Code Free Data Science Coursera Quiz Answers

All Weeks Code Free Data Science Coursera Quiz Answers

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements.

Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions.

During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any prerequisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models.

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Code Free Data Science Week 01 Quiz Answers

Big Data Quiz Answers

Q1. Over what X% of data was created in last 2 years?

  • 90%

Q2. Data generated is growing at exponential rate

  • True
  • False

Q3. The number of smart connected devices in the world has reached over 50 ?

  • hundreds
  • millions
  • billions
  • thousands

Module 1 quiz Answers

Q1. How many Terabytes are in a Petabyte?

  • 10
  • 1000
  • 100

Q2. Big Data is fueling Data Science

  • TRUE
  • FALSE

Q3. What types of data are consumed in order to bring the value from Big Data?

  • Stream
  • Social
  • Financial
  • Behavioral

Q4. Which one of the V’s below does NOT describe one of the 4 major characteristics of Big Data?

  • Volume
  • Velocity
  • Variety
  • Viscosity
  • Veracity

Q5. Descriptive Analytics enables faster decision automation than Prescriptive Analytics

  • True
  • False

Code Free Data Science Week 02 Quiz Answers

Install KNIME Quiz Answers

Q1. I have successfully downloaded and installed KNIME Analytics Platform

  • TRUE
  • FALSE

Q2. In order to download KNIME you should consult the following website

  • https://www.knime.com/downloads
  • https://www.knime.com/knime-for-developers
  • https://www.knime.com/knime-for-decision-makers

Q3. KNIME Nodes indicate their status by utilizing

  • red, yellow and green light
  • red, white and blue light
  • yellow, purple and green light

Exploring KNIME Answers

Q1. KNIME Analytics Platform requires programming

  • TRUE
  • FALSE

Q2. Workflow editor in KNIME enables us to include documentation with the analytics

  • TRUE
  • FALSE

Q3. If we are not sure what the certain node does we can look it up in the

  • Node Repository Panel
  • Node Description Panel
  • Console Panel
  • Workflow Coach Panel

Node Operations Quiz Answers

Q1. In order to bring a node into to workflow editor from the node repository you can – click all that apply

  • double-click the node
  • drag the node
  • right click on the node
  • hover over the node

Q2. The node needs to be _________ before executed

  • Painted
  • Configured
  • Aligned
  • Cleaned

Q3. When you use a file reader node you can see the file after you

  • configure the node
  • execute the node

Filtering Data Quiz Answer

Q1. KNIME Supports all of the following data types except

  • String
  • Double
  • Integer
  • Compound
  • Date or Time

Q2. KNIME indicates the missing value in the data set with the following character

  • !
  • ?
  • %
  • #

Q3. Column filter in KNIME can be used to

  • Add columns to the existing data set
  • Exclude columns from the data set

Q4. Column filter can select columns based on

  • Type
  • Name
  • Name or Type

Q5. Row Filter in KNIME can include of exclude rows based on

  • Attribute value test
  • Row number
  • Row length
  • Row ID

Filtering Workflow Assignment Solution

Q. Create a KNIME workflow where you read the provided autos data set. Exlcude the columns ‘normalized’ and ‘bore’. Filter the rows on the price column and keep only instances describing cars that are less than $10,000. Write the file out to csv and report back the number of rows.

Code Free Data Science Week 03 Quiz Answers

Rule Engine Quiz Ansswers

Q1. Rule engine node in KNIME takes a list of user-defined rules and tries to match them to each row in the input table

  • True
  • False

Q2. Each rule in Rule Engine Node is represented by a

  • line
  • :
  • column

Q3. Rules in the Rule Engine Node consist of a condition 2 main pars

  • antecedent and consequent
  • question and answer
  • start and stop

Q4. Rules in the Rule Engine Node the outcome of the rule is indicated by

  • <>
  • =>
  • ?
  • <-

Q5. If no rule matches, the outcome is a missing value unless a default value is specified.

