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Quantitative Applications for Data Analysis
(Winter 2019)
(Old site; new site is at https://scinet.courses)
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Thursday May 23, 2024 - 22:14
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1 Description
2 Software Requirements
3 Lectures
3.1 Intro to Linux Shell I
3.2 Intro to Linux Shell II
3.3 Introduction to R
3.4 Vectors, data frames, loops
3.5 Functions
3.6 Scripts
3.7 Version Control - Git
3.8 Coding Best Practices
3.9 Distributions
3.10 Multi-sample Tests
3.11 One-sample Tests
3.12 Linear Models
3.13 Visualization in R
3.14 Resampling
3.15 Introduction to Python
3.16 Loops and functions in Python
3.17 NumPy & SciPy
3.18 Visualization and ODEs
3.19 Machine Learning
3.20 Pandas
3.21 Classification II
3.22 Clustering
3.23 Neural Networks
3.24 Text Analysis
4 Recordings
4.1 Intro to Linux Shell I
4.2 Intro to Linux Shell II
4.3 Intro to R
4.4 Vectors and Data Frames
4.5 Functions
4.6 Scripts
4.7 Version Control - Git
4.8 Best Practices
4.9 Distributions
4.10 Multi-sample Tests
4.11 One-sample Tests
4.12 Linear Models
4.13 Visualization
4.14 Resampling
4.15 Introduction to Python
4.16 Python Loops and Functions
4.17 Numpy and Scipy
4.18 Visualization and ODEs
4.19 Machine Learning
4.20 Pandas
4.21 Classification II
4.22 Clustering
4.23 Neural Networks
4.24 Text Analysis
5 Assignments
5.1 Assignment 1
5.2 Assignment 2
5.3 Assignment 3
5.4 Assignment 4
5.5 Assignment 5
5.6 Assignment 6
5.7 Assignment 7
5.8 Assignment 8
5.9 Assignment 9
5.10 Assignment 10
5.11 Assignment 11 - Make Up
Course Calendar
Related
Quantitative Applications for Data Analysis (Winter 2021)
Quantitative Applications for Data Analysis (Winter 2020)
Quantitative Applications for Data Analysis (Winter 2018)
Quantitative Applications for Data Analysis (Winter 2017)
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