Coursera | Introduction to Big Data

Data Science 101

Data Science 101 >> Introduction to Big Data

1. Which of the following are parts of the 5 P’s of data science and what is the additional P introduced in the slides?

  • Perception
  • Product
  • Platforms
  • People
  • Process
  • Purpose
  • Programmability

2.Which of the following are part of the four main categories to acquire, access, and retrieve data?

  • Traditional Databases
  • Text Files
  • Web Services
  • Remote Data
  • NoSQL Storage

3. What are the steps required for data analysis?

  • Investigate, Build Model, Evaluate
  • Regression, Evaluate, Classification
  • Classification, Regression, Analysis
  • Select Technique, Build Model, Evaluate

4. Of the following, which is a technique mentioned in the videos for building a model?  

  • Investigation
  • Validation
  • Analysis  
  • Evaluation

5. What is the first step in finding a right problem to tackle in data science?

  • Assess the Situation
  • Define Goals
  • Ask the Right Questions
  • Define the Problem

6. What is the first step in determining a big data strategy?

  • Build In-House Expertise
  • Business Objectives
  • Organizational Buy-In
  • Collect Data

7. According to Ilkay, why is exploring data crucial to better modeling? 
Data exploration…

  • enables a description of data which allows visualization.
  • leads to data understanding which allows an informed analysis of the data.
  • enables understanding of general trends, correlations, and outliers.
  • enables histograms and others graphs as data visualization.

8. Why is data science mainly about teamwork? 

  • Exhibition of curiosity is required.
  • Data science requires a variety of expertise in different fields.
  • Engineering solutions are preferred.
  • Analytic solutions are required.

9. What are the ways to address data quality issues?

  • Remove data with missing values.
  • Remove outliers.
  • Generate best estimates for invalid values.
  • Merge duplicate records.
  • Data Wrangling

10. What is done to the data in the preparation stage?

  • Cleaning, Integrating, and Packaging
  • Build Models
  • Select Analytical Techniques
  • Retrieve Data
  • Identify Data Sets and Query Data

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