https://github.com/ben-aaron188/ucl_aca_20182019

Repo for the UCL 3rd year UG module Advanced Crime Analysis (Data Science)

https://github.com/ben-aaron188/ucl_aca_20182019

Repository

Repo for the UCL 3rd year UG module Advanced Crime Analysis (Data Science)

https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/

## DATA SCIENCE FOR CRIME SCIENTISTS (ADVANCED CRIME ANALYSIS) 2018/2019

This is the companion website for the 2018-2019 module for 3rd year undergraduate students of the BSc in Crime Science at UCL.


### Resources


The [module handbook](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/aca_SECU0050_module_outline.html) provides you with all information around assessment, learning outcomes, timetables, and a general overview of the module. Use the module handbook as your go-to guide throughout the module.


#### Week 1: INTRODUCTION

- Lecture 1: Introduction [(slides html)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture1_intro.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture1_intro.pdf)
- Homework 1: [Getting ready for R](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/getting_ready_for_r.html)
- Homework 2: [R in 12 Steps](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/r_in_12_steps.html)

Suggested reading:

- Williams, M. L., Burnap, P., & Sloan, L. (2017). Crime Sensing With Big Data: The Affordances and Limitations of Using Open-source Communications to Estimate Crime Patterns. The British Journal of Criminology, 57(2), 320340. [https://doi.org/10.1093/bjc/azw031](https://doi.org/10.1093/bjc/azw031)


Tutorial:

- [How to solve R data science problems](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/how_to_solve_data_science_problems.html), [SOLUTIONS](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/solutions_how_to_solve_data_science_problems.html)
- 17 Steps to investigate R dataframes - [https://www.rstatisticsblog.com/r-tutorial/dataframe-manipulations/](https://www.rstatisticsblog.com/r-tutorial/dataframe-manipulations/)


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#### Week 2: WEB SCRAPING 1

- Lecture 2: APIs and web-scraping  [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture2_apis.html), [pdf](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture2_apis.pdf)
- Homework: [Getting API access](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/week2_api_access.html), [SOLUTIONS](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/solutions_week2_api_access.Rmd)

Required reading/preparation:

- Pfeffer, J., Mayer, K., & Morstatter, F. (2018). Tampering with Twitters Sample API. EPJ Data Science, 7(1), 50. [https://doi.org/10.1140/epjds/s13688-018-0178-0](https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0178-0)


Suggested reading:

- Solymosi, R., Bowers, K. J., & Fujiyama, T. (2018). Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data. The British Journal of Criminology, 58(4), 944967. [https://doi.org/10.1093/bjc/azx048](https://doi.org/10.1093/bjc/azx048)
- Founta, A.-M., Djouvas, C., Chatzakou, D., Leontiadis, I., Blackburn, J., Stringhini, G.,  Kourtellis, N. (2018). Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. ArXiv:1802.00393 [Cs]. Retrieved from [http://arxiv.org/abs/1802.00393](http://arxiv.org/abs/1802.00393)


#### Week 3: WEB SCRAPING 2

- Lecture 3:  Webscraping with R [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture3_webscraping.html), [pdf](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture3_webscraping.pdf)
- Homework: -

Required reading:

- Mozilla MDN (2018). HTML basics. Retrieved January 6, 2019, from [https://developer.mozilla.org/en-US/docs/Learn/Getting_started_with_the_web/HTML_basics](https://developer.mozilla.org/en-US/docs/Learn/Getting_started_with_the_web/HTML_basics)
- R Web Scraping Tutorial with rvest. (2018, February 27). Retrieved January 20, 2019, from [https://www.datacamp.com/community/tutorials/r-web-scraping-rvest](https://www.datacamp.com/community/tutorials/r-web-scraping-rvest)
- Solares, J. R. A. (2017, August 2). Web scraping tutorial in R. Retrieved January 20, 2019, from [https://towardsdatascience.com/web-scraping-tutorial-in-r-5e71fd107f32](https://towardsdatascience.com/web-scraping-tutorial-in-r-5e71fd107f32)
- Dsilva, D. (2018, May 4). Learn To Create Your Own Datasets  Web Scraping in R. Retrieved January 20, 2019, from [https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5](https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5)
- Hadley Wickham (2016). rvest: Easily Harvest (Scrape) Web Pages. R package version 0.3.2. [https://CRAN.R-project.org/package=rvest](https://CRAN.R-project.org/package=rvest), [pdf](https://cran.r-project.org/web/packages/rvest/rvest.pdf)

Suggested reading:

- ElSherief, M., Kulkarni, V., Nguyen, D., Wang, W. Y., & Belding, E. (2018). Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media. ArXiv:1804.04257 [Cs]. Retrieved from [http://arxiv.org/abs/1804.04257](http://arxiv.org/abs/1804.04257)

Tutorial: [Webscraping and APIs in R](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/tutorial2_webscraping_in_R.nb.html), [SOLUTIONS](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/solutions_tutorial2_webscraping_in_R.nb.html)


#### Week 4: TEXT DATA 1

- Lecture 4:  Text data and text mining in R [slides](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture4_textdata1.html), [pdf](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture4_textdata1.pdf)
- Homework: [Text data basics](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/week4_textdata.nb.html), [SOLUTIONS](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/solutions_week4_textdata.nb.html)

