THE DATA ANALYTICS JOURNEY D204
EXAM QUESTIONS WITH CORRECT
ANSWERS
implicit rules - ANSWER-o the implicit rules are rules that help the algorithms
function. They are the rules that they develop by analyzing the test data. And they're
implicit because they cannot be easily described to humans.
Microsoft Excel and its many versions. Google Sheets - ANSWER-spreadsheets the
universal data tool. It's my untested theory that there are more datasets in
spreadsheets than in any other format in the world. The rows and columns are very
familiar to a very large number of people and they know how to explore the data and
access it using those tools. The most common by far
MLaaS - ANSWER-machine learning as a service.Amazon Machine Learning, and
Google AutoML, and IBM Watson Analytics,
Algebra - ANSWER-Number one is that it allows you to scale up. The solution you
create to a problem should deal efficiently with many instances at once. Basically
create it once, run it many times. And the other one closely related to that is the
ability to generalize. Your solution should not apply to just a few specific cases with
what's called Magic Numbers, but to cases that vary in a wide range of arbitrary
ways, so you want to prepare for as many contingencies as possible
Calculus - ANSWER-to do a maximization and a minimization, when you're trying to
find the balance between these disparate demands.
Optimization and the combinatorial explosion - ANSWER-You're trying to find an
optimum solution, but randomly going through every possibility doesn't work. This is
called the combinatorial explosion because the growth is explosive as the number of
units and the number of possibilities rises and so you need to find another way that
can save you some time and still help you find an optimum solution.
Bayes' theorem - ANSWER-What Bayes' Theorem does is it gives you the posterior
or after-the-data probability of a hypothesis as a function of the likelihood of the data
given the hypothesis, the prior probability of the hypothesis and the probability of
getting the data you found.
Descriptive analyses - ANSWER-descriptive analyses are one way of doing this. It's
a little like cleaning up the mess in your data to find clarity in the meaning of what
you have. And I like to think that there are three very general steps to descriptive
statistics. Number one, visualize your data, make a graph and look at it. Number two,
compute univariate descriptive statistics. There's things like the mean. It's an easy
way of looking at one variable at a time. And then go on to measures of association,
or the connection between the variables in your data.
,Steps for Descriptive Analyses - ANSWER-looking at your data through charts, i.e
Historgram.
skews - ANSWER-**positively-skewed distributions ie Think of the valuations at
companies, the cost of houses. negative skew, where most of the people are at the
high end and the trailing ones are at the low end. If you think of something like birth
weight. U-shaped distribution:polarizing movie and the reviews that it gets**
what's a univariate descriptive - ANSWER-you can look for one number that might
be able to represent the entire collection.
measures that give you a numerical description of association - ANSWER-correlation
coefficient or regression analysis
Predictive models - ANSWER-Find and use relevant past data. It doesn't have to be
really old. It can be data from yesterday. But you always have to use data in the past
because that's the only data you can get. And then you model the outcome using
any of many possible choices.
Predictive Model Critical Step - ANSWER-validate your model by testing it against
new data, often against data that's been set aside for this very purpose. This is the
step that's often neglected in a lot of scientific research, but it's nearly universal in
predictive analytics and it's a critical part of making sure that your model works well
outside of the constraints of the data that you had available
predictive analytics - ANSWER-One of them is trying to predict future events, and
that's using presently available data to predict something that will happen later in the
future, or use past medical records to predict future health. The other possibly more
common use is using prediction to refer to alternative events, that is, approximating
how a human would perform the same task. So you're going to have a machine do
something like classifying photos and you want to see whether this is a person
decomposition - ANSWER-breaking things down from the whole into their elements,
to try to see what's happening with your data over time. This is decomposition. Think
of it like disassembling a clock or some other item. You're going to take the trend
over time and break it down into several separate elements. You're going to look at
the overall trend, you're going to look at seasonal or a cyclical trend, and you're
going to have some leftover random noise
Clustering - ANSWER-You can look at things like a K-dimensional space. So you
locate each data point, each observation, in a multidimensional space with K-
dimensions for K variables. So if you have five dimensions, K is five. If you have 500,
then you have 500 dimensions. What you need to do then, is you need to find a way
to measure the distance between each point, and you're going to do one point, every
other point, and you're looking for clumps and gaps.
