Data Science Fundamentals and Applications
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Questions and Answers

In a data science study, which step directly follows the data collection phase?

  • Problem Definition
  • Conclusion and Recommendation
  • Data Cleaning
  • Data Analysis (correct)

Which type of statistics involves using sample data to draw conclusions about a larger population?

  • Predictive Statistics
  • Comparative Statistics
  • Descriptive Statistics
  • Inferential Statistics (correct)

A researcher aims to understand customer sentiment from a large set of social media posts. Which data collection method and data type are most relevant for this task?

  • Questionnaires; Structured Data
  • Text Mining; Unstructured Data (correct)
  • Interviews; Unstructured Data
  • Direct Observation; Structured Data

What is the primary goal of data cleaning in the context of a data science project?

<p>To remove errors, noise, and inconsistencies from the data (D)</p> Signup and view all the answers

Which of the following scenarios best illustrates the application of descriptive statistics?

<p>Calculating the average age and standard deviation of participants in a study. (C)</p> Signup and view all the answers

A company wants to analyze customer feedback from phone conversations to identify common complaints. Which combination of data collection method and analytical technique is most appropriate?

<p>Interviews; Text Mining (B)</p> Signup and view all the answers

Which of the following is a critical consideration when using questionnaires for data collection?

<p>Ensuring the sample accurately represents the entire population (A)</p> Signup and view all the answers

During data collection using direct observation methods, what is a significant challenge that needs to be addressed?

<p>Filtering out irrelevant or 'noisy' data to improve data quality (B)</p> Signup and view all the answers

When is it most appropriate to delete entire records with missing values from a dataset?

<p>When there are a minimal number of missing values. (B)</p> Signup and view all the answers

Which of the following scenarios best describes the application of K-Nearest Neighbors (K-NN) imputation for handling missing data?

<p>Predicting missing age values based on the ages of the individuals with similar characteristics. (D)</p> Signup and view all the answers

A data analyst observes that a large number of respondents in a survey have skipped a question about their income. Which of the following methods would be the least appropriate for handling these missing values?

<p>Deleting all the records with missing income values from the dataset. (B)</p> Signup and view all the answers

Which sequence accurately represents the transition from traditional statistical models to contemporary data science methodologies?

<p>Traditional statistical models $\rightarrow$ Machine learning algorithms (B)</p> Signup and view all the answers

In the context of data analysis, what distinguishes inferential analysis from descriptive analysis?

<p>Inferential analysis draws conclusions about a population based on sample data, while descriptive analysis summarizes data. (D)</p> Signup and view all the answers

A researcher is using sample data to test a claim about the average income of all homeowners in a city. What type of data analysis is the researcher conducting?

<p>Inferential Analysis (D)</p> Signup and view all the answers

What is the primary purpose of data visualization within the scope of data science?

<p>To interpret data and communicate results effectively. (A)</p> Signup and view all the answers

In the context of data science, how does Natural Language Processing (NLP) primarily contribute to the field?

<p>By allowing computers to understand and process human language. (C)</p> Signup and view all the answers

Which of the following tasks falls under the umbrella of 'data wrangling' rather than 'data cleaning'?

<p>Standardizing date formats across different data sources. (A)</p> Signup and view all the answers

In the equation $y = f(X, parameters) + \epsilon$ representing a supervised learning model, what does $\epsilon$ represent?

<p>The random error or noise in the model. (C)</p> Signup and view all the answers

What distinguishes 'Big Data Processing' from traditional data processing methods?

<p>Big Data Processing emphasizes the storage, transformation, and analysis of very large datasets. (D)</p> Signup and view all the answers

Which of the following is a primary goal of supervised learning?

<p>Predicting future outcomes based on labeled training data. (B)</p> Signup and view all the answers

How do AI-powered systems enhance decision-making processes in data science applications?

<p>By delivering real-time predictions and automating complex tasks. (D)</p> Signup and view all the answers

A financial analyst aims to predict potential stock values for the next quarter. Which data science application is most suitable for this task?

<p>Financial Modeling (C)</p> Signup and view all the answers

In the context of key components of Data Science, which factor ensures that data insights are relevant and applicable to real-world problems?

