Business Intelligence and Analytics
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1) Share an example of a situation that you imagine you would use business analytics, business intelligence, visualization and reporting terminologies and processes.
2) Conduct a brief research and share a new trend in business intelligence solutions regarding incorporation of business analytics.
1) What is a factor variable? When would you want to use a factor variable?
2) What is unique about a numeric variable?
3) Why would you use a data frame over a vector to store your data?
4) Lists are the most complex of the R data types. What is the practical benefit of a list that stores a string, a numeric vector, and a matrix? Give real life examples.
- What are various ways and libraries that provide similar capabilities for Importing data from CSV and text files as well as other forms of datafile such as excel file formats? (investigate many R libraries that are available beyond the required content)
- Compare and contrast the functionality of for loop, while loop, with apply functions? Provide your unique examples and show the differences by a script that your classmates can run to see the difference
- Compare and contrast different measures of central tendency and describe what the strength and weakness of each are.
- Compare and contrast various measures of dispersion and explain what the strength and weakness of each are.
- Use a dataset of your choice and use ggplot2 to visualize various attributes in the dataset. Do not use a similar dataset as your classmates. Once one of your classmates used a dataset, you choose a different one. Share your scripts and graphic output with your classmates.
- Describe various types of Discrete, Continuous, Nominal, Ordinal, Interval, and Ratio data and provide unique examples for each one.
- Describe Relational databases, SQL and NoSQL databases.
- Compare and contrast SQL databases such as MySQL and NoSQL databases MongoDB.
- Describe supervised, unsupervised, and reinforcement machine learning.
- Provide real life business examples for when you would use supervised, unsupervised, and reinforcement machine learning.
- Describe how the confusion matrix is used for model evaluation.
- Describe the definition of accuracy, precision, recall, sensitivity, specificity, True positive rate, False positive rate.
- Synthesize real-life examples where you would rely on each one of these measures to evaluate prediction, or classification, performance of your model.
- What is a Lift chart? Explain in what situations you would use it?
- What is ROC chart? Explain in what situations you would use it?
- Describe the Naive Bayes Classifier algorithm and its uses, what is the suitable data scale (level) of predicted outcome in Naive Bayes Classifier? What is the data scale (level) of predictor variables in Naive Bayes Classifier?
- Describe the KNN algorithm and its uses, what is the data suitable scale (level) of predicted outcome in KNN? What is the data Scale (level) of predictor variables in KNN?
- Provide unique examples of business situations that you would use Naive Bayes Classifier or KNN for making decisions such as classifying your potential customers.
- Describe how classification trees work.
- Describe the overfitting and underfitting problems?
- Compare and contrast various ways to choose the optimum size of the tree?
- Provide unique examples of business situations that you would use classification trees for making decisions such as classifying your potential customers.
- Describe when what are the advantages and disadvantages of using Naive Bayes Classifier or KNN vs classification trees.
- Describe hierarchical and k-means clustering [CLO 6].
- Argue for advantages and disadvantages of each algorithm [CLO 6].
- Describe the market basket analysis and its applications [CLO 6].
- Provide unique examples of business situations that you would use market basket analysis, and hierarchical or k-means clustering for making decisions [CLO 6].
- What are attributes of the big data and the drivers of business interest in it?
- Compare and contrast the role of various modern solutions to difficulties regarding big data analysis.
- Provide unique examples of business situations that you would need big data and would use deep learning. Why would you need Apache Hadoop, MapReduce, Yarn, Hadoop Distributed File System and neural networks?
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