Data Science 101 - Big Data

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?

    • Platforms

    • Product

    • Process

    • People

    • Perception

    • Programmability

    • Purpose

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

    • Text Files

    • Traditional Databases

    • Remote Data

    • NoSQL Storage

    • Web Services

  3. What are the steps required for data analysis?

    • Investigate, Build Model, Evaluate

    • Classification, Regression, Analysis

    • Select Technique, Build Model, Evaluate

    • Regression, Evaluate, Classification

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

    • Validation

    • Investigation

    • Analysis

    • Evaluation

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

    • Define the Problem

    • Define Goals

    • Assess the Situation

    • Ask the Right Questions

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

    • Business Objectives

    • Build In-House Expertise

    • Collect Data

    • Organizational Buy-In

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

    Data exploration... <complete the sentence>

    • enables understanding of general trends, correlations, and outliers.

    • enables histograms and others graphs as data visualization.

    • leads to data understanding which allows an informed analysis of the data.

    • enables a description of data which allows visualization.

  8. Why is data science mainly about teamwork?

    • Analytic solutions are required.

    • Data science requires a variety of expertise in different fields.

    • Exhibition of curiosity is required.

    • Engineering solutions are preferred.

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

    • Remove data with missing values.

    • Generate best estimates for invalid values.

    • Merge duplicate records.

    • Remove outliers.

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

    • Retrieve Data

    • Select Analytical Techniques

    • Identify Data Sets and Query Data

    • Understand Nature of Data and Preliminary Analysis.

    • Build Models