Question

1) What are the Analyze (Exploratory) goals? A. Present data and ideas B. Provide evidence and support C. Explain and inform D. Explore the data 2) The difference between "Raster" and "Vector" is: A. Raster is composed by pixels and Vector is composed by paths B. Vector represents images and Raster represents points, lines and polygons C. Raster represents data as cell or grid while Vector represents data using sequential points or vertices. 3) Which of the following is one of the key data science skill? A. Statistics B. Machine learning C. Data visualization D. All of the above 4) Which of the following is definition of Raw Data? A. Set of Measurement on Recorded Values B. Processed Data C. Easy to use for data analysis D. None of the Mentioned 5) Why Machine Learning in Data Science? A. For Visualization B. For Prediction C. For Cleaning D. All the above 6) Which of the following are "Measures of Central Tendency"? A. Mean, Range, Mode B. Mean, Standard Deviation, Range C. Mode, Mean, Median D. Range, Standard Deviation, Variance

Answer 1

Ans 1: D) Explore the data

The goals are to assess the data and gain new insights by exploring the data

Ans 2: C) Raster represents data as cell or grid while vector represents data using sequential points of vertices.

Vector data use X Y coordinates to define the data. Raster data, on the other hand, uses a matrix to define the location of the features.

Ans 3: D) All of the above.

Programming Skills,Statistics,,Machine Learning, Linear Algebra,Data Wrangling, Data Visualization & Communication, etc all are necessary for for data science.

Ans 4: A) Set of measurements on recorded values.

Raw value is the unprocessed data which can be also called as measurements of given values.

Ans 5: D) All of the above

Machine learning can be used to visualize, predict and clean data and information.

Ans 6: C) Mode,Mean,Median.

Mean, Mode and Median are the most common measures of central tendency .

Ans 7: B) Original version of data

Raw data means it is the data before any type of processing. i.e it is the original version of data without any changes or alterations.

Ans 8: B) Processed

In order to put the data in the equation for the training of the model the raw data has to be pre processed.

Ans 9: B) It takes numbers and makes them into a recognizable pattern

Data visualization takes up the numbers or data from the dataset and finds out a pattern visualisation which makes the data easily understandalble.

Ans 10: D) It transforms data to most basic form

Data visualisation converts the data to visual form for easily understandable. It reduces the complexity of the shuffled data.

Ans 11: B) Decision Tree

Decision Tree is a practical aglorithm in Machine Learning which can be used in prediction models.

Ans 12: A) Categorical data type

Color is used in Categorial data type as it has unique features in it.

Ans 13: B) Quantitative data type

Quantittaive data type uses Shape as it consist of numeric data.

Ans 14: A) Parallel visual processing

Parallel visual processing is considered to be the fastest processing in visuals.