• Unit guide
• Moodle (for discussion forum, assignments, grades, protected material)
• Textbook website
• Online short course videos
• Unofficial solutions to textbook exercises

• Lectures:
  • Monday, 2pm-3pm, CL_15 Ancora Imparo Way, Room L1 - Law Theatre (Bldg 12)
  • Thursday, 12pm-1pm, CL_21 College Walk, Room E5, Eng Theatre (Bldg 32)
• Labs:
  • (with Mojdeh) Wednesday, 9:30am-11:00am, CL_20 Chancellors Walk, Room S317 Computer Lab (Building 11)
  • (with Zina) Wednesday, 11:00am-12:30pm, CL_20 Chancellors Walk, Room S317 Computer Lab (Building 11)
  • (with Mojdeh) Wednesday, 12:30pm-2pm, CL_20 Chancellors Walk, Room S317 Computer Lab (Building 11)

• Souhaib: Monday, 1pm-2pm, Room E765, Menzies Building, Clayton campus
• Mojdeh: Wednesday, 2pm-3:30pm, Clayton campus, room W1105
• Zina: Wednesday, 1pm-1:45pm, Clayton campus, room W1105

If you feel like you need more practice in using RStudio, there are lots of free online tutorials. Some good ones (in order of difficulty) are listed below.

• tryr.codeschool.com
• www.datacamp.com/courses/introduction-to-r
• www.cyclismo.org/tutorial/R/
• www.coursera.org/course/rprog
• https://github.com/dicook/SISBID-2016
• dicook.github.io/Monash-R
• www.cookbook-r.com
• Using RStudio

• Project 2017
• Training data

• Week 1. Introduction: Ch1

  • Lecture 1 (Jul. 24): Introduction to Business Analytics and R [slides]
  • Lab 1: Introduction to R [lab 1 (pdf)] [lab 1 (Rmd)] [solutions (pdf)]
  • Lecture 2 (Jul. 27): Statistical learning [slides]

• Week 2. Statistical Learning: Ch2

  • Lecture 3 (Jul. 31): Statistical learning [slides] [proof] [Example (R code)]
  • Lab 2: Statistical Learning and R [lab 2 (pdf)] [lab 2 (Rmd)] [solutions (pdf)] [solutions (Rmd)]
  • Lecture 4 (Aug. 3): Statistical learning [slides]

• Week 3. Regression: Ch3, 7

  • Lecture 5 (Aug. 7): Linear regression [slides] [Credit scores example (R code)] (Dr. Anastasios Panagiotelis)
  • Lab 3: More R programming (exercises without assignment) [lab 3 (pdf)] [solutions (R)]
  • Lecture 6 (Aug. 10): Flexible regression [slides] [GAM example (R code)] (Dr. David Frazier)

• Week 4. Classification: Ch4

  • Lecture 7 (Aug. 14): Logistic regression [slides]
  • Lab 4: Regression and K-NN classification [lab 4 (pdf)] [lab 4 (Rmd)] [solutions (Rmd)] [solutions (pdf)]
  • Lecture 8 (Aug. 17): Linear discriminant analysis [slides]

• Week 5. Classification: Ch4, 9

  • Lecture 9 (Aug. 21): Comparison of classifiers [slides]
  • Lab 5: Classification [lab 5 (pdf)] [lab 5 (Rmd)] [solutions (Rmd)] [solutions (pdf)]
  • Lecture 10 (Aug. 24): Support vector machines [slides]

• Week 6. Model selection and resampling methods: Ch5

  • Lecture 15 (Aug. 28): Model assessment and selection [slides] [proof]
  • Lab 6: Model selection [lab 6 (pdf)] [lab 6 (Rmd)] [solutions (Rmd)] [solutions (pdf)]
  • Lecture 16 (Aug. 31): Resampling methods [slides]

• Week 7. Dimension reduction: Ch10

  • Lecture 17 (Sep. 4): Principal Components Analysis [slides]
  • Lab 7: Bootstrapping [lab 7 (pdf)] [lab 7 (Rmd)] [solutions (Rmd)] [solutions (pdf)]
  • Lecture 18 (Sep. 7): Other dimensionality reduction methods [slides]

• Week 8. Advanced regression

  • Lecture 21 (Sep. 11): Advanced regression [slides]
  • Lab 8: Principal Components Analysis [lab 8 (pdf)] [lab 8 (Rmd)]
  • Lecture 22 (Sep. 14): Advanced regression

• Week 9. Advanced learning methods

  • Lecture 23 (Sep. 18): Trees and forests
  • Lab 9: Advanced regression
  • Lecture 24 (Sep. 21): Bagging, Random Forests, Boosting

• Semester break

• Week 10. Clustering

  • Lecture 19 (Oct. 2): K-means clustering
  • Lab 10: Advanced classification
  • Lecture 20 (Oct. 5): Hierarchical clustering

• Week 11. Visualisation and Data wrangling

  • Lecture 11 (Oct. 9): Visualisation
  • Lab 11: Clustering
  • Lecture 12 (Oct. 12): Data wrangling

• Week 12. Data wrangling

  • Lecture 13 (Oct. 16): Project presentation (I/II)
  • Lab 12: Data wrangling and visualisation
  • Lecture 14 (Oct. 19): Project presentation (II/II)