Data Warehousing. Hortonworks was introduced by Cloudera and owned by Yahoo. This is done using the hashing trick to map features to indices in the feature vector.. True
D. decimal descriptors. When we get any dataset, not necessarily every column (feature) is going to have an impact on the output variable. How to use the ColumnTransformer. You can opt out of our newsletters at any time. 79. 2. Explanation: Predictive Analytics is major data analysis approaches not Predictive Intelligence. Thanks for reading! Can reduce time required to analyse data (i.e., after the data are
____________ phase sorts the data & ___________ creates logical clusters. B. Predictive Intelligence
The task of assigning a classification to a set of examples
This cheatsheet is a reminder that there are plenty of ways of converting features to some efficient useful information and it is better not to stick to one and only transformation with different models, but to look at the transformation from different sides. a. 1. And afterward, there is a huge range of different projections and coordinates variations you might be interested in. Boolean operators are words that are used to create logical
An advantage of using computer programs for qualitative data is, 103. 75. If the null hypothesis is false then which of the following is
1. There are __________ major Classification of Collaborative Filtering Mechanisms, 66. 91. 71. a. Time can also be separated into hours/minutes/seconds/etc and to which units to convert depends on the specifics of your dataset. CS614- Data Warehousing Solved MCQ(S) From Midterm Papers (1 TO 22 Lectures) BY Arslan Oct 26,2017 V-U For Updated Files Visit Our Site : Www.VirtualUstaad.blogspot.com Updated. 92. 64. b. The process of quantifying data is referred to as _________. 82. a. Which of the following statements about Naive Bayes is incorrect? This article was published as a part of the Data Science Blogathon. Feature Scaling with scikit-learn. It could be plain text, just some phrases, words, or any structured and unstructured number of characters. Which storage subsystem can support massive data volumes of increasing size. If there are N records in a table, then the selectivity of primary key column is b. Hypothesis
95. The process of quantifying data is referred to as _________. Here’s What You Need to Know to Become a Data Scientist! b. Data Analysis is defined by the statistician? a. Typology
Explanation: Text Data Mining is the process of deriving high-quality information from text. Data Mining
Decision trees use ____________, in that they always choose the option that seems the best available at that moment. We went through the most common feature types you might face in your day-to-day routine and suggested a good practice to manage transformation for such features. 5.63N, 3.23W). Basically, all 2. a. Can we use K Mean Clustering to identify the objects in video? ML Studio (classic): Feature Selection modules - Azure | ��� c. Gamma Distribution
Division By Zero During Forward Elimination St��� On the other hand, use of relevant data features can increase the accuracy of your ML model especially linear and logistic regression. 68. a. The discrete variables and continuous variables are two types of, 47. c. Enumeration
15. 94. c. The range
We also use third-party cookies that help us analyze and understand how you use this website. One is dividing values into k clusters in which each value belongs to the cluster with the nearest mean, and another just binning continuous data into k intervals. ______________ takes the grouped key-value paired data as input and runs a Reducer function on each one of them. Most relevant problems: I A symmetric (and large) I A spd (and large) I Astochasticmatrix,i.e.,allentries0 aij 1 are probabilities, and thus P j aij =1. In which of the following cases will K-Means clustering fail to give good results? 52. There is only one operation between Mapping and Reducing is it True or False…. 96. D. Can not say. Consider the features of a movie which are not relevant to a recommendation system. collected in a study. The Process of describing the data that is huge and complex to store and process is known as. B. Hans Peter Luhn
77. Though it creates two features in exchange for one, it still can be useful when taking into account the difference in time between the end of one day and the start of another day (with sin-cos features there will not be a gap as it is between 23 hour and 00 hour next day). The standard deviation
Read more in the User Guide.. Parameters score_func callable, default=f_classif. Geolocation can be presented in many different ways, but here let’s show the most common option with latitude and longitude, given that in the dataset it is presented in latitude N/S and longitude E/W form (e.g. Please leave a comment if you have any feedback. The FeatureHasher transformer operates on multiple columns. Explanation: In data analysis, two main statistical methodologies are used Descriptive statistics and Inferential statistics. d. Vertical graph, 110. What is the cyclical process of collecting and analysing data
_________ as a result of data accessibility, data latency, data availability, or limits on bandwidth in relation to the size of inputs. Now the question arise that what is automatic feature selection? c. Level of Significance
Full code for this article can be found here. Feature hashing projects a set of categorical or numerical features into a feature vector of specified dimension (typically substantially smaller than that of the original feature space). In such case units of the model, coefficients are going to be the same, therefore they all bring equal contribution into the analysis. developing branch of research methodology. Data Warehouse MCQ Questions and Answers. False, 108. 53. 49. Statistical Hypothesis
