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2.3 Application Areas · Jimmy Huang's Blog
课程目录
学习
←
Foundation of Data Science
学习
›
C6001
›
2025 S2
›
FIT5145 Foundations of data science
›
Foundation of Data Science
2.3 Application Areas
2.3 Application Areas
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在本课程目录里查找
课程目录
About
Foundation of Data Science
›
1 Data Science and Data in Society
1.1 Overview of Data Science
1.2 Melbourne Datathon
1.3 The Data Science Process
1.4 What is Data Science
1.5 Roles of a Data Scientist
1.6 Impact of Data Science
1.7 Activity: Motion Charts
2 Data Models in Organisations
2.1 Data and Decision Models
2.2 Business Models with Data
2.3 Application Areas
2.4 Analysis and Interviews
2.5 Activity: Decision Modelling
3 Data Types and Storage
3.1 Characterizing Data
3.2 Activity: Big Data
3.3 Data Case Studies
3.4 Big Data Processing
4 Data Resources, Processes, Standards and Tools
4.1 Introduction to Resources
4.2 Activity: DataWrangler
4.3 Standards and Issues
4.4 Case Studies of Data and Standards
5 Data Analysis Process
5.1 Introduction to Data Analysis
5.2 Theory of Data Analysis
5.3 Activity: Regression in iPython
5.4 Tools for the Data Analysis Process
5.5 Activity: Decision trees with BigML
5.6 Activity: Prediction with BigML
5.7 Data Analysis Case Studies
6 Data Curation and Management
6.1 Issues in Data Curation and Management
6.2 Frameworks for Data Management
7 Data Science Resources
Getting started
Week 1 Overview of data science
›
Extract from Leada's Data Analytics Handbook Part 1
Summary
Setting up RStudio
Introduction to R
Introduction to R Markdown
Week 2 History and impact of data science
›
Seminar Week 2
›
Summary
Summary
Class instructions
Activity 2.1: Describing the Job
›
备用
Activity 2.2 Your perception of the world
Activity 2.3: Motion Charts
›
备用指导
Assignment A1: Business & Data Case Study - 15%
›
Assignment 1 Specification
›
补充信息
Rubric
Assignment A3: Business & Data Case Study - 30%
›
Specification
›
Report - 17%
Feedback - 3%
Presentation - 10%
Rubric
Week 3 Data business models
›
Seminar Week 3 - Data business models
Class instructions
Lab
Week 4 Data sources and standards
›
Seminar Week 4
Class instructions
Activity 4.1: Stream Processing of Big Data
Week 5 Characterising data and "big" data
›
Class instructions
Week 6 Big data and data science
Week 7 Supervised data analysis (1)
Week 8 Supervised data analysis (2)
Assignment A2: R coding - 15%
›
Specification
Rubric
Week 9 Unsupervised data analysis
Week 10 Data management
Week 11 Issues in data science
Week 12 Future of data science & Guest lecture
Assignment A4: R & Shell scripting - 40%
›
Specification
Rubric
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2.4 Analysis and Interviews