{"id":460,"date":"2026-03-26T11:02:24","date_gmt":"2026-03-26T11:02:24","guid":{"rendered":"https:\/\/socs.binus.ac.id\/data-science\/?p=460"},"modified":"2026-03-27T03:15:05","modified_gmt":"2026-03-27T03:15:05","slug":"year-2-skills","status":"publish","type":"post","link":"https:\/\/socs.binus.ac.id\/data-science\/2026\/03\/26\/year-2-skills\/","title":{"rendered":"Year 2 \u2014 Core Analytics & Engineering"},"content":{"rendered":"
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Year 2 is the heart of the program. This is where you go from understanding data to mastering<\/em> it \u2014 building predictive models, reasoning under uncertainty, engineering data pipelines at scale, and deploying your work into the real world. By the end of this year, you won’t just be analyzing data; you’ll be building production-ready systems that make decisions automatically.<\/p>\n

The seven data science courses in Year 2 cover the full spectrum of modern data science: from machine learning and deep learning, to cloud engineering, probabilistic modeling, and MLOps. This is the year that turns a student into a practitioner.<\/p>\n

What You’ll Be Able to Do<\/h3>\n
\n

\u2192<\/span>Build, evaluate, and select machine learning models for classification, regression, and clustering problems<\/p>\n

\u2192<\/span>Design and train deep learning architectures \u2014 CNNs, RNNs, LSTMs \u2014 for images, sequences, and more<\/p>\n

\u2192<\/span>Apply Bayesian reasoning to quantify uncertainty and build probabilistic models<\/p>\n

\u2192<\/span>Design rigorous surveys and apply sound sampling methods for data collection<\/p>\n

\u2192<\/span>Process massive datasets using Apache Spark and PySpark on cloud infrastructure<\/p>\n

\u2192<\/span>Architect cloud systems on AWS for storage, compute, and big data workloads<\/p>\n

\u2192<\/span>Deploy machine learning models as scalable APIs and cloud services \u2014 and monitor them in production<\/p>\n<\/p><\/div>\n

Data Science Courses<\/h3>\n\n\n\n\n\n\n\n\n\n\n\n
Course<\/th>\nWhat You’ll Learn<\/th>\nKey Skills & Tools<\/th>\n<\/tr>\n<\/thead>\n
Machine Learning<\/strong><\/td>\nMaster the core toolkit of supervised and unsupervised learning \u2014 from decision trees and SVMs to ensemble methods and recommendation systems. Learn how to prepare data, select the right algorithm, and rigorously evaluate results.<\/td>\nScikit-learn, classification, regression, clustering, ensemble learning, model evaluation<\/td>\n<\/tr>\n
Bayesian Data Analysis<\/strong><\/td>\nLearn to reason probabilistically. Move beyond point estimates and understand uncertainty \u2014 using Bayesian inference, conjugate priors, MCMC sampling, and hierarchical models to build models that tell you how confident<\/em> to be in your results.<\/td>\nBayesian inference, MCMC (Metropolis, Gibbs), posterior distributions, Bayesian regression, model comparison<\/td>\n<\/tr>\n
Survey and Sampling Methods<\/strong><\/td>\nGood data doesn’t collect itself. Learn how to design surveys, choose sampling strategies (simple random, stratified, cluster), and make statistically valid inferences from samples \u2014 foundational for any research or data collection project.<\/td>\nSurvey design, probability sampling, statistical inference, bias assessment<\/td>\n<\/tr>\n
Data Management and Organization<\/strong><\/td>\nWork with data at scale using Apache Spark and PySpark. Learn data governance principles, implement ETL workflows, and run machine learning pipelines on distributed systems \u2014 skills essential for any enterprise data environment.<\/td>\nApache Spark, PySpark, ETL, data governance, Scala, structured streaming, feature engineering at scale<\/td>\n<\/tr>\n
Big Data Infrastructure and Technology<\/strong><\/td>\nMove into the cloud. Learn how to design and deploy cloud architectures on AWS \u2014 covering compute, storage, databases, networking, and security \u2014 and apply them to real big data workloads using services like S3, EC2, and AWS big data tools.<\/td>\nAWS (EC2, S3, RDS, CloudWatch), cloud architecture design, ETL\/ELT on cloud, cost optimization, AWS certification preparation<\/td>\n<\/tr>\n
Deep Learning<\/strong><\/td>\nGo beyond classical machine learning into neural networks. Build and train deep architectures for image classification, object detection, sequence modeling, and generation. Explore CNNs, RNNs, LSTMs, GANs, and autoencoders \u2014 and learn how to explain what your models are doing with Explainable AI.<\/td>\nTensorFlow, Keras, CNN, RNN, LSTM, GAN, autoencoder, transfer learning, explainable AI (XAI)<\/td>\n<\/tr>\n
Model Deployment<\/strong><\/td>\nBuilding a model is only half the job. Learn to take models from notebooks to production \u2014 packaging them as REST APIs with FastAPI, orchestrating ML pipelines, deploying to AWS SageMaker, and monitoring model performance over time.<\/td>\nFastAPI, Scikit-learn pipelines, AWS SageMaker, Docker, MLOps, model monitoring, scalable training<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Beyond the Data Science Courses<\/h3>\n

Year 2 also deepens your computer science foundations with courses in algorithm design, database technology, computer networks, and operating systems \u2014 giving you the engineering intuition to build robust, efficient data systems.<\/p>\n

Year 2 is where ambition gets engineering behind it. You’ll finish it with the skills to build, ship, and scale real data science solutions.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Year 2 is the heart of the program. This is where you go from understanding data to mastering it \u2014 building predictive models, reasoning under uncertainty, engineering data pipelines at scale, and deploying your work into the real world. By the end of this year, you won’t just be analyzing data; you’ll be building production-ready […]<\/p>\n","protected":false},"author":709,"featured_media":472,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-460","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-courses"],"_links":{"self":[{"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/posts\/460","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/users\/709"}],"replies":[{"embeddable":true,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/comments?post=460"}],"version-history":[{"count":1,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/posts\/460\/revisions"}],"predecessor-version":[{"id":461,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/posts\/460\/revisions\/461"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/media\/472"}],"wp:attachment":[{"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/media?parent=460"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/categories?post=460"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/socs.binus.ac.id\/data-science\/wp-json\/wp\/v2\/tags?post=460"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}