51. c. Inter-item analysis
84. d. None of the above, 105. So that would be a good transformation – to translate all country words to one language. 65. A statement made about a population for testing purpose is, 114. Overview. This two-part article explores the topic of data engineering and feature engineering for machine learning (ML). A. There is also a common option to replace missing values with 0 or with any constant value way more different from existing in a given feature. ___________ refers to the ability to turn your data useful for business. prior knowledge of the data, they are among the most popular algorithms for classifying text documents. Any intelligent system basically consists of an end-to-end pipeline starting from ingesting raw data, leveraging data processing techniques to wrangle, process and engineer meaningful features and attributes from this data. The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the ��� If you have some idea about your dataset and knowledge of where these missing values come from, then it would be easier for you to guess how to treat them. 10. What will be the number of clusters formed? 74. 72. If you noticed the scaling results are not satisfactory, check the distributions of the data again to come up with a better method for feature scaling. d. Mnemoning, 106. Similarly, if 60% of all transactions contain itemset {bread,butter}, then the support of. ________ for improving supply chain management to optimize stock management, replenishment, and forecasting; 23. which among the following is not a Data mining and analytical applications? Here we suppose we have a more or less cleaned dataset with some null values. Composite hypothesis
C. Web Mining. We will use Keras to define the model, and feature columns as a bridge to map from columns in a CSV to features used ��� 11. For these purposes, I am going to use a GlobalLandTemperatures_GlobalLandTemperaturesByMajorCity.csv dataset which represents global climate changes from 1750 to 2015 years. d. All of the above, 103. Introduction. These cookies will be stored in your browser only with your consent. __________ is a programming model for writing applications that can process Big Data in parallel on multiple nodes. Compiler Design MCQ with introduction, Phases, Passes, Bootstrapping, Optimization of DFA, Finite State machine, Formal Grammar, BNF ��� ________________require an oracle that decides which recommender should be used in a specific situation, depending on the user profile and/or the quality of recommendation. Which of the following algorithm is most sensitive to outliers? Hadoop YARN is used for Cluster Resource Management in Hadoop Ecosystem. Once you did a means or similar technique to reduce the number of unique values in a feature, you might want to proceed with further encoding e.g., create dummies: One hot encoding helps to make your classes be equal for the model by creating a binary feature per each (so that a model would not be able to assign more importance to class number 100 than to class number 1). Is this True or False. train_features = qconstant_filter.transform(train_features) test_features = qconstant_filter.transform(test_features) train_features.shape, test_features.shape If you execute the above script, you will see that both our training and test sets will now contain 265 columns, since the 50 constant, and 55 quasi-constant columns have been removed from a total of default 370 columns. For example, in the observed dataset, there is a Country column consists of country names. After performing K-Means Clustering analysis on a dataset, you observed the following. Predictive modeling machine learning projects, such as classification and regression, always involve some form of data preparation. 27. B. estimating numerical characteristics of the data
GFS consists of a _________ Master and ____________ Chunk Servers. Data generated from online transactions is one of the example for volume of big data. by Akshay Sadawarte Here are only two numerical features of type float64 and the rest are object types (some of them have to be converted to exact type). C. modeling relationships within the data
It is mandatory to procure user consent prior to running these cookies on your website. 29. Decision trees cannot handle categorical attributes with many distinct values, such as country codes for telephone numbers. It displays several cells that together form a mesh that includes rows and columns, each cell ��� Bar graph
81. D. ��� 8. could have happened by chance. One of the most common feature representations is a text. The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. ________________ are easy to implement and can execute efficiently even without. When your features are in different scales it is a good practice to transform them all into one scale. C. numerical descriptors