Cluster Analysis Methods - ANSWER-hierarchical clustering,K-means, or a group
centroid model. You can use density models or distribution models, or a linkage
clustering model
, Creating data/Get your own Data - ANSWER-natural observation, informal
discussions with, for instance, potential clients. You can do this in person in a one on
one, or a focus group setting. You can do it online through email, or through chat,
and this time you're asking specific questions to get the information you need to
focus your own projects. Surveys. Words > Numbers. Let ppl express themselves.
Start general
Research Ethics when gathering data - ANSWER-informed consent,Also sometimes
confidentiality, or anonymity
Passive collection of training data - ANSWER-gathering enormous amounts of data
doesn't always involve enormous amounts of work. In certain respects, you can just
sit there and wait for it to come to you. Photo Classificaiton. issue with this:One, and
this is actually a huge issue, is that you need to ensure that you have adequate
representation; things like categorizing photos/ limit cases
Self-generated data - ANSWER-external reinforcement learning.generative
adversarial networks. internal
The enumeration of explicit rules - ANSWER-business strategies, flowcharts, Or
criteria for medical diagnoses.
expert system - ANSWER-An expert system is an approach to machine decision-
making in which algorithms are designed that mimic the decision-making process of
a human domain expert.
linear regression - ANSWER-linear regression, which is a common and powerful
technique for combining many variables in an equation to predict a single outcome.
decision tree
decision tree - ANSWER-This is a whole series, a sequence of binary decisions,
based on your data, that can combine to predict an outcome. It's called a tree
because it branches out from one decision to the next
Neural networks - ANSWER-look at things in a different way than humans do and in
certain situations they're able to develop rules for classification, even when humans
can't see anything more than static.
Classifying - ANSWER-Locate the case in a k-dimensional space where k is the
number of variables or different kinds of information that you have. And there's
probably going to be more than three. It might be hundreds or thousands. But once
you get it located in that space, compare the labels on nearby data, that of course
assuming that other data already has labels that it says whether it's a photo of a cat,
or a dog, or a building. And then once you've done that, assign the new case to the
same category. LOCATE,COMPARE, ASSIGN
Classifying Methods - ANSWER-K-means, k nearest neighbors. BInary
Classificaiton. Many Categories, Distance Measures.
EXAM QUESTIONS WITH CORRECT
ANSWERS
implicit rules - ANSWER-o the implicit rules are rules that help the algorithms
function. They are the rules that they develop by analyzing the test data. And they're
implicit because they cannot be easily described to humans.
Microsoft Excel and its many versions. Google Sheets - ANSWER-spreadsheets the
universal data tool. It's my untested theory that there are more datasets in
spreadsheets than in any other format in the world. The rows and columns are very
familiar to a very large number of people and they know how to explore the data and
access it using those tools. The most common by far
MLaaS - ANSWER-machine learning as a service.Amazon Machine Learning, and
Google AutoML, and IBM Watson Analytics,
Algebra - ANSWER-Number one is that it allows you to scale up. The solution you
create to a problem should deal efficiently with many instances at once. Basically
create it once, run it many times. And the other one closely related to that is the
ability to generalize. Your solution should not apply to just a few specific cases with
what's called Magic Numbers, but to cases that vary in a wide range of arbitrary
ways, so you want to prepare for as many contingencies as possible
Calculus - ANSWER-to do a maximization and a minimization, when you're trying to
find the balance between these disparate demands.
Optimization and the combinatorial explosion - ANSWER-You're trying to find an
optimum solution, but randomly going through every possibility doesn't work. This is
called the combinatorial explosion because the growth is explosive as the number of
units and the number of possibilities rises and so you need to find another way that
can save you some time and still help you find an optimum solution.
Bayes' theorem - ANSWER-What Bayes' Theorem does is it gives you the posterior
or after-the-data probability of a hypothesis as a function of the likelihood of the data
given the hypothesis, the prior probability of the hypothesis and the probability of
getting the data you found.
Descriptive analyses - ANSWER-descriptive analyses are one way of doing this. It's
a little like cleaning up the mess in your data to find clarity in the meaning of what
you have. And I like to think that there are three very general steps to descriptive
statistics. Number one, visualize your data, make a graph and look at it. Number two,
compute univariate descriptive statistics. There's things like the mean. It's an easy
way of looking at one variable at a time. And then go on to measures of association,
or the connection between the variables in your data.
