Data Science Final Exam |155
Questions with 100% Verified Answers
Data Exploration - ✔ ✔ The part of the data science process where a scientist will
ask basic questions that helps her understand the context of a data set. What
you learn during the exploration phase will guide more in-depth analysis later.
Further, it helps you recognize when a result might be surprising and warrant
further investigation.
Data Mining - ✔ ✔ The process of pulling actionable insight out of a set of data
and putting it to good use. This includes everything from cleaning and organizing
the data; to analyzing it to find meaningful patterns and connections; to
communicating those connections in a way that helps decision-makers improve
their product or organization.
Data Pipelines - ✔ ✔ A collection of scripts or functions that pass data along in
a series. The output of the first method becomes the input of the second. This
continues until the data is appropriately cleaned and transformed for whatever
task a team is working on.
Data Wrangling - ✔ ✔ The process of taking data in its original form and "taming"
it until it works better in a broader workflow or project. Taming means making
values consistent with a larger data set, replacing or removing values that might
,affect analysis or performance later, etc. Wrangling and munging are
used interchangeably.
ETL (Extract, Transform, Load) - ✔ ✔ This process is key to data warehouses. It
describes the three stages of bringing data from numerous places in a raw form
to a screen, ready for analysis. ETL systems are generally gifted to us by data
engineers and run behind the scenes.
Web Scraping - ✔ ✔ Web scraping is the process of pulling data from a website's
source code. It generally involves writing a script that will identify the
information a user wants and pull it into a new file for later analysis.
Clustering - ✔ ✔ Clustering techniques attempt to collect and categorize sets
of points into groups that are "sufficiently similar," or "close" to one another.
"Close" varies depending on how you choose to measure distance. Complexity
increases as the more features are added to a problem space.
Decision Trees - ✔ ✔ This machine learning method uses a line of branching
questions or observations about a given data set to predict a target value.
They tend to over-fit models as data sets grow large.Random forests are a type
of decision tree algorithm designed to reduce over-fitting.
Deep Learning - ✔ ✔ Deep learning models use very large neural networks —
called deep nets — to solve complex problems, such as facial recognition. The
layers in a model start with identifying very simple patterns and then build in
complexity. By the end the net (hopefully) has a nuanced understanding that can
accurately classify or predict values.
, Feature Engineering - ✔ ✔ The process of taking knowledge we have as humans
and translating it into a quantitative value that a computer can understand. For
example, we can translate our visual understanding of the image of a mug into a
representation of pixel intensities.
Feature Selection - ✔ ✔ The process of identifying what traits of a data set are
going to be the most valuable when building a model. It's especially helpful
with large data sets, as using fewer features will decrease the amount of time
and complexity involved in training and testing a model. The process begins
with measuring how relevant each feature in a data set is for predicting your
target variable. You then choose a subset of features that will lead to a high-
performance model.
Neural Networks - ✔ ✔ A machine learning method that's very loosely based on
neural connections in the brain. Neural networks are a system of connected
nodes that are segmented into layers — input, output, and hidden layers. The
hidden layers (there can be many) are the heavy lifters used to make
predictions. Values from one layer are filtered by the connections to the next
layer, until the final set of outputs is given and a prediction is made.
Supervised Machine Learning - ✔ ✔ With supervised learning techniques, the
data scientist gives the computer a well-defined set of data. All of the columns
are labelled and the computer knows exactly what it's looking for. It's similar to a
professor handing you a syllabus and telling you what to expect on the final.
Unsupervised Machine Learning - ✔ ✔ In unsupervised learning techniques, the
computer builds its own understanding of a set of unlabeled data. Unsupervised
ML techniques look for patterns within data, and often deal with classifying
items based on shared traits.
Questions with 100% Verified Answers
Data Exploration - ✔ ✔ The part of the data science process where a scientist will
ask basic questions that helps her understand the context of a data set. What
you learn during the exploration phase will guide more in-depth analysis later.
Further, it helps you recognize when a result might be surprising and warrant
further investigation.
Data Mining - ✔ ✔ The process of pulling actionable insight out of a set of data
and putting it to good use. This includes everything from cleaning and organizing
the data; to analyzing it to find meaningful patterns and connections; to
communicating those connections in a way that helps decision-makers improve
their product or organization.
Data Pipelines - ✔ ✔ A collection of scripts or functions that pass data along in
a series. The output of the first method becomes the input of the second. This
continues until the data is appropriately cleaned and transformed for whatever
task a team is working on.
Data Wrangling - ✔ ✔ The process of taking data in its original form and "taming"
it until it works better in a broader workflow or project. Taming means making
values consistent with a larger data set, replacing or removing values that might
,affect analysis or performance later, etc. Wrangling and munging are
used interchangeably.
ETL (Extract, Transform, Load) - ✔ ✔ This process is key to data warehouses. It
describes the three stages of bringing data from numerous places in a raw form
to a screen, ready for analysis. ETL systems are generally gifted to us by data
engineers and run behind the scenes.
Web Scraping - ✔ ✔ Web scraping is the process of pulling data from a website's
source code. It generally involves writing a script that will identify the
information a user wants and pull it into a new file for later analysis.
Clustering - ✔ ✔ Clustering techniques attempt to collect and categorize sets
of points into groups that are "sufficiently similar," or "close" to one another.
"Close" varies depending on how you choose to measure distance. Complexity
increases as the more features are added to a problem space.
Decision Trees - ✔ ✔ This machine learning method uses a line of branching
questions or observations about a given data set to predict a target value.
They tend to over-fit models as data sets grow large.Random forests are a type
of decision tree algorithm designed to reduce over-fitting.
Deep Learning - ✔ ✔ Deep learning models use very large neural networks —
called deep nets — to solve complex problems, such as facial recognition. The
layers in a model start with identifying very simple patterns and then build in
complexity. By the end the net (hopefully) has a nuanced understanding that can
accurately classify or predict values.
, Feature Engineering - ✔ ✔ The process of taking knowledge we have as humans
and translating it into a quantitative value that a computer can understand. For
example, we can translate our visual understanding of the image of a mug into a
representation of pixel intensities.
Feature Selection - ✔ ✔ The process of identifying what traits of a data set are
going to be the most valuable when building a model. It's especially helpful
with large data sets, as using fewer features will decrease the amount of time
and complexity involved in training and testing a model. The process begins
with measuring how relevant each feature in a data set is for predicting your
target variable. You then choose a subset of features that will lead to a high-
performance model.
Neural Networks - ✔ ✔ A machine learning method that's very loosely based on
neural connections in the brain. Neural networks are a system of connected
nodes that are segmented into layers — input, output, and hidden layers. The
hidden layers (there can be many) are the heavy lifters used to make
predictions. Values from one layer are filtered by the connections to the next
layer, until the final set of outputs is given and a prediction is made.
Supervised Machine Learning - ✔ ✔ With supervised learning techniques, the
data scientist gives the computer a well-defined set of data. All of the columns
are labelled and the computer knows exactly what it's looking for. It's similar to a
professor handing you a syllabus and telling you what to expect on the final.
Unsupervised Machine Learning - ✔ ✔ In unsupervised learning techniques, the
computer builds its own understanding of a set of unlabeled data. Unsupervised
ML techniques look for patterns within data, and often deal with classifying
items based on shared traits.
