Introduction to Data Analytics | Week 2

Course Link: https://in.coursera.org/learn/introduction-to-data-analytics/

The Data Ecosystem and Languages for Data Professionals

Practice Quiz

1. What data type is typically found in databases and spreadsheets?

Structured data

Unstructured data

Semi-structured data

Social media content

2. Which of these data sources is an example of semi-structured data?

Network and web logs

Social media feeds

Documents 

Emails

3. Which one of the provided file formats is commonly used by APIs and Web Services to return data?

XLS

XML

Delimited file 

JSON 

4. What is one example of the relational databases discussed in the video?

XML

Spreadsheet 

SQL Server 

Flat files

5. Which of the following languages is one of the most popular querying languages in use today?

Python

R

SQL

Java

Graded Quiz

1. In the data analyst’s ecosystem, languages are classified by type. What are shell and scripting languages most commonly used for? 

Automating repetitive operational tasks

Querying data 

Manipulating data 

Building apps 

2. Which of the following is an example of unstructured data? 

Zipped files 

Spreadsheets

XML

Video and audio files

3. Which one of these file formats is independent of software, hardware, and operating systems, and can be viewed the same way on any device? 

PDF

XLSX

Delimited text file

XML

4. Which data source can return data in plain text, XML, HTML, or JSON among others? 

Delimited text file 

API

PDF 

XML 

5. According to the video “Languages for Data Professionals,” which of the programming languages supports multiple programming paradigms, such as object-oriented, imperative, functional, and procedural, making it suitable for a wide variety of use cases? 

Unix/Linux Shell 

PowerShell 

Java

Python

Understanding Data Repositories and Big Data Platforms

Practice Quiz

1. Structured Query Language, or SQL, is the standard querying language for what type of data repository?

Data lake 

Flat Files

NoSQL

RDBMS

2. In use cases for RDBMS, what is one of the reasons that relational databases are so well suited for OLTP applications?

Minimize data redundancy 

Allow you to make changes in the database even while a query is being executed 

Support the ability to insert, update, or delete small amounts of data 

Offer easy backup and restore options 

3. Which NoSQL database type stores each record and its associated data within a single document and also works well with Analytics platforms?

Document-based 

Column-based 

Key-value store 

Graph-based

4. What type of data repository is used to isolate a subset of data for a particular business function, purpose, or community of users?

Data Lake

Data Pipeline

Data Warehouse

Data Mart

5. What does the attribute “Velocity” imply in the context of Big Data?

Diversity of data 

The speed at which data accumulates 

Scale of data 

Quality and origin of data 

6. Which of the Big Data processing tools provides distributed storage and processing of Big Data?

ETL

Spark

Hadoop

Hive

Graded Quiz

1. Data Marts and Data Warehouses have typically been relational, but the emergence of what technology has helped to let these be used for non-relational data?

ETL

NoSQL

SQL

Data Lake

2. What is one of the most significant advantages of an RDBMS? 

Enforces a limit on the length of data fields

Is ACID-Compliant 

Can store only structured data  

Requires source and destination tables to be identical for migrating data

3. Which one of the NoSQL database types uses a graphical model to represent and store data, and is particularly useful for visualizing, analyzing, and finding connections between different pieces of data?

Key value store 

Graph-based

Document-based

Column-based 

4. Which of the data repositories serves as a pool of raw data and stores large amounts of structured, semi-structured, and unstructured data in their native formats? 

Data Warehouses

Relational Databases

Data Lakes

Data Marts

5. What does the attribute “Veracity” imply in the context of Big Data?

The speed at which data accumulates

Scale of data

Diversity of the type and sources of data 

Accuracy and conformity of data to facts

6. Apache Spark is a general-purpose data processing engine designed to extract and process Big Data for a wide range of applications. What is one of its key use cases?

Scalable and reliable Big Data storage

Perform complex analytics in real-time 

Consolidate data across the organization

Fast recovery from hardware failures