FIT1043

Introduction to data science

6 credit pointsLevel 1First semester / Second semesterMalaysiaFaculty of Information Technology

基本信息

学分	6 credit points
开课学期	First semester / Second semester
校区	Malaysia
考核构成	In-Class examination — 10% Data science assignment 2 — 20% Data science assignment 1 — 20% Semester 1: Scheduled final assessment (2 hours and 10 minutes) — 50% Semester 2: Scheduled final assessment (2 hours and 10 minutes) — 50%

开课安排2 条

教学期	授课方式	状态
Second semester	Teaching activities are on-campus (ON-CAMPUS)	开课
First semester	Teaching activities are on-campus (ON-CAMPUS)	开课

以上为该校区在官方资料中登记的全部开课安排，不是汇总。同一门课可能在多个教学期开课，也可能不同教学期的授课方式不同。

课程简介

This unit looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.

以上为 Monash Handbook 的官方原文，版权属 Monash University，此处按本站要求转载并标注出处：官方页面 ↗

学习成果6 条

官方原文（Learning outcomes），版权属 Monash University。

ULO1 Detail the phases of the data science lifecycle and differentiate the roles involved in a data science project.
ULO2 Implement strategies for acquiring, cleaning, and organising data prior to analysis.
ULO3 Utilise basic data analysis models to extract insights and critique their effectiveness.
ULO4 Understand fundamental properties of Big Data and their influence on storage and processing and evaluate the strengths and weaknesses of Big Data tools for specific contexts.
ULO5 Examine data science projects by identifying and discussing inherent ethical, privacy, and data management issues, including their broader impacts.
ULO6 Apply commonly used data science software and programming languages to interpret results across a diverse range of scenarios.

教学方式与预期工作量

教学方式

Active learning

预期工作量

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

官方原文，版权属 Monash University。