Fundamentals of Data Science - Chapter 1 Quiz
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Questions and Answers

Data science techniques have no historical roots in applied statistics.

False (B)

The final exam in the DS302 course is worth 40% of the total grade.

True (A)

Knowledge discovery, machine learning, and data mining are synonymous with data science.

True (A)

The bonus points for project and class work in the grading scheme can only be 1 point.

<p>False (B)</p> Signup and view all the answers

Data science is solely focused on structured data and does not deal with unstructured data.

<p>False (B)</p> Signup and view all the answers

The mid-term exam in the DS302 course takes place in the 7th week.

<p>True (A)</p> Signup and view all the answers

Artificial intelligence and machine learning are unrelated to data science.

<p>False (B)</p> Signup and view all the answers

Data science is a field that includes only information technology skills.

<p>False (B)</p> Signup and view all the answers

Artificial intelligence can sometimes perform better than humans in certain tasks.

<p>True (A)</p> Signup and view all the answers

Machine learning is a branch of artificial intelligence that allows machines to become more skilled in performing tasks without explicit programming.

<p>True (A)</p> Signup and view all the answers

Training data is irrelevant for machine learning algorithms.

<p>False (B)</p> Signup and view all the answers

A data scientist only needs programming skills to be effective in their role.

<p>False (B)</p> Signup and view all the answers

Data science combines elements of artificial intelligence, machine learning, statistics, and visualization.

<p>True (A)</p> Signup and view all the answers

Data scientists gather structured and unstructured data from various sources.

<p>True (A)</p> Signup and view all the answers

A self-driving car's incorrect decision can be attributed to its inability to recognize a detour due to poor programming.

<p>True (A)</p> Signup and view all the answers

Mathematics and statistics are not important for a data scientist's role.

<p>False (B)</p> Signup and view all the answers

Recommendation engines are purely an example of data science without any relation to artificial intelligence.

<p>False (B)</p> Signup and view all the answers

Data validation is a necessary step in the data processing phase.

<p>True (A)</p> Signup and view all the answers

Machines cannot learn from experience as they only follow predetermined rules.

<p>False (B)</p> Signup and view all the answers

A data scientist's primary role is to create visualization tools for business presentations.

<p>False (B)</p> Signup and view all the answers

Automated systems can help in detecting abusive content by analyzing examples of both abusive and non-abusive posts.

<p>True (A)</p> Signup and view all the answers

Inquisitiveness is listed as a vital skill for data scientists.

<p>True (A)</p> Signup and view all the answers

A data scientist does not need to understand business strategies.

<p>False (B)</p> Signup and view all the answers

The analysis phase for a data scientist involves identifying patterns and trends in the data.

<p>True (A)</p> Signup and view all the answers

The Data Science Life Cycle includes phases such as Capture, Analyze, and Communicate.

<p>True (A)</p> Signup and view all the answers

Preprocess or Process is the phase where data is captured from all relevant sources.

<p>False (B)</p> Signup and view all the answers

Data preparation involves cleansing, deduplicating, and reformatting the data.

<p>True (A)</p> Signup and view all the answers

The Analyze phase is solely about presenting insights through reports and charts.

<p>False (B)</p> Signup and view all the answers

A data scientist's main role is to analyze business data to extract meaningful insights.

<p>True (A)</p> Signup and view all the answers

ETL stands for Extract, Transfer, Load.

<p>False (B)</p> Signup and view all the answers

Data scientists do not examine biases or distributions during the preprocessing phase.

<p>False (B)</p> Signup and view all the answers

Communicate is the final phase where insights and impacts are visualized for decision makers.

<p>True (A)</p> Signup and view all the answers

Flashcards

Data Science

A collection of techniques to extract value from data, using methods from statistics, machine learning, visualization, logic, and computer science to find patterns, connections, and relationships in data.

Data Science Techniques

Methods in data science that find patterns and relationships in data to derive useful insights.

Data Science Synonymous Terms

Data science, knowledge discovery, machine learning, predictive analytics, and data mining are often used interchangeably.

Data Science Methods

The methods used in data science are based on evidence and historical observations.

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Data Science Applications

Data science helps increase efficiency, manage costs, find new market opportunities, and improve market advantage.

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Data Types in Data Science

Data science involves both structured and unstructured data.

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Mid-Term Exam

An exam given during the middle of the course, weighted at 20% of the final grade.

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Course Grading

Course grade is composed of multiple assessments, including mid-term, quizzes, assignments, class work, project discussions, practical exam, and a final exam.

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Final Exam

A comprehensive exam at the end of the course, weighted at 40% of the final grade.

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Artificial Intelligence (AI)

Giving machines the ability to mimic human behavior, especially cognitive functions like facial recognition and automated driving.

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Machine Learning

A subfield of AI that teaches machines to learn from experience, using data to improve their performance.

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Training Data

Data used to teach machines in machine learning.

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Data Science

Applying machine learning, AI, and quantitative methods to extract value from data.

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AI System Errors

Potential problems arising from incorrect programming, incomplete or inaccurate data, resulting in flawed AI outputs.

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Abusive Content Removal

Using machine learning to automatically identify and remove offensive content from online platforms.

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Input/Output Signals

Programmed actions converting input data into specific outputs following pre-determined rules.

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Machine Learning Algorithms

Programs that analyze input and output to create models for programs that turn input to output.

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Fraud Detection

Using data science to build models that identify and flag potential fraudulent transactions.

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Churn Prediction

Using data science to identify customers likely to leave a company, service, or product.

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Revenue Prediction

Using data science to forecast future revenue based on historical data and trends.

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Recommendation Engines

Using data science to suggest items or options such as movies or products to users.

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Data Science Life Cycle

The process of working with data, from gathering it to communicating insights.

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Capture (Data Science)

Gathering raw data from various sources (structured/unstructured).

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Prepare & Maintain (Data Science)

Formatting raw data for analysis; cleaning, deduplicating, and combining.

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Preprocess/Process (Data Science)

Examining data for biases, patterns, and suitability for analysis.

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Analyze (Data Science)

Using statistical methods, machine learning, and more to find insights.

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Communicate (Data Science)

Presenting insights in reports, charts, and visualizations.

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Data Scientist

A professional who analyzes data to find useful information.

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Data Scientist Role

A data scientist solves business problems by analyzing data to extract meaningful insights.

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Business Acumen Skills

Data scientists need to understand business strategy, problem-solving, communication, and presentation to effectively solve business problems.

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Technology Expertise Skills

Data scientists need skills like database knowledge (RDBMS, NoSQL), programming (