Homework for summer version of MH3511 - Data Analysis with Computer

These are requirements, the list of tasks, the timeline, and the grading rubric for Fedor's (summer) version of MH3511 - Data Analysis with Computer.

Homework for summer version of MH3511 - Data Analysis with Computer

Notebook for practice in class 3 for summer version of MH3511 - Data Analysis with Computer

Notebook for practice in class 2 for summer version of MH3511 - Data Analysis with Computer

Homework for summer version of MH3511 - Data Analysis with Computer

Homework for summer version of MH3511 - Data Analysis with Computer

Topic modelling, sentiments etc.

By the end of this activity, you should 1. Summarize data with the package `tidyverse` 2. Train linear and polynomial regression in R 3. Train logistic regression in R 4. Print linear and logistic regressions as nicely formatted tables. 5. Explain what Receiver operating characteristic (ROC) is and how it can be used to estimate predictive power of a classifier.

By the end of this activity, you should 1. Manipulate data with the package `tidyverse` 2. Split the data into training and test sets. 3. Train a KNN model 4. Explain importance of data normalization for KNN 5. Measure and report the error of a regression model

By the end of this activity, you should 1. Get familiar with R notebooks 2. Recall basic functionality of R, such as libraries, variables, data types, printing.

This is a sample R markdown presentation

Gradient descent for logistic regression

Correlation diagrams of everything with everything

By the end of this activity, you should 1. Get familiar with R notebooks 2. Recall basic functionality of R, such as libraries, variables, data types, printing.

Some hypothetical plots for GradeME

We experiment with unsupervised learning techniques applied to genomic data on coronaviruses

Examples of simple analysis of ESSAI data.

We are investigating the data of IRA and TRA scores collected over 3 years teaching the course Ordinary Differential Equations at Nanyang Technological University. The number of teams is 107, the number of students 580.

Can topology predict a financial crisis?

Capstone project for Data Science Specialization on Coursera

Coursera data science - capstone project. Milestone report: exploratory analysis.

"Stopping distance prediction". Data science track, level 9 - Developing data products. Course project.

Details of comparing 11 linear models for the online course "Regression Models''

We have found that tornadoes caused the most deaths and injured the most people in the USA between 1950 and 2011. On the other hand, if we measure the average number of killed or injured people per event, then tsunamis are on top of the list. It means that a tsunami is, on average, deadlier than a tornado, but it is also a much rarer event. Heat waves are present in the top 5 most dangerous events according to either total or mean number of casualties. At the same time, thunderstorms and tornadoes have also caused the most total damage to property while hails caused the most damage to crops. Finally, hurricanes have caused the most average damage to both property and crops. Thus an individual hurricane is more harmful to economy than an individual tornado or hail, although hurricanes are rarer.