Applied Data Analytics Unit 1

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Questions and Answers

Which of the following best describes the data analytics process?

Exploration, Visualization, Cleaning, Processing, Summarization

Collection, Cleaning, Exploration, Analysis, Visualization (correct)

Data Mining, Cleaning, Reporting, Analysis, Presentation

Collection, Analysis, Visualization, Reporting, Interpretation

What does the 'Variety' aspect of Big Data refer to?

Speed at which data is generated (correct)

Quality of the data

Methods used for processing the data

Size of the data sets

What is one technique used in data preprocessing to address missing data?

Data normalization

Data transformation

Imputation (correct)

Data augmentation

In which type of learning does the model learn from labeled data?

Supervised learning Signup and view all the answers

Which of the following visualization techniques is best suited for identifying outliers in a dataset?

Box plot Signup and view all the answers

What is the primary purpose of Exploratory Data Analysis (EDA)?

To summarize the main characteristics of the data Signup and view all the answers

What does the term 'Veracity' in Big Data refer to?

The reliability and accuracy of the data Signup and view all the answers

Which of the following tools is commonly used for creating data visualizations?

Tableau Signup and view all the answers

Which data collection method involves a one-on-one conversation between an interviewer and a respondent?

Interviews Signup and view all the answers

What is the primary purpose of using focus groups in research?

To gain qualitative insights on specific topics through group discussion Signup and view all the answers

Which of the following best defines observation in research?

Watching and recording behavior in a natural setting Signup and view all the answers

What is a characteristic feature of experiments in scientific research?

Manipulating variables to observe effects Signup and view all the answers

Which tool is commonly used for creating online surveys?

SurveyMonkey Signup and view all the answers

Which of the following is an example of a dynamic scraping technique?

Scraping data from JavaScript-rendered content Signup and view all the answers

What is the function of REST APIs?

To retrieve data from web services Signup and view all the answers

Which of the following is a technique used with NoSQL databases?

Querying using MongoDB Signup and view all the answers

Which of the following techniques is used for collecting data in discrete intervals?

Batch Data Collection Signup and view all the answers

What is the main purpose of using Google Analytics?

Track and report website traffic Signup and view all the answers

Which tool is specifically designed for building electronic devices for data collection?

Raspberry Pi Signup and view all the answers

Which of the following techniques is employed to identify trends and patterns from social media data?

Trend Analysis Signup and view all the answers

What type of data can Smart Sensors typically collect?

Environmental data like temperature and humidity Signup and view all the answers

What is a common use of Kaggle Datasets?

Accessing a variety of datasets for analysis Signup and view all the answers

Which tool is typically utilized for social listening and monitoring mentions and trends?

Hootsuite Signup and view all the answers

Which technique helps in monitoring health conditions through data collection?

Health Monitoring using wearable devices Signup and view all the answers

Which technique is NOT commonly used to handle missing data?

Transformation Signup and view all the answers

What is the primary purpose of data standardization?

To give data a mean of 0 and a standard deviation of 1 Signup and view all the answers

Which tool is known for having built-in data cleaning features?

Microsoft Excel Signup and view all the answers

What does Winsorization involve?

Capping outliers to specified percentiles Signup and view all the answers

Which technique is used during the data entry process to correct errors?

Validation Rules Signup and view all the answers

When is the technique of aggregation most appropriately used?

To summarize data to higher levels like totals or averages Signup and view all the answers

What is one limitation of Microsoft Excel for data cleaning?

Limited scalability for large datasets Signup and view all the answers

What technique involves reshaping data for analysis?

Transformation Signup and view all the answers

Which type of data analysis focuses on studying the relationship between two variables?

Bivariate analysis Signup and view all the answers

What is the primary purpose of data visualization?

To graphically represent information and data Signup and view all the answers

Which type of visualization technique commonly uses a time variable on the x-axis?

Line plots Signup and view all the answers

What types of data can be visualized for market research purposes?

