# Data Science 101 - 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?**
    
    * <mark>Platforms</mark>
        
    * <mark>Product</mark>
        
    * <mark>Process</mark>
        
    * <mark>People</mark>
        
    * Perception
        
    * Programmability
        
    * <mark>Purpose</mark>
        
2. **Which of the following are part of the four main categories to acquire, access, and retrieve data?**
    
    * <mark>Text Files</mark>
        
    * <mark>Traditional Databases</mark>
        
    * <mark>Remote Data</mark>
        
    * <mark>NoSQL Storage</mark>
        
    * Web Services
        
3. **What are the steps required for data analysis?**
    
    * Investigate, Build Model, Evaluate
        
    * Classification, Regression, Analysis
        
    * <mark>Select Technique, Build Model, Evaluate</mark>
        
    * Regression, Evaluate, Classification
        
4. **Of the following, which is a technique mentioned in the videos for building a model?**
    
    * Validation
        
    * Investigation
        
    * <mark>Analysis</mark>
        
    * Evaluation
        
5. **What is the first step in finding a right problem to tackle in data science?**
    
    * <mark>Define the Problem</mark>
        
    * Define Goals
        
    * Assess the Situation
        
    * Ask the Right Questions
        
6. **What is the first step in determining a big data strategy?**
    
    * <mark>Business Objectives</mark>
        
    * Build In-House Expertise
        
    * Collect Data
        
    * Organizational Buy-In
        
7. **According to Ilkay, why is exploring data crucial to better modeling?**
    
    *Data exploration... &lt;complete the sentence&gt;*
    
    * enables understanding of general trends, correlations, and outliers.
        
    * enables histograms and others graphs as data visualization.
        
    * <mark>leads to data understanding which allows an informed analysis of the data.</mark>
        
    * enables a description of data which allows visualization.
        
8. **Why is data science mainly about teamwork?**
    
    * Analytic solutions are required.
        
    * <mark>Data science requires a variety of expertise in different fields.</mark>
        
    * Exhibition of curiosity is required.
        
    * Engineering solutions are preferred.
        
9. **What are the ways to address data quality issues?**
    
    * <mark>Remove data with missing values.</mark>
        
    * <mark>Generate best estimates for invalid values.</mark>
        
    * <mark>Merge duplicate records.</mark>
        
    * <mark>Remove outliers.</mark>
        
10. **What is done to the data in the preparation stage?**
    
    * Retrieve Data
        
    * Select Analytical Techniques
        
    * Identify Data Sets and Query Data
        
    * <mark>Understand Nature of Data and Preliminary Analysis.</mark>
        
    * Build Models
        

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