Common modes of access to business data

Common modes of access to business dataQ1. If the required data are in one or more database structures, discuss three common modes of access to business dataQ2. Given the following data extracts in a data warehouse, show how you can integrate these records to produce an analytical record.Q3. Given the following data extract, how can you decide whether the value 454 is an outlier?Q4. What does the data distribution mean to you? And how much they are related to the data modeling process?Q5. What do we mean be Data Auditing? And how it can be applied to the data set under hand?Q6. The inclusion of erroneous data or blanks in the modeling data set will decrease the predictive power of the model; Suggest a procedure to validate the data set?s to detect such entries.Q7. What is the difference between the Parametric Statistical Routines and the Machine Learning approach for data modeling?Q8. Algorithms that depend on calculations of covariance (e.g., regression) or that require other numerical operations must operate on numbers; how can these methods be applied to variables may contain textual categories rather than numeric values?Q9. How do relate Generality of a data model to the accuracy of the model?Q10. What is model overfitting? And how much does it affect the predictive power of the model?Q11. When data are missing in a variable of a particular case, it is very important to fill this variable with some intuitive data; suggest a procedure for handling missing values that will never degrade the prediction ability of the model.Q12. A data value input from source-A may be twice as accurate as data from source-B, more accurate data will reinforce the prediction power of a model; how can you manage to make source-A data to contribute more to the model?Q13. Given the following data sheet of history of estimate time for a computer repair business. Explain in details how can you quote a repair time for a customer? Days 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Freq. 1 5 3 4 1 7 3 12 15 17 18 22 10 7 4 1Q14. Removing unnecessary information reduces the noise below the level that can confuse the analysis; 1. What data can be unnecessary? 2. Suggest some measures that can be used in identifying such entries.Q15.Q16. In the experience of many data analytics, some of the most predictive variables are those you derive yourself; how do think this can be done?Q17.Q18.Q19.Q20.Q21.Q22.Q23.Q24. What is the Curse of Dimensionality? Can it be eliminated? How is that?Q25. How do you determine which variables are unnecessary to the model before you train the model?Q26.Q27.Q28.:

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