A high-confidence rule can sometimes be misleading because confidence does not consider support of the itemset in the rule consequent. ___________metric is examined to determine a reasonably optimal value of k. 60. Electrical Engineering MCQ questions and answers for an engineering student to practice, GATE exam, interview, competitive examination and entrance exam. A research question the results will answer. Explanation: The branch of statistics which deals with development of particular statistical methods is classified as applied statistics. 76. This category only includes cookies that ensures basic functionalities and security features of the website. 104. DA Objective Questions (MCQ /True or False / Fill up with Choices ) Here are all MCQs of DA(Data Analysis) of SPPU 4th year/final year with answers for online examination SPPU. a. Twitter b. Google c. Insta d. Youtube 2. Association rules are sometimes referred to as, 70. if 80% of all transactions contain itemset {bread}, then the support of {bread} is 0.8. 58. Côte D’Ivoire country name), and, for example, you might want to have all your records in one language. Clustering techniques are ______________ in the sense that the data scientist does not determine, in advance, the labels to apply to the clusters. Here are two very short texts to compare and find the cosine similarity measure? b. ________________ recommend items based on similarity measures between users and/or items. A challenge of qualitative data analysis is that it often includes, 107. Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. First, you might want to convert these coordinates to standard form (meaning that they become numerical, which is good). _________ is factors considered before Adopting Big Data Technology. This technique uses mean and standard deviation scores to transform real-valued attributes. ___________ is able to identify trustworthy rules, but it cannot tell whether a rule is coincidental. The performance of ML model will be affected negatively if the data features provided to it are irrelevant. 1. What could be done about it? Positive Hypothesis
be true is called? 105. c. A theory that underpins the study. they failed to ��� He has determined that if he checks in for his flight at least two hours early, the probability that he will get an upgrade is 0.75; otherwise, the probability that he will get an upgrade is 0.35. statistics. In recommendation approaches, items are retrieved using similarity measures that describe to which extent item properties match some given user’s requirements. There are a vast number of different types of data preparation techniques that could be used on a predictive modeling project. sense of the large pool of data. Velocity is the speed at which the data is processed. a. FALSE
that they _______. 42. The maximum value for entropy depends on the number of classes so if we have 8 Classes what will be the max entropy. Another useful transformation is to obtain an address from your coordinates, which might include country name, city, street, house, name of the place, postcode, and other things. Which of the following is true about hypothesis testing? What is a feature and why we need the engineering of it? This tutorial demonstrates how to classify structured data (e.g. We can see that this data is easily linearly separable, so Logistic Regression would give us quite a ��� 30. TRUE
The branch of statistics which deals with development of particular statistical methods is classified as, A. industry statistics
48. d. A statistical method for calculating the extent to which the results
5. We can see that only these two numerical features AverageTemperature and AverageTemperatureUncertainty have 11002 null values each. 99. A statement that the researcher wants to test through the data
a. descriptive words, or category names is known as _______. Answer: (d) Spreadsheet Explanation: Spread Sheet is the most appropriate for performing numerical and statistical calculation. Should I become a data scientist (or a business analyst)? A graph that uses vertical bars to represent data is called a ___, 110. Alternative Hypothesis is also called as? D. All of the above. Feature engineering is a topic every machine ��� 116. True
80. c. Scatterplot
______________ is a type of local Reducer that groups similar data from the map phase into identifiable sets. c. Simple Hypothesis
The most efficient way to transform data into something meaningful might be to divide it into the year, season, month, week, and day. Text Analytics, also referred to as Text Mining? 24. Eigenvalues and eigenvectors How hard are they to 詮�nd? 13. 40. sklearn.feature_selection.SelectKBest¶ class sklearn.feature_selection.SelectKBest (score_func=, *, k=10) [source] ¶. 83. a. A subdivision of a set of examples into a number of classes
FeatureHasher. A. ------------------- is good at handle missing data and support both the kind of attributes ( i.e Categorial and Continuous attributes ). If the assumed hypothesis is tested for rejection considering it to
__________ have a structure but cannot be stored in a database. +91 9404 340 614 The process of marking segments of data with symbols, 100. c. Line graphs
In the example dataset, AverageTemperature is a continuous feature because its value can assume any value from the set of real numbers. As a starting point, here is a quick look at the overall dataset statistics: The dataset has no duplicated rows. b. Explanation: The goal of business intelligence is to allow easy interpretation of large volumes of data to identify new opportunities. B. floating descriptors
Data Mining. The denominator (bottom) of the z-score formula is
Which of these distributions is used for a testing hypothesis? The easy way is to convert your floats to ints, or round it by some decimal. Line graph
Here you should be aware that in each country/hemisphere these weekends/holidays/seasons may vary and it might not be a good idea to apply one rule to all the countries. It cannot be recovered later. Explanation: Data Analysis is a process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Hadoop MapReduce allows you to perform distributed parallel processing on large volumes of data quickly and efficiently… is this MapReduce or Hadoop… i.e statement is True or False. It is used to transform skewed data to make it looks more like a normal distribution. You also have the option to opt-out of these cookies. The denominator (bottom) of the z-score formula is. tabular data in a CSV). See our privacy policy. Consider the following example “How we can divide set of articles such that those articles have the same theme (we do not know the theme of the articles ahead of time) " is this: 56. Hypothesis testing and estimation are both types of descriptive
