Data Analytics

The post is about Data Analytics MCQs. There are 20 multiple-choice questions for preparation for various subjects related to BS Data Analytics Degree Programs. Let us start with the Data Analytics MCQs with Answers.

Online Data Analytics MCQs with Answers

• Which emerging technology has made it possible for every enterprise to have access to limitless storage and high-performance computing?
• Which of the data roles is responsible for extracting, integrating, and organizing data into data repositories?
• When you analyze historical data to predict future outcomes what type of Data Analytics are you performing?
• A modern data ecosystem includes a network of continually evolving entities. It includes:
• Data Analysts work within the data ecosystem to:
• When we analyze data to understand why an event took place, which of the four types of data analytics are we performing?
• The first step in the data analysis process is to gain an in-depth understanding of the problem and the desired outcome. What are you seeking answers to at this stage of the data analysis process?
• From the provided list, select the three emerging technologies that are shaping today’s data ecosystem.
• From the provided list, select the three emerging technologies that are shaping today’s data ecosystem.
• Which of these skills is essential to the role of a Data Analyst?
• What, according to Sivaram Jaladi, goes a long way in lending credibility to your data analysis findings?
• Why is proficiency in Statistics an important skill for a Data Analyst?
• Which of these is one of the soft skills required to be a successful Data Analyst?
• Which of the data analyst functional skills helps research and interpret data, theorize, and make forecasts?
• In “A Day in the Life of a Data Analyst”, what according to Sivaram Jaladi forms a large part of a Data Analyst’s job?
• In “A Day in the Life of a Data Analyst”, what are some of the data points that were useful in analyzing the use case?
• What data type is typically found in databases and spreadsheets?
• Which of these data sources is an example of semi-structured data?
• Which one of the provided file formats is commonly used by APIs and Web Services to return data?
• What is one example of the relational databases discussed in the video?

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The post is about MCQs Data Analytics Quiz. There are 20 multiple-choice questions for preparation for various subjects related to BS Data Analytics Degree Programs. Let us start with the Data Analytics Quiz.

Please go to Data Analytics Quiz 1 to view the test

Online Data Analytics Quiz with Answers

• As a data analyst, you work with data about the life expectancy of sea turtles in the Coral Triangle. The dataset contains an estimated birthdate and death date for all tracked sea turtles. With the data you have, what questions are you able to answer?
• Which of the following processes helps ensure a close alignment of data and business objectives?
• Data engineers are responsible for making data —————.
• Insights or ————— team managers supervise an organization’s analytical strategy.
• What type of data professional is responsible for organizing information and making it accessible?
• In order to have a strong and thorough analysis, a data analyst must verify ————-.
• How would a data professional practice active listening?
• Which of the following are data engineer responsibilities?
• A data professional practices active listening when they allow others to —————- before offering a response.
• What does a data professional do when cleaning data?
• What is the primary goal of active listening?
• A data professional uses a scatterplot to plot residuals and predicted values from a regression model to check for homoscedasticity and finds that this assumption is met. What shape do the points in the scatterplot appear as?
• A data professional minimizes the sum of squared residuals to estimate parameters in a linear regression model. What method are they using?
• A data professional testing for linear regression assumptions plots their dependent variable against their independent variable and notices that the graph appears as an upward curve. Which model assumption does this invalidate?
• Which model might a data professional consider first if the outcome variable is binary?
• What type of variables would a data professional use to classify types of homes, such as apartments, single-family, or townhouses?
• A data professional is working on a project that involves labeling thousands of books by their various book genres. What type of variable should they use when working with this dataset?
• In a situation where a data analyst wants to test whether the population mean is lower than some hypothesized value, the data analyst would employ.
• Data Analytics uses ————- to get insights from data.
• Amongst which of the following is/are not a major data analysis approach?

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The post is about the Data Analytics Quizzes list. Each data Analytics Quiz is of multiple-choice type questions. To start with a quiz click on the links below.

Online Data Analytics Quizzes

	Quiz Data Analytics 5
Data Analytics MCQs Questions 4	Data Analytics Quiz 3
Data Analytics Quiz 2	Data Analytics Quiz 1

Data Analytics Definition

Data analytics is the process of collecting, analyzing, and interpreting data to find patterns, trends, and relationships between them. It is a multidisciplinary field that uses tools and techniques from mathematics, statistics, and computer science.

Data analytics can include

• Data analysis: Working with data to glean useful information
• Data science: High-level analysis
• Data engineering: Creating the frameworks needed to store data
• Data mining: Extracting usable data from a large dataset
• Data modeling: Diagramming data flows
• Data visualization: Presenting data in a clear picture using visuals like bar graphs, pie charts, and tables

Note that Data analytics can help:

• Improve decision-making
• Gain a deeper understanding of their processes and services
• Identify new opportunities
• Build related digital products
• Create personalized customer experiences
• Harness cost savings
• Optimize operations
• Increase employee productivity

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