Dell EMC Data Science and BigData Certification Questions and Answers

Question : Which word or phrase completes the statement? Structured data is to OLAP data as quasistructured
data is to____

1. Text documents
2. XML data
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4. Image files

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Explanation: A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing or using another software application. As the user clicks anywhere in the webpage or
application, the action is logged on a client or inside the web server, as well as possibly the web browser, router, proxy server or ad server. Clickstream analysis is useful for web activity analysis,[1] software
testing, market research, and for analyzing employee productivity. Initial clickstream or click path data had to be gleaned from server log files. Because human and machine traffic were not differentiated, the study
of human clicks took a substantial effort. Subsequently Javascript technologies were developed which use a tracking cookie to generate a series of signals from browsers. In other words, information was only collected
from "real humans" clicking on sites through browsers.

A clickstream is a series of page requests, every page requested generates a signal. These signals can be graphically represented for clickstream reporting. The main point of clickstream tracking is to give webmasters
insight into what visitors on their site are doing.This data itself is "neutral" in the sense that any dataset is neutral. The data can be used in various scenarios, one of which is marketing. Additionally, any
webmaster, researcher, blogger or person with a website can learn about how to improve their site. Use of clickstream data can raise privacy concerns, especially since some Internet service providers have resorted to
selling users' clickstream data as a way to enhance revenue. There are 10-12 companies that purchase this data, typically for about $0.40/month per user.[3] While this practice may not directly identify individual
users, it is often possible to indirectly identify specific users, an example being the AOL search data scandal. Most consumers are unaware of this practice, and its potential for compromising their privacy. In
addition, few ISPs publicly admit to this practice.

Analyzing the data of clients that visit a company website can be important in order to remain competitive. This analysis can be used to generate two findings for the company, the first being an analysis of a user's
clickstream while using a website to reveal usage patterns, which in turn gives a heightened understanding of customer behaviour. This use of the analysis creates a user profile that aids in understanding the types of
people that visit a company's website. As discussed in Van den Poel and Buckinx (2005), clickstream analysis can be used to predict whether a customer is likely to purchase from an e-commerce website. Clickstream
analysis can also be used to improve customer satisfaction with the website and with the company itself. This can generate a business advantage, and be used to assess the effectiveness of advertising on a web page or
site.

Data mining, column-oriented DBMS, and integrated OLAP systems can be used in conjunction with clickstreams to better record and analyze this data.

Clickstreams can also be used to allow the user to see where they have been and allow them to easily return to a page they have already visited, a function that is already incorporated in most browsers.

Unauthorized clickstream data collection is considered to be spyware. However, authorized clickstream data collection comes from organizations that use opt-in panels to generate market research using panelists who
agree to share their clickstream data with other companies by downloading and installing specialized clickstream collection agents. Potential Uses of Clickstream Data
One of the original uses of Hadoop at Yahoo was to store and process their massive volume of clickstream data. Now enterprises of all types can use Hadoop and the Hortonworks Data Platform (HDP) to refine and analyze
clickstream data. They can then answer business questions such as:

What is the most efficient path for a site visitor to research a product, and then buy it?
What products do visitors tend to buy together, and what are they most likely to buy in the future?
Where should I spend resources on fixing or enhancing the user experience on my website?
In this tutorial, we will focus on the "path optimization" use case. Specifically: how can we improve our website to reduce bounce rates and improve conversion?

Question : What describes a true property of Logistic Regression method?

1. It handles missing values well.
2. It works well with discrete variables that have many distinct values.
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4. It works well with variables that affect the outcome in a discontinuous way.

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Explanation: What is a person's expected income?
What is the probability that an applicant will default on a loan?
Linear regression is a useful tool for answering the first question, and logistic regression is
a popular method for addressing the second. This chapter examines these two regression
techniques and explains when one technique is more appropriate than the other. In linear regression modeling, the outcome variable is a continuous variable. As seen in the earlier Income example, linear regression can
be used to model the relationship between age and education to income. Suppose a person's actual income was not of interest, but rather whether someone was wealthy or poor. In such a case, when the outcome variable is
categorical in nature, logistic regression can be used to predict the likelihood of an outcome based on the input variables. Although logistic regression can be applied to an outcome variable that represents multiple
values, the following discussion examines the case in which the outcome variable represents two values such as true/false, pass/fail, or yes/no. For example, a logistic regression model can be built to determine if a
person will or will not purchase a new automobile in the next 12 months. The training set could include input variables for a person's age, income, and gender as well as the age of an existing automobile. The training
set would also include the outcome variable on whether the person purchased a new automobile over a 12-month period. The logistic regression model provides the likelihood or probability of a person making a purchase
in the next 12 months. After examining a few more use cases for logistic regression, the remaining portion of this chapter examines how to build and evaluate a logistic regression model. The logistic regression model
is applied to a variety of situations in both the public and the private sector. Some common ways that the logistic regression model is used include the following:
Medical: Develop a model to determine the likelihood of a patient's successful response to a specific medical treatment or procedure. Input variables could include age, weight, blood pressure, and cholesterol levels.
Finance: Using a loan applicant's credit history and the details on the loan, determine the probability that an applicant will default on the loan. Based on the prediction, the loan can be approved or denied, or the
terms can be modified. Marketing: Determine a wireless customer's probability of switching carriers (known as churning) based on age, number of family members on the plan, months remaining on the existing contract,
and social network contacts. With such insight, target the high-probability customers with appropriate offers to prevent churn. Engineering: Based on operating conditions and various diagnostic measurements, determine
the probability of a mechanical part experiencing a malfunction or failure. With this probability estimate, schedule the appropriate preventive maintenance activity.

Question : You have been assigned to do a study of the daily revenue effect of a pricing model of online
transactions. You have tested all the theoretical models in the previous model planning stage, and
all tests have yielded statistically insignificant results. What is your next step?

1. Run all the models again against a larger sample, leveraging more historical data.
2. Report that the results are insignificant, and reevaluate the original business question.
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4. Modify samples used by the models and iterate until a significant result occurs.

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Dell EMC Data Science and BigData Certification Questions and Answers

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