d. Constant analysis, 101. Datetime is a common type of feature that can be transformed in different ways, so let’s first look at its ‘date’ part. Research Hypothesis
Which of these distributions is used for a testing hypothesis? a. Statistic
data that are unwieldy and complex; it is a major challenge to make
accepted? d. Alternative Hypothesis. Here are few ideas to transform your missing values into something more informative: One way to go is to impute these missing values with median, mean, or mode (most frequent value). The specific data preparation required for a dataset depends on the specifics of the data, such as the variable types, as well as the algorithms that will be used to model them that may ��� This website uses cookies to improve your experience while you navigate through the website. __________ are the basic building blocks of qualitative data. If you have a large dataset and a little number of rows with nulls, it might be fine to remove these rows at all. Then we usually C. describing associations within the data
B. Categories
For a matrix A 2 Cn���n (potentially real), we want to 詮�nd 2 C and x 6=0 such that Ax = x. 9. Can decision trees be used for performing clustering? b. What is the cyclical process of collecting and analysing data. With his busy schedule, he checks in at least two hours before his flight only 40% of the time. 7. Which of the following is not an example of Social Media? But opting out of some of these cookies may affect your browsing experience. Google Introduced MapReduce Programming model in 2004. relationship between two quantitative variables. b. The process of marking segments of data with symbols,
d. Scatterplots, 111. B. Numerical Input, 90. John flies frequently and likes to upgrade his seat to first class. Midterm Papers Solved MCQ(S) (1 TO 22 Lectures) 1. 59. ___________ are used when you want to visually examine the, 111. Is This True? An advantage of using computer programs for qualitative data is
Boolean operators are words that are used to create logical. 98. D. Text Analytics. b. __________ are the basic building blocks of qualitative data. C. Can be true or false
Large-scale e-commerce sites, often implement a different technique. What is the minimum no. True
101. d. The mean, 112. a. Null Hypothesis
Bar graphs
16. Which of the following is true about regression analysis? Value tells the trustworthiness of data in terms of quality and accuracy. a. decimal scaling b. min-max normalization c. z-score normalization d. logarithmic normalization 9. Are You sure you want to delete blog - Data Analysis SPPU all MCQ's for online exam. 102. Suppose John did not receive an upgrade on his most recent attempt. MCQ Questions and Solutions for all Competitive Exams - ��� 28. You might consider one of those is the most suitable in your case, but in the most general way, you would only use the web Mercator form. b. Inter analysis
88. 116. combinations. False, 99. 3. of variables/ features required to perform clustering? A. answering yes/no questions about the data, 98. A set of data organised in a participants(rows)-byvariables(columns) format is known as a “data set.”
62. Which of the following is not a major data analysis approaches? Another way of numerical feature transformation is scaling. With respect to the determination of the set of similar users, one common measure used in. a. Segmenting
43. b. Diagramming
In this review, we will save for later a discussion about feature selection and various angles of feature engineering, as here we have a straight focus on possible ways of encoding such data. 1. Artificial Intelligence Vs Machine Learning Vs Deep Learning: What exactly is the difference ? Feature Selection Methods 2. True
c. Make many procedures available that are rarely done by hand
b. Pie graphs
Another way of numerical feature transformation is scaling. Movie Recommendation to peoples is an example of. a. Or just simply binarize your feature or make two labels only, although it reduces a lot of information, in some cases, it might be helpful. Parallelized hybrid recommender systems operate dependently of one another and produce separate recommendation lists. These cookies do not store any personal information. 61. if {bread,eggs,milk} has a support of 0.15 and {bread,eggs} also has a support of 0.15, the confidence of rule {bread,eggs}→{milk} is. Qualitative data analysis is still a relatively new and rapidly
36. But it can be argued that it lacks self-adaptation. A set of data organised in a participants(rows)-byvariables(columns) format is known as a “data set.”, 109. High entropy means that the partitions in classification are. C. Gregory Piatetsky-Shapiro
a. 55. ______________ is an open source framework for storing data and running application on clusters of commodity hardware. Another way to make classes out of feature values is to apply quantization methods like K-means. ____________ recommend items based on similarity measures between users and/or items. Interim analysis
This tutorial is divided into 4 parts; they are: 1. 26. a. a. 67. d. Coding, 102. 3. 93. Sometimes you might need to create a zone of locations and therefore you transform your coordinates to polygons and points of polygons. 63. A. A. William S.
