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Stuck on the Coursera Introduction to Data Analytics quizzes? Get unstuck! This blog post offers solutions to help you conquer the quizzes and master the fundamentals of data analysis. Learn about key steps in the process, data roles, structures, formats, and how to collect, clean, analyze, and visualize data.

This course will equip you with the following skills:

• Grasp the fundamentals of data analytics, including the key steps involved in the process.
• Understand the distinctions between various data professions, including data engineer, data analyst, data scientist, business analyst, and business intelligence analyst.
• Explore different data structures, file formats, and sources.
• Demystify the data analysis process, encompassing data collection, wrangling, mining, and visualization.

There are five modules in the course “Introduction to Data Analytics”

1. What is Data Analytics
2. The Data Ecosystem
3. Gathering and Wrangling Data
4. Mining & Visualizing Data and Communicating Results
5. Career Opportunities and Data Analysis in Action

Week 1 – Introduction to Data Analytics

Week 1 Quiz 1 – Introduction to Data Analytics

1. A modern data ecosystem includes a network of continually evolving entities. It includes: 

• Data sources, enterprise data repository, business stakeholders, and tools, applications, and infrastructure to manage data
• Data sources, databases, and programming languages
• Data providers, databases, and programming languages
• Social media sources, data repositories, and APIs

2. Data Analysts work within the data ecosystem to:

• Develop and maintain data architectures
• Provide business intelligence solutions by monitoring data on different business functions
• Gather, clean, mine, and analyze data for deriving insights
• Build Machine Learning or Deep Learning models

3. When we analyze data in order to understand why an event took place, which of the four types of data analytics are we performing?

• Prescriptive Analysis
• Descriptive Analysis
• Predictive Analysis
• Diagnostic Analysis

4. 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?

• Where you are and where you need to be
• What will be measured and how it will be measured
• The data you need 
• The best tools for sourcing data

5. From the provided list, select the three emerging technologies that are shaping today’s data ecosystem.

• Cloud Computing, Internet of Things, and Dashboarding
• Cloud Computing, Machine Learning, and Big Data
• Big Data, Internet of Things, and Dashboarding
• Machine Language, Cloud Computing, and Internet of Things

Week 1 Quiz 2 – Introduction to Data Analytics

1. Why is proficiency in Statistics an important skill for a Data Analyst?

• For creating queries to extract required data 
• For identifying patterns and correlations in data 
• For creating project documentation 
• For acquiring data from multiple sources

2. Which of these is one of the soft skills required to be a successful Data Analyst?

• Work collaboratively with cross-functional teams 
• Prepare reports and dashboards
• Filter, clean, and standardize data 
• Integrate data coming from multiple sources 

3. Which of the data analyst functional skills helps research and interpret data, theorize, and make forecasts?

• Analytical skills 
• Problem-solving skills 
• Proficiency in Statistics
• Probing skills 

4. 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?

• Cleaning and preparing data 
• Interacting with stakeholders 
• Generating hypotheses
• Creating a report

5. 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. (Select all that apply)

• Employment history of the complainants
• Serial number of the meters
• Average billing amount of complainants
• Age and education details of complainants

Week 2 – Introduction to Data Analytics

Week 2 Quiz 1 – Introduction to Data Analytics

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
• Building apps 
• Manipulating data 
• Querying data 

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

• Video and audio files
• Spreadsheets
• Zipped files 
• XML

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? 

• Delimited text file
• XLSX
• PDF
• XML

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

• API
• Delimited text file 
• 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? 

• Python
• Java
• Unix/Linux Shell 
• PowerShell 

Week 2 Quiz 2 – Introduction to Data Analytics

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?

• Data Lake
• NoSQL
• SQL
• ETL

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

• Can store only structured data  
• Requires source and destination tables to be identical for migrating data
• Enforces a limit on the length of data fields
• Is ACID-Compliant 

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?

• Graph-based
• Key value store 
• Column-based 
• Document-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 Lakes
• Relational Databases
• Data Marts
• Data Warehouses

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

• The speed at which data accumulates
• Diversity of the type and sources of data 
• Scale 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?