<p>In-depth knowledge and understanding of the specific field of application. (B)</p> Signup and view all the answers

Given the characteristics of big data (Volume, Variety, Velocity, Veracity), how does 'Veracity' directly impact the outcomes of data analysis?

<p>It addresses the accuracy, reliability, and quality of data, influencing decision-making. (C)</p> Signup and view all the answers

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Flashcards

Problem Definition

First step in a data science study, defining the aims and scope.

Data Collection

Gathering and preparing relevant data for analysis.

Data Analysis

Extracting meaningful insights and patterns from collected data.

Conclusion

Forming decisions and recommendations based on the analysis.

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Descriptive Statistics

Summarizes and describes data from a sample using measures like mean and median.

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Inferential Statistics

Uses sample data to make predictions or inferences about a larger population.

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Structured Data

Data organized in a predefined format, like tables with rows and columns.

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Unstructured Data

Data lacking a predefined format, like text, images, and videos.

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Conventional Data Approach

Collecting only necessary data to minimize data cleaning efforts.

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Automated Data Approach

Collecting all available data, then cleaning it extensively.

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Data Cleaning

Removing errors & inconsistencies from raw data to improve quality.

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Data Wrangling

Restructuring & transforming data into an analyzable format.

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Mean Imputation

Replacing missing values with the average of the available data.

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Inferential Analysis

Using sample data to make generalizations about a larger group.

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Hypothesis Testing

Validating or rejecting a statement about a population parameter.

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Supervised Learning

Training a model with labeled data to predict outcomes.

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Algorithms in Data Science

Using algorithms instead of traditional models.

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Big Data Processing

Storing, transforming, and analyzing massive datasets.

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Data Visualization

Using data visualization to clearly display and explain results.

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Predictive Modeling

Using past data to forecast outcomes and trends.

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Natural Language Processing (NLP)

Enables computers to process and understand human language.

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Automation & Decision Support

Real-time predictions to support decision-making processes.

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Big Data Characteristics (4 V's)

Large-scale, diverse, real-time, and accurate data.

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Challenges of Big Data

Noise, bias, and incompleteness impacting analysis.

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Study Notes

  • Data Science Fundamentals and Applications

Study Process

  • The data science study process involves four steps
  • Problem Definition: Define the study's objectives
  • Data Collection: Gather and process relevant data
  • Analysis: Extract useful information and identify patterns
  • Conclusion: Make decisions and provide recommendations

Example Study: Proportion of Smokers in Sri Lanka

  • Problem: Determine the proportion of smokers in Sri Lanka
  • Population: The entire population of Sri Lanka
  • Sample: A smaller, representative sample (e.g., 1000 people) is used for estimation instead of the entire population
  • Sampling Methods: Improve accuracy

Types of Statistics

  • Statistics is divided into two main categories

Descriptive Statistics

  • Summarizes and describes data from a given sample
  • Includes measures such as mean, median, mode, and standard deviation

Inferential Statistics

  • Uses sample data to make predictions about a larger population
  • Includes hypothesis testing, confidence intervals, and regression analysis

Data Collection Methods

  • Includes questionnaires, direct observation, and interviews
  • Questionnaires (Surveys):
  • Can be automated using digital tools
  • Requires technical devices and digital literacy
  • May not always represent the entire population
  • Direct Observation:
  • Uses sensors, cameras, and scanners for data collection
  • Efficient but requires filtering of irrelevant (noisy) data
  • Interviews:
  • Can be conducted in person, over the phone, or through voice/video recordings
  • Requires text mining to analyze spoken information

Structured vs. Unstructured Data

  • Structured Data (Conventional Method):
  • Organized in tables with rows (observations) and columns (variables)
  • Example: Data in a spreadsheet
  • Unstructured Data:
  • Includes speech, videos, images, and text
  • Requires techniques like text mining and topic modeling to extract insights
  • Example Use Case: Analyzing speech from news channels to detect trending topics; extracting key insights from social media posts

Data Cleaning

  • The process of removing errors and inconsistencies from data

Steps in Data Cleaning

  • Data Collection: Raw data is gathered
  • Data Cleaning: Errors, noise, and irrelevant data are removed
  • Data Analysis: The refined dataset is analyzed for insights

Methods for Data Cleaning

  • Conventional Approach: Collect only necessary data to minimize cleaning effort.
  • Automated Approach: Collect all data, then perform extensive cleaning