Both numerical and categorical data Signup and view all the answers

Which of the following is NOT considered a type of data visualization?

Text summaries Signup and view all the answers

To analyze the behavior of only one variable at a time, which type of analysis would be used?

Univariate analysis Signup and view all the answers

Which data visualization tool helps combine multiple visualizations into one platform?

Dashboards Signup and view all the answers

What is a characteristic of multivariate analysis?

Analyzes more than two variables simultaneously Signup and view all the answers

Study Notes

Fundamentals of Data Analytics

Data analytics involves systematic analysis to derive insights from data, essential for informed decision-making.
Key types of data include structured, unstructured, and semi-structured, sourced from databases, sensors, social media, etc.
Data analytics process stages: Collection, Cleaning, Exploration, Analysis, Visualization.

Data Preprocessing and Cleaning

Data cleaning removes inaccuracies, duplicates, and inconsistencies to ensure quality.
Techniques for handling missing data: Imputation, Deletion, Flagging.
Outliers can distort analysis; use statistical methods (z-scores, IQR) for detection and management.
Exploratory Data Analysis (EDA) employs descriptive statistics to summarize data characteristics.

Data Visualization Techniques

Key methods include histograms, scatter plots, and box plots to unveil patterns and trends.
Effective visualization principles enhance communication of analytical insights.

Data Analysis Techniques and Tools

Statistical analysis involves probability distributions, hypothesis testing, and regression analysis.
Machine learning types: Supervised (classification/regression), Unsupervised (clustering), and Semi-supervised learning.
Text analysis incorporates Natural Language Processing for sentiment analysis, with diverse applications.

Tools for Data Visualization

Popular tools: Matplotlib, Seaborn, and Tableau, for designing actionable visual representations of data insights.

Applied Data Analytics Projects

Selecting suitable projects requires careful data collection, preparation, and analysis technique application.
Presenting findings necessitates clear communication of insights, alongside reflective assessment of challenges faced.

Big Data Properties

Characteristics of Big Data encapsulate the 4Vs: Volume (size), Variety (types), Velocity (speed), Veracity (accuracy).
Data volumes have escalated to terabytes and petabytes, necessitating advanced processing technologies beyond traditional software.

Data Collection Methods

Interviews can be structured or unstructured, utilized to gather qualitative data.
Focus groups facilitate moderated discussions for deeper insights on specific topics.
Observational methods allow real-time data gathering in natural settings, can be overt or covert.
Experiments manipulate variables to evaluate effects; case studies provide detailed analysis of specific phenomena.

Survey and Questionnaire Tools

Tools: SurveyMonkey, Google Forms, and Qualtrics for diverse survey designs.
Techniques for data collection include online, paper-based, and phone surveys.

Web Scraping and APIs

Tools like BeautifulSoup and Scrapy facilitate data extraction from web pages.
APIs allow for efficient data retrieval from web services (e.g., Twitter, Google Maps).

Database Queries and Data Entry

SQL and NoSQL databases enable data querying for relational and non-relational data respectively.
Tools for data entry include Google Forms and Typeform, enhancing data gathering efficiency.

Devices like Raspberry Pi and Arduino monitor real-time data from sensors.
Social media provides vast datasets for sentiment and trend analysis through APIs and listening tools.

Data Cleaning Techniques

Imputation strategies: replacing missing values using statistical means.
Statistical methods employed for identifying outliers include z-scores and winsorization.
Data transformation techniques include aggregation, pivoting, and normalization.

Data Visualization Insights

Graphical representation aids in identifying trends and correlations among variables.
Univariate, bivariate, and multivariate analysis summarize varying levels of data complexity.
Key visualization techniques include line plots, essential for time-series data representation.

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Description

This quiz covers the fundamentals of data analytics, including definitions, importance, and types of data. It explores the data analytics process from collection to visualization, focusing on preprocessing and data cleaning techniques. Additionally, you'll learn about exploratory data analysis and various visualization methods.