,Steps for Descriptive Analyses - ANSWER-looking at your data through charts, i.e
Historgram.
skews - ANSWER-**positively-skewed distributions ie Think of the valuations at
companies, the cost of houses. negative skew, where most of the people are at the
high end and the trailing ones are at the low end. If you think of something like birth
weight. U-shaped distribution:polarizing movie and the reviews that it gets**
what's a univariate descriptive - ANSWER-you can look for one number that might
be able to represent the entire collection.
measures that give you a numerical description of association - ANSWER-correlation
coefficient or regression analysis
Predictive models - ANSWER-Find and use relevant past data. It doesn't have to be
really old. It can be data from yesterday. But you always have to use data in the past
because that's the only data you can get. And then you model the outcome using
any of many possible choices.
Predictive Model Critical Step - ANSWER-validate your model by testing it against
new data, often against data that's been set aside for this very purpose. This is the
step that's often neglected in a lot of scientific research, but it's nearly universal in
predictive analytics and it's a critical part of making sure that your model works well
outside of the constraints of the data that you had available
predictive analytics - ANSWER-One of them is trying to predict future events, and
that's using presently available data to predict something that will happen later in the
future, or use past medical records to predict future health. The other possibly more
common use is using prediction to refer to alternative events, that is, approximating
how a human would perform the same task. So you're going to have a machine do
something like classifying photos and you want to see whether this is a person
decomposition - ANSWER-breaking things down from the whole into their elements,
to try to see what's happening with your data over time. This is decomposition. Think
of it like disassembling a clock or some other item. You're going to take the trend
over time and break it down into several separate elements. You're going to look at
the overall trend, you're going to look at seasonal or a cyclical trend, and you're
going to have some leftover random noise
Clustering - ANSWER-You can look at things like a K-dimensional space. So you
locate each data point, each observation, in a multidimensional space with K-
dimensions for K variables. So if you have five dimensions, K is five. If you have 500,
then you have 500 dimensions. What you need to do then, is you need to find a way
to measure the distance between each point, and you're going to do one point, every
other point, and you're looking for clumps and gaps.
Cluster Analysis Methods - ANSWER-hierarchical clustering,K-means, or a group
centroid model. You can use density models or distribution models, or a linkage
clustering model
, Creating data/Get your own Data - ANSWER-natural observation, informal
discussions with, for instance, potential clients. You can do this in person in a one on
one, or a focus group setting. You can do it online through email, or through chat,
and this time you're asking specific questions to get the information you need to
focus your own projects. Surveys. Words > Numbers. Let ppl express themselves.
Start general
Research Ethics when gathering data - ANSWER-informed consent,Also sometimes
confidentiality, or anonymity
Passive collection of training data - ANSWER-gathering enormous amounts of data
doesn't always involve enormous amounts of work. In certain respects, you can just
sit there and wait for it to come to you. Photo Classificaiton. issue with this:One, and
this is actually a huge issue, is that you need to ensure that you have adequate
representation; things like categorizing photos/ limit cases
Self-generated data - ANSWER-external reinforcement learning.generative
adversarial networks. internal
The enumeration of explicit rules - ANSWER-business strategies, flowcharts, Or
criteria for medical diagnoses.
expert system - ANSWER-An expert system is an approach to machine decision-
making in which algorithms are designed that mimic the decision-making process of
a human domain expert.
linear regression - ANSWER-linear regression, which is a common and powerful
technique for combining many variables in an equation to predict a single outcome.
decision tree
decision tree - ANSWER-This is a whole series, a sequence of binary decisions,
based on your data, that can combine to predict an outcome. It's called a tree
because it branches out from one decision to the next
Neural networks - ANSWER-look at things in a different way than humans do and in
certain situations they're able to develop rules for classification, even when humans
can't see anything more than static.
Classifying - ANSWER-Locate the case in a k-dimensional space where k is the
number of variables or different kinds of information that you have. And there's
probably going to be more than three. It might be hundreds or thousands. But once
you get it located in that space, compare the labels on nearby data, that of course
assuming that other data already has labels that it says whether it's a photo of a cat,
or a dog, or a building. And then once you've done that, assign the new case to the
same category. LOCATE,COMPARE, ASSIGN
Classifying Methods - ANSWER-K-means, k nearest neighbors. BInary
Classificaiton. Many Categories, Distance Measures.