• Consolidate data across the organization
• Perform complex analytics in real-time 
• Fast recovery from hardware failures 
• Scalable and reliable Big Data storage

Week 3 – Introduction to Data Analytics

Week 3 Quiz 1 – Introduction to Data Analytics

1. What are some of the steps in the process of “Identifying Data”? (Select all that apply)

• Determine the visualization tools that you will use
• Determine the information you want to collect 
• Define a plan for collecting data 
• Define the checkpoints 

2. What type of data refers to information obtained directly from the source?

• Primary data 
• Secondary data 
• Third-party data 
• Sensor data 

3. Web scraping is used to extract what type of data? 

• Text, videos, and data from relational databases
• Text, videos, and images
• Data from news sites and NoSQL databases
• Images, videos, and data from NoSQL databases

4. Data obtained from an organization’s internal CRM, HR, and workflow applications is classified as:

• Secondary data
• Third-party data
• Primary data
• Copyright-free data

5. Which of the provided options offers simple commands to specify what is to be retrieved from a relational database?

• Web Scraping
• RSS Feed
• API
• SQL

Week 3 Quiz 2 – Introduction to Data Analytics

1. What does a typical data wrangling workflow include?

• Using mathematical techniques to identify correlations in data
• Predicting probabilities
• Recognizing patterns
• Validating the quality of the transformed data 

2. OpenRefine is an open-source tool that allows you to: 

• Automatically detect schemas, data types, and anomalies
• Use add-ins such as Microsoft Power Query to identify issues and clean data
• Enforces applicable data governance policies automatically
• Transform data into a variety of formats such as TSV, CSV, XLS, XML, and JSON

3. What is one of the steps in a typical data cleaning workflow? 

• Inspecting data to detect issues and errors
• Building classification models
• Establishing relationships between data events
• Clustering data

4. When you’re combining rows of data from multiple source tables into a single table, what kind of data transformation are you performing?

• Normalization
• Denormalization
• Joins
• Unions

5. When you detect a value in your data set that is vastly different from other observations in the same data set, what would you report that as?

• Missing value
• Outlier
• Irrelevant data
• Syntax error

Week 4 – Introduction to Data Analytics

Week 4 Quiz 1 – Introduction to Data Analytics

1. What is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of numerical or quantitative data?

• Calculus
• Statistics
• Pie
• Algebra

2. Data Mining is defined as the process of:

• Preparing raw data for analysis
• Identifying errors in data
• Extracting knowledge from data
• Filtering data based on pre-defined criteria

3. What type of data mining operations was R specifically built to handle?

• Calculating mean, median, and mode
• Sorting
• Filtering
• Classification of data 

4. When you’re calculating the middle value of a data field in a data set, what are you really calculating?

• Median
• Mean
• Average
• Mode

5. What is the general tendency of a set of data to change over time called?

• Variation
• Pattern
• Trend
• Anomaly

Week 4 Quiz 2 – Introduction to Data Analytics

1. “A presentation is not a data dump”. What is the one thing you would do to ensure your presentation is not a data dump?

• Deliver the findings in a single slide
• Include only that information as is needed to address the business problem
• Not use visuals in the presentation
• Not include facts and figures in the presentation

 2. What is the discipline of communicating information through the use of visual elements?

• Data visualization 
• Data profiling 
• Data regression 
• Data type conversion 

3. Matplotlib is a widely used Python data visualization library.

• True
• False

4. What is the goal of Data Visualization?

• Make information easy to comprehend, interpret, and retain
• Make collaboration easy
• Make the presentation look attractive
• Establish trust in the audience

5. What can you do to help your audience trust you?

• Make your presentation look good
• Share the detailed documentation of every aspect of your project so they can verify all details
• Share your data sources, hypotheses, and validations
• Hand them copies of the data sets you have used for analysis

Week 5 – Introduction to Data Analytics

Week 5 Quiz 1 – Introduction to Data Analytics

1. Which of the following statement describes Data Analyst Specialist Roles? 

• Analysts who specialize in specific fields like HR, Sales, and Finance
• Analysts who specialize in data lakes and data repositories
• Analysts who advance technical, statistical, and analytical skills, over time, to expert levels
• Analysts who can work with Machine and Deep Learning models

2. A Principal Data Analyst is responsible for:

• Being a domain specialist 
• Establishing processes in the team 
• Being well-versed in Big Data processing tools
• Having expertise in all tools and technologies used in data analytics 

3. Job roles such as Project Managers, Marketing Managers, and HR Managers, can achieve greater efficiency and effectiveness in their current roles by acquiring data analysis skills, and are therefore known as analytics-enabled job roles.

• True
• False

4. Which of these is essential for getting started and growing as a Data Analyst?

• Domain specialization
• A degree in Statistics
• A degree in Computer Science
• Love for numbers, a curious mind, and openness to learn

5. What Data Analysis roles may be best suited for people with little or no technical training?

• Data Scientist 
• Functional Analyst  
• Data Analyst 
• Big Data Engineer 

Week 5 Peer-graded Assignment – Introduction to Data Analytics

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