Data Science dalam Pendidikan Formal: Diploma, S1, dan Pasca Sarjana

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“In our world of Big Data, businesses are relying on data scientists to glean insight from their large, ever-expanding, diverse set of data … while many people think of data science as a profession, it’s better to think of data science as a way of thinking, a way to extract insights using the scientific method.” — Bob E. Hayes.
Image Source: https://thewokesalaryman.com/2020/06/15/i-switched-careers-at-34-and-became-a-data-analyst-heres-how/

Pendapat kami terkait Data Science sebagai jurusan/program studi di tingkat perguruan tinggi (pendidikan formal): sebaiknya ada pembeda fokus kompetensi yang jelas dan berjenjang dengan baik.

Level S1/Diploma (3-4 tahun):

Kompetensi utama:

  •  Exploratory Data Analysis
  •  Soft skills untuk data scientist/analyst (termasuk design thinking)
  •  Legal & Ethics seputar data dan AI
  •  Analisa Data Terstruktur dasar (machine learning/statistik): Regresi, klasifikasi, clustering
  •  Feature engineering dan extraction + dasar Dimension Reduction techniques
  •  Dasar analisa Time Series dan Spatial

Kompetensi prasyarat:

  •  Matematika dan statistika: Kalkulus, Alin, MetDisk, StatEl, etc.
  •  Dasar basis data & pemrograman: RDBMS, Struktur Data & Algoritma,  OOP. 

Opsional di level S1/Diploma:

  •  Dasar Teknologi Cloud
  •  Teknik sampling & Digital Data Gathering
  •  Dasar Analisa Media/Network Sosial (SMA/SNA)
  •  Dasar Neural Network/Deep Learning

Level S2 - Pasca Sarjana (1.5-2 tahun):

Kompetensi Utama minimal 2 dari 3 masalah utama Big Data:

  •  Velocity: Real-time data storage, analytics, and models for prediction/forecasting.
  •  Variety: Unstructured data representation and analysis. Bisa Image Processing~computer vision, NLP~Text Mining, dan-atau audio analysis~speech recognition.
  •  Volume/Scalability: NoSQL, Distributed dan-atau GPU Programming (e.g. Functional Programming di Spark/hadoop).

Beberapa topik advanced ML/DS dan-atau Research Project lain: 

  •  Advanced AI: bisa membahas berbagai model Deep Learning tingkat lanjut dan aplikasinya.
  •  Advanced Network Analysis: misal transportasi atau distribusi online untuk barang/penumpang.
  •  Advanced Social media Analytics
  •  Autonomous vehicles: Car/Drone/Robotics.
  •  IoT & Smart Devices ~ storage, analytics, and prediction: misal Predictive Maintenance solutions.
  •  BioInformatics dan-atau AgroTech
  •  Blockchain Technology

Kompetensi Pendukung:

  •  Teknologi Cloud tingkat lanjut.
  •  Pemrograman tingkat lanjut.

Optional: 

  •  Innovation to Business in AI-ML, termasuk topik tentang leadership & Project management.
  •  Latest/Recent AI topics: misal DCAI, PINN/DDDS, etc.

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