang-kadang masuk dalam diskot tapi jarang. Wow, pemain kejadian. Sejak kejadian.