Yuk Ketahui Kenyataan Seseorang Data Scientist di Industri Data

Yuk Ketahui Kenyataan Seseorang Data Scientist di Industri Data

Kenyataan Seseorang Data Scientist di Industri Data – Di kota-kota besar, arti serta teknologi Internet of Things (IoT) mungkin biasa didengar serta banyak dipakai. Di Jakarta contohnya, panggilan ‘Jakarta Smart City’ yang memaksimalkan mekanisasi smart lighting yang mengendalikan lampu penerangan jalan serta lampu jalan raya dari jarak jauh adalah satu bentuk dari aplikasi IoT.

Terdapatnya integrasi teknologi info serta komunikasi dalam mentransfer data dengan automatis tanpa ada membutuhkan hubungan manusia dalam tata atur kehidupan keseharian berikut yang jadikan IoT dapat tingkatkan efisiensi, penyebaran info, kualitas service, dan kesejahteraan warga. IoT adalah sisi dari pemrosesan pengetahuan data science yang diterapkan pada bagian sehari-harinya.

Saat ini, penyebaran pemrosesan data science serta teknologi IoT memulai menebar ke pedesaan. Warga pedesaan dapat juga memakai pemakaian data agar bisa diimplementasikan dalam pekerjaan serta dalam kehidupan keseharian. Misalnya alat pemberi pakan ikan automatis yang ditingkatkan oleh eFishery.

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Dalam peluang pertemuan dengan eFishery, Ahmad Anshorimuslim (Head of Engineering eFishery) serta Dimas Gilang (Software Engineering eFishery) membuka serunya pemrosesan data science dari dapur engineering eFishery. Mereka mengupas fakta-fakta menarik di balik pekerjaan Data Scientist di lapangan.

1. IoT tidak selalu masalah internet

Jika dengar arti IoT, tentu beberapa orang langsung memandang jika aplikasinya tetap terkait dengan internet. Walau sebenarnya tidak juga. Aplikasi IoT di perkotaan serta di pedesaan itu jauh berlainan. “Temuan-temuan yang tidak baik di lapangan dan karakter pedesaan, memengaruhi bagaimanakah cara kita memproses data, menghasilkan data, ataupun berhubungan dengan datanya”, tutur Anshori.

“Di eFishery, kita menolong beberapa petani serta peternnak menyiapkan paket lengkap dari mulai penyediaan pakannya, konsultasinya, serta bagaimana mereka dapat mengatur bibitnya sampai bisa dipanen”, imbuhnya.

eFishery yang bukan sekedar bisa mengotomiasasi pemberian pakan ikan dengan terencana ini dapat juga mencatat tiap pemberian pakan dengan real-time dengan ukuran yang pas. “Agar bisa menolong petani serta peternak ikan, kami tidak memakai image recognizion, tetapi deteksi getaran. Dari getaran itu kita dapat untuk jadikan sensor pendeteksi jika ikan telah kenyang”, tuturnya.

2. Data Scientist serta Data Engineer itu berbeda

“Satu hal yang menurut saya sangat penting dari pemahaman data scientist ialah yakni kata science tersebut. Seseorang Data Scientist harus lewat proses scientific method”, kata Anshori.

Tujuannya, sama dengan waktu belajar di bangku kuliah, harus ada metodenya, ada beberapa langkah yang pasti yang perlu diurutkan. Contohnya ada pendahuluan, latar, persoalan, cara, serta jalan keluar, pelajari, serta rangkuman.

“Dalam data science, yang paling penting ialah scientific process-nya. Data science itu bukan memiliki bentuk, tetapi bergantung prosedurnya, walau ranahnya ialah business intelligence atau business analyst. Banyak yang berasumsi data science itu sama juga dengan data mining, walau sebenarnya belum pasti. Balik lagi, bergantung pada prosesnya”, lanjut Anshori.

3. Kunci data science ialah matematika

eFishery mengerti jika tidak kebanyakan orang yang profesinya jadi Data Scientist mempunyai latar belakang pendidikan spesial data science yang perlu ditempuh sekian tahun. Tetapi yang perlu diingat ialah, tools apa pun yang dipakai, baik Python, ataupun R, basic yang perlu dimengerti ialah matematika.

“Kalo kata orang matematik, R itu ialah matematik tingkat lanjut”, kata Anshori. “Jadi agar bisa menjalankan R, harus memahami dahulu rumus matematikanya”.

Tidak hanya matematika, hal-hal lain yang perlu dimengerti ialah potensi coding, sebab dalam memproses data pasti memerlukan custom solution. Seringkali Data Scientist memerlukan mode serta output tersendiri agar sesuai dengan pihak yang akan memakai data itu, contohnya jadikan situs server, atau bentuk yang lain.

“Sekarang ini, Data Scientist tidak dapat Hanya jadi sebatas Data Scientist, sebab cakupannya yang mewajibkan untuk menganalisa insight serta mengurus skema, jadi minimum sekali harus dapat matematika serta programming”, papar Dimas memberikan tambahan.

4. Data Scientist itu ialah serangkaian proses yang panjang

Berarti, agar bisa memproses data, seseorang Data Scientist harus turut serta terjun langsung ke pemungutan data. Mengapa demikian? hanya karena mereka yang tahu mereka memerlukan data yang seperti apa, sekitar apa, serta sevariatif apa.

Seseorang Data Scientist tidak harus jadi orang yang ambil datanya, tetapi sekurang-kurangnya, mereka harus jadi reviewer waktu pemungutan data itu untuk pastikan apa data yang diambil telah sesuai apa yang diharapkan.

“Di eFishery, sebab sumber data kita ialah makhluk hidup serta outputnya ialah behavior, jadi sangat banyak temuan-temuan baru yang kita bisa. Tiap data baru ini tidak langsung bisa kita olah gunakan software yang ada. Jika kurang cocok ya harus membuat baru”, tutup Dimas.

Jadi seseorang Data Scientist yang andal memang memerlukan proses. Latihan serta pengalaman ialah unsur yang tidak kalah penting agar bisa mengerti lebih dalam menganai penolahan data.

Karena itu, Course Net bukan sekedar menyiapkan sarana belajar yang flexibel serta gampang dimengerti sebab berbahasa Indonesia, dan juga melengkapinya dengan real-case proyek serta meetup sesi agar bisa membuat portofolio peserta. Mulai langkahmu belajar data science menjadi Data Scientist dengan perebutan pengetahuan yang pas bersama dengan Course Net!

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