Big data Analysis

1. Big data:
1. Definition, organization and storage,
2. Data warehouses,
3. Preprocessing and structuring data,
4. Basic analytic tools and approaches; Hadoop and MapReduce
5. Missing values,
6. Outliers,
7. Choice of variables,
8. Segmentation,

2. Data mining overview:
1. Predictive models: linear regression, logistic regression, decision trees, neural networks, support vector machines,
2. Evaluation of models,
3. Descriptive analytics:association rules, segmentation.

3. Text mining:
1. Acquiring data,
2. Extracting information;

4. Web mining:
1. Acquring web data,
2. Web content analysis,

5. Fields of data mining applications:
1. Credit risk scoring,
2. Fraud detection,
3. Churn prediction,
4. Recommendation systems,
5. Web analytics,
6. Social media analytics,
7. Business processes analytics.

Required Readings:

1. Bart Baesens. Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014. https://plus.cobiss.net/cobiss/si/sl/bib/fflj/2560000000148561

2. Richard Boire. Data Mining for Managers. How to Use Data (Big and Small) to Solve Business Challenges. ISBN: 978-1-349-48786-8 (Print) 978-1-137-40619-4 (Online). https://plus.cobiss.net/cobiss/si/sl/bib/fflj/4200000000000024

Dodatna literatura/Additional Readings:

3. CHAKRABARTI, Soumen. Mining the Web : discovering knowledge from hypertext data / Soumen Chakrabarti. - Amsterdam [etc.] : Morgan Kaufmann, 2007, cop. 2003. - XVIII, 345 str. : graf. prikazi ; 25 cm. - (The Morgan Kaufmann series in data management systems). https://plus.cobiss.net/cobiss/si/sl/bib/fflj/111087028168028

4. HASTIE, Trevor. The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman. - 2nd ed., corrected at 5th printing 2011. - New York : Springer, 2011, cop. 2009. - XXII, 745 str. : ilustr. ; 24 cm. - (Springer series in statistics, ISSN 0172-7397). COBISS.SI-ID – 40339554 https://plus.cobiss.net/cobiss/si/sl/bib/fflj/1000000000746280