Required reading:

- Grolemund, G., & Wickham, H. (2018). Strings. In R for Data Science. Retrieved from [https://r4ds.had.co.nz/](https://r4ds.had.co.nz/)

Suggested tutorials/reading:

- Replication of Chapter 5 of Quantitative Social Science: An Introduction. (n.d.). Retrieved January 26, 2019, from [https://quanteda.io/articles/pkgdown/replication/qss.html](https://quanteda.io/articles/pkgdown/replication/qss.html)
- Example: textual data visualization. (n.d.). Retrieved January 26, 2019, from [https://quanteda.io/articles/pkgdown/examples/plotting.html](https://quanteda.io/articles/pkgdown/examples/plotting.html)

Tutorial: - 


#### Week 5: TEXT DATA 2

- Lecture 5: Advanced text analysis in R [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture5_textdata2.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture5_textdata2.pdf)
- Tutorial: [Data cleaning and preprocessing and text mining in R](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/tutorial3_textmining_in_r.nb.html), [(raw Rmd file)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/tutorials/tutorial3_textmining_in_r.Rmd), [SOLUTIONS](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/solutions_tutorial3_textmining_in_R.nb.html)

Required reading:

- Kleinberg, B., Mozes, M., & Van der Vegt, I. (2018). Identifying the sentiment styles of YouTubes vloggers. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 35813590. Retrieved from [http://aclweb.org/anthology/D18-1394](http://aclweb.org/anthology/D18-1394)
- Prez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2018). Automatic Detection of Fake News. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 33913401). Santa Fe, New Mexico, USA: Association for Computational Linguistics. Retrieved from [http://aclweb.org/anthology/C18-1287](http://aclweb.org/anthology/C18-1287)



#### Week 6: MACHINE LEARNING 1

- Lecture 6: Machine learning in R 1 [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture6_ml1.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture6_ml1.pdf)
- Homework: [Supervised machine learning in R](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/week6_supervised_machinelearning.nb.html), [(SOLUTIONS)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/homework/week6_supervised_ML_solutions.nb.html)

Required reading:

- [http://www.r2d3.us/visual-intro-to-machine-learning-part-1/](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
- Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. New York: Springer-Verlag. Retrieved from https://www.springer.com/de/book/9781461468486
    - Chapter: "Introduction"
    - Chapter: "A Short Tour of the Predictive Modeling Process"
    
Recommended reading:

- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (2nd ed.). New York: Springer-Verlag. Retrieved from https://www.springer.com/de/book/9780387848570
    - Chapter: "Overview of Supervised Learning" 
    - Chapter: "Linear Methods for Classification"


#### Week 7: MACHINE LEARNING 2

- Lecture 7: Unsupervised machine learning in R + performance metrics [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture7_ml2.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture6_ml2.pdf)
- Tutorial: [Machine learning in R](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/tutorial4_machinelearning.nb.html), [(SOLUTIONS)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/tutorials/solutions_tutorial4_machinelearning.nb.html)

Required reading:

- Gatto, L. (n.d.). An Introduction to Machine Learning with R. Retrieved from https://lgatto.github.io/IntroMachineLearningWithR/unsupervised-learning.html
    - Chapter 4: Unsupervised learning

Recommended:

-  DataCamp course on Unsupervised Learning in R [https://www.datacamp.com/courses/unsupervised-learning-in-r](https://www.datacamp.com/courses/unsupervised-learning-in-r)


#### Week 8: PROMISES AND PROBLEMS

- Lecture 8: Advances, promises and problems of data science for crime science [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture8_promisesproblems.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture8_promisesproblems.pdf)
- No tutorial
- Homework: peer-feedback + your project + revision

Required reading

- Coveney, P. V., Dougherty, E. R., & Highfield, R. R. (2016). Big data need big theory too. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2080), 20160153. https://doi.org/10.1098/rsta.2016.0153

Recommended reading

- Quijano-Snchez, L., Liberatore, F., Camacho-Collados, J., & Camacho-Collados, M. (2018). Applying automatic text-based detection of deceptive language to police reports: Extracting behavioral patterns from a multi-step classification model to understand how we lie to the police. Knowledge-Based Systems, 149, 155168. https://doi.org/10.1016/j.knosys.2018.03.010
- Kadar, C., & Pletikosa, I. (2018). Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science, 7(1), 26. https://doi.org/10.1140/epjds/s13688-018-0150-z
- Burnap, P., & Williams, M. L. (2016). Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Science, 5(1), 11. https://doi.org/10.1140/epjds/s13688-016-0072-6


#### Week 9: RECAP, CASE STUDIES, PEER-FEEDBACK

- Lecture 9: Module recap, case studies [(slides)](https://raw.githack.com/ben-aaron188/ucl_aca_20182019/master/slides/aca_20182019_lecture9_recap_peerfeedback.html), [(pdf)](https://github.com/ben-aaron188/ucl_aca_20182019/blob/master/slides/aca_20182019_lecture9_recap_peerfeedback.pdf)
- Tutorial: project work.

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Module convenor and author: Bennett Kleinberg (bennett.kleinberg@ucl.ac.uk)

Department of Security and Crime Science, UCL

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Owner

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