Data Wrangling vs. Data Cleaning

  • Data Cleaning: Focuses on removing errors and inconsistencies from raw data
  • Data Wrangling: Involves restructuring and transforming data into a format suitable for analysis

Handling Missing Values

  • Missing values occur due to various reasons, such as respondents skipping sensitive questions (e.g., age, salary)

Solutions for Handling Missing Values

  • Deleting Entire Records: Only if a minimal number of missing values exist
  • Replacing Missing Values: Using estimation techniques:
  • Mean Imputation: Replacing missing values with the mean of available data
  • K-Nearest Neighbors (K-NN) Imputation: Filling missing values using the closest observations

Data Analysis

  • Classified into Descriptive Analysis and Inferential Analysis

Descriptive Analysis

  • Focuses on summarizing and visualizing data
  • Includes tables, graphs, and summary statistics

Inferential Analysis

  • Uses sample data to make generalizations about a population
  • Includes Estimation, Predictive Analysis, and Hypothesis Testing

Hypothesis Testing

  • A hypothesis is a statement about a population parameter
  • Hypothesis testing is used to validate or reject a hypothesis using sample data

Statistical Learning

  • Involves extracting patterns and insights from data
  • It is classified into Supervised Learning and Unsupervised Learning

Supervised Learning

  • Involves training a model using labeled data
  • Example: Predicting whether a customer will continue using a network provider

Supervised Learning Model

  • To relate input (X) and output (y): y = f(X, parameters) + ∈ (random error)

Goals of Supervised Learning

  • Understand relationships between inputs and outputs
  • Predict future outcomes

Applications of Supervised Learning

  • Email Spam Detection
  • Medical Diagnosis
  • Stock Price Prediction
  • Customer Churn Prediction

What is Data Science?

  • Data Science (DS) is an interdisciplinary field that combines:
  • Mathematics
  • Statistics
  • Computer Science

Purpose of Data Science

  • Extracting knowledge and insights from structured and unstructured data
  • Using scientific methods, algorithms, and processes to analyze data

Use case examples for Data Science

  • Data Interpretation
  • Graph Visualization
  • Automated Data Collection

Components of Data Science

  • Algorithms
  • Processes
  • Systems

Algorithms in Data Science

  • Modern data science replaces traditional statistical models with machine learning algorithms

Systems in Data Science

  • Big Data Storage and Data Management

Scope of Data Science

Data Analysis and Visualization

  • Data visualization helps interpret and communicate results effectively

Predictive Modeling

  • Uses past data to predict future outcomes
  • Example: Trend forecasting

Natural Language Processing (NLP)

  • Enables computers to understand human language
  • Applications: Text Analysis, Machine Translation, Speech Recognition, Summarization & Recommendations

Big Data Processing

  • Focuses on storing, transforming, and analyzing large datasets

Automation & Decision Support

  • AI-powered systems provide real-time predictions for decision-making
  • Example: Fraud detection in banking using AI

Applications of Data Science

  • Data Science is applied in various industries

Industries Benefitting from Data Science

  • Business Analytics & Decision Making
  • Healthcare & Medical Research
  • Financial Modeling
  • Social Media Analysis
  • Scientific Research
  • Artificial Intelligence & Machine Learning

Profit Prediction

  • Estimating next year's profit based on historical data
  • Involves: Statistical modeling, Predictive analytics, Cost optimization through automation

Key Components of Data Science

  • Data (Structured & Unstructured)
  • Tools & Technologies
  • Statistical Methods (Machine Learning & AI)
  • Domain Expertise
  • Communication & Visualization

Data Software & Platforms

  • Data Analysis Software: MINITAB, SAS, Excel, R, Python
  • Big Data Tools: Jupyter Notebook, Power BI, Tableau
  • Platforms: Hadoop, Spark, AWS, Google Cloud, Microsoft Azure

Characteristics of Big Data

  • Volume: Large-scale data
  • Variety: Different data formats
  • Velocity: Real-time data processing
  • Veracity: Data accuracy and quality

Challenges of Big Data

  • Noise, bias, and incomplete data affect decision-making

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Fundamentals of Data Science PDF

Description

Explores the data science study process, including problem definition, data collection, analysis, and conclusion. Covers types of statistics, including descriptive and inferential.

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