Machine Learning & Predictive Analytics Boot Camp (PREDICTDATA)

Course Outline

Section I: Overview of Data Science

1. Data Science as a quantitative discipline

• How to define Data Science scopes
• The many faces of Data Science: Data Mining, Data Analysis, Data Analytics, Machine Learning, Predictive Modeling, Statistical Learning, Mathematical Modeling. What are these all about?
• Data Mining as a data exploration process
• Machine Learning: supervised vs. unsupervised
• Machine Learning vs. Predictive Analytics
• Big Data Analytics: what is it and why it's important

2. Overview of a Data Mining process cycle

• Understanding business needs and identifying new business opportunities
• Formulating a business problem and associated requirements
• Defining key quantitative metrics to measure success and evaluating business benefits
• Translating business requirements into technical requirements and documentations
• Formulating data models based on business and technical requirements
• Identifying a set of quantitative models based on technical requirements and metrics of success
• Running the models and evaluating results
• Selecting the best model
• Deploying the model

Section II: The Data Foundation

3. Data sources

4. Types of data

• Structured vs. unstructured data
• Static data vs. real-time data
• Types of data attributes: numerical vs. categorical
• Role of time factor and time trends in data analysis

5. Working with missing values

• Main causes of missing data
• Understanding the importance of missing information
• Types of missing information
• Restoring missing values
• Imputing missing values and selecting imputation techniques
• Understanding and evaluating potential consequences of manipulating records with missing values

6. Working with outliers

• Defining quantitative criteria for outlier detection in 1D cases
• Understanding role of outliers in model building
• Deciding on outlier removal
• Defining outlier detection metrics in multi-dimensional space

7. Working with duplicate records

• Defining duplicates
• Understanding sources of duplicates
• Deciding on duplicate removal

Section III: Sampling and Hypothesis Testing

8. Why sampling may be important for Machine Learning

9. Sampling techniques and sample bias

10. Statistical hypothesis

11. Z-score, t-score and F statistic

12. P-values

13. Implementation of hypothesis testing for model evaluation analysis

Section IV: Machine Learning Fundamentals

14. What is Machine Learning?

15. Supervised vs. unsupervised learning

16. Overview of supervised Machine Learning

• Regression models
• Classification models

17. Overview of unsupervised Machine Learning

• Clustering methods
• Principal component analysis and dimension reduction
• Association rules

18. Overview of major steps in building and testing quantitative models

• Criteria for model selection
• How to prepare a training set
• Criteria for selecting model attributes / predictors
• Working with collinear variables
• Addressing imbalance problem
• Dealing with over-fitting; bias-variance tradeoff
• Validation and cross-validation

Section V: Building a Linear Regression Model with R.

19. Univariate regression vs. multiple regression

20. Mathematical foundation of linear regression overview: least square method vs. maximum likelihood method

21. Model assumptions

22. Working with continuous attributes

23. Dealing with collinear variable

24. Model subset selection:

• Forward stepwise selection
• Backward selection
• Shrinkage methods: ridge regression and Lasso
• Dimension reduction
• Information criteria

25. Automating model selection procedure

26. Model parameter evaluation, R squared vs. adjusted R squared

27. Validating the model

28. Working with categorical variables

29. Considering input variable interactions

Section VI: Example of building a Classification Model with R

30. Dealing with imbalanced training sets

31. Understanding confusion matrix

32. Evaluating binary classifiers using ROC / AUC

Section VII: Example of Cluster Analysis with R

33. Overview of cluster analysis mathematical foundation

34. K-means clustering method

• Algorithm overview
• Convergence criteria
• How to determine the number of clusters

Section VIII: Dimension Reduction techniques with R

35. What is dimension reduction?

36. The practical goals of dimension reduction implementation

37. Principal component analysis vs. singular value decomposition

38. How many components to choose

Section IX: Class Conclusion

39. What was not covered in the class

40. Big Data Analytics – the future of machine learning: main tools and concepts