  • True
  • False

Module 3 Assignment Answers

Q1. Read in the Baloon Data Set from the UCI Data Repository at https://archive.ics.uci.edu/ml/datasets/balloons.

https://archive.ics.uci.edu/ml/datasets/balloons

Download : Yellow-small.data

This file has 5 columns: Color, Size, Act, Age and Inflated (True/False)

1. Rename the columns accordingly. 

2. Add the following classification column and name it Class

IF Color=yellow AND Size=small => Class=inflated

ELSE                                                   Class= not inflated

3. Add a final column called “Full sentence” that provide the info as

“inflated is T”

OR

“not Inflated is F”

Where “inflated/not inflated” comes from the “Class” column and “T/F” from the “Inflated (True/False) column.

Question: How many rows are there with the Class = inflated

Hint: you can use Rule engine and String Manipulation Nodes to accomplish this assignment

  • 7
  • 8
  • 9
  • 10

Q2. Read the adult data set. Download adult.data from https://archive.ics.uci.edu/ml/machine-learning-databases/adult/

What is the highest number of hours of work per week for people who have Masters degree and are 20-40 years?

Q3. Read the Iris.csv data set. Utilize the color manager node to a assign color attribute to different values of a categorical class attribute (Iris Setosa, Virginica, Versicolor). Use this information then in the Scatter plot (JavaScript) node to color data points according to group membership. One of the Iris Setosa’s member seem to be an outlier and closer to the other two classes. can you spot it in the Scatter plot by visualizing sepal With vs. Sepal Length. What is the sepal with value for that instance?

Code Free Data Science Week 04 Quiz Answers

Decision Trees Quiz Answers

Q1. Decision Trees are (mark all that apply)

  • Robust to missing and noisy data
  • Can learn non-linear relationships from data
  • Have inductive Bias towards shortest trees
  • Are an Unsupervised types of machine learning

Q2. Decision Trees can only have one Root Node

  • True
  • False

Q3. In decision trees each leaf node assigns

  • test on an attribute
  • a classification
  • corresponding attribute value

Q4. Decision Trees use Information gain to calculate which attribute to split on. Information gain is measured in

  • kg
  • meters
  • bits
  • hertz

Q5. What does Decision tree uses to prevent overfitting?

  • divide and conquer
  • pruning
  • self selection
  • sorting

Decision Tree Assignment Answers

Q2. Read Iris data set from the UCI repository. Train a Decision Tree on 75% of the data and use the remaining 25% for testing. Set the parameters of the Learner Decision Tree node to utilize: gain ratio with no pruning and minimum of 4 attributes per leaf node. What is the overall model accuracy ? (hint: answer should be in decimal point format)

  • 0.7-0.8
  • 0.8-0.9
  • 0.9-1
  • 0.6-0.7

Q2. Using the wine.data data set from the UCI ML Repository. Split the training and testing into 80%/20%.  Train a Decision Tree to recognize the class to which each wine belongs.   Evaluate the Decision Tree on the wine test set and measure the Decision  Tree performance.  In particular, report back how many false negatives for class 2 there are?

  • More than 10
  • More than 100
  • Less than 3
  • More than 3 but less than 10

Clustering Assignment Answers

Q1. Use the wine.data data set.  Partition the data into 80/20% training and test data sets.  Utilize the K-means clustering algorithm to train the model to produce 3 clusters. Use color, shape and scatter plot nodes to visualize results.       Visualizing the cluster assignments how many instances have ended up in the cluster_0.  Hint: use the number to string on the class attribute.

  • 8
  • 9
  • 10

Q2. Use the clustering algorithm on the Iris data set and partition the training test data set into 90/10%.  By utilizing the cluster assigner node – are there any Iris-versicolor that were clustered across numerous clusters? How about Iris-Virginica? Which classes were “spread” across multiple cluster assignments? If you change the split to 85 or 90% for training – how does it influence the “assignments” of classes to clusters? What happens if you increase the number of clusters? Mark all that apply.

  • Iris-versicolor was” spread” across several clusters
  • Iris-virginica demonstrated more “spread” across clusters as % training/testing was changed
  • Increased number of clusters produced less diversity on class to cluster assignments
Code Free Data Science Course Review:

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