If you���re looking to use machine learning to solve a business problem requiring you to predict a numerical ��� Sentiment Analysis is an example of. A. integer descriptors
b. How To Have a Career in Data Science (Business Analytics)? This dataset consists of very frequent feature types such as date, location, numerical and categorical columns. b. Electrical Engineering MCQ questions and answers especially for the Electrical Engineer and who preparing for GATE Exam. What is the probability that he did not arrive two hours early? True
Wavelet transform is being widely used as a method to denoise ecg signal. c. Individuals
___________ are used when you want to visually examine the
It is a very common case where data has blank fields, random characters, and even wrong information. _________________are based on a sequenced order of techniques, in which each succeeding recommender only refines the recommendations of its predecessor. ________ provides performance through distribution of data and fault tolerance through replication. 22. 39. It’s not always appropriate to fill nulls with 0 as there could be some zero values already (like in this example with temperature) and that would just mean a piece of wrong information. Qualitative data analysis is still a relatively new and rapidly. 113. To choose between these three just take a look at a boxplot and distribution. 38. A graph that uses vertical bars to represent data is called a ___
during a single research study called? by Akshay Sadawarte, 85. A feature in case of a dataset simply means a column. 18. Is this true or False. 3/25 And general statistics for these temperature values: First of all, let’s deal with missing values. Pure collaborative approaches take a matrix of given user–item ratings as the only input and typically produce output. The difference between a score and the mean
Wanna showcase your skills by contributing to our society and further your career at the same time? Solved: 2. For two runs of K-Mean clustering is it expected to get same clustering results? If an itemset is considered frequent, then any subset of the frequent itemset must also be frequent. Now, as we have compressed the data, we can easily apply any machine learning algorithm to it. As is known, a proper feature transformation can bring a significant improvement to your model. b. By 2025, the volume of digital data will increase to. In our example let’s go for the median option. Movie Recommendation systems are an example of, 35. Otherwise, there is also an option to predict missing values, and at this point, you should ask yourself if it is worth trying so (in the matter of time and efficiency). Select features according to the k highest scores. Scaling could also fasten the convergence and show a better performance. Function taking two ��� How many main statistical methodologies are used in data analysis? The goal of business intelligence is to allow easy interpretation of large volumes of data to identify new opportunities. Feature engineering techniques are a must know concept for machine learning professionals; Here are 7 feature engineering techniques you can start using right away . It may ��� I have seen candidates failing the interviews because they have good knowledge about models, but did not pay much importance in the Exploratory Data Analysis part. A. answering yes/no questions about the data
112. HDFS Stores how much data in each clusters that can be scaled at any time? 78. Lift is defined as the measure of certainty or trustworthiness associated with each discovered rule. A challenge of qualitative data analysis is that it often includes
____________is based on the availability of item descriptions and a profile that assigns importance to these characteristics. a. While Installing Hadoop how many xml files are edited and list them? Case-based recommenders focus on the retrieval of similar items on the basis of different types of similarity measures. A data normalization technique for real-valued attributes that divides each numerical value by the same power of ��� None of these, A. inspecting data
By using Analytics Vidhya, you agree to our, Certified Computer Vision Master’s Program, GlobalLandTemperatures_GlobalLandTemperaturesByMajorCity.csv, 40 Questions to test a Data Scientist on Clustering Techniques (Skill test Solution), How to Download Kaggle Datasets using Jupyter Notebook, Python List Programs For Absolute Beginners, Commonly used Machine Learning Algorithms (with Python and R Codes), Understanding Delimiters in Pandas read_csv() Function, 45 Questions to test a data scientist on basics of Deep Learning (along with solution), Introductory guide on Linear Programming for (aspiring) data scientists, 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R, 16 Key Questions You Should Answer Before Transitioning into Data Science. D. applied statistics. False, 104. This is the process of transforming qualitative research data from, 106. c. Colouring
A. answering yes/no questions about the data
Another point of view is that with proper imputation/deletion you again can gain a better result. 21. For Drawing insights for Business what are needed? b. Chi-Squared Distribution
Which of the following is not an example of Social Media? Image Processing Using Numpy: With Practical Implementation And Code. Which of the following can act as possible termination conditions in K-Means? b. If your data is skewed or contains outliers, try to look into the mode or median imputation options. This first part discusses best practices of preprocessing data in a machine learning pipeline on Google Cloud. In this post we explore 3 methods of feature scaling that are implemented in scikit-learn: StandardScaler; MinMaxScaler; RobustScaler; Normalizer; Standard Scaler. By 2025, the volume of digital data will ��� 50. A _____________ has been implemented, for similarity based retrieval under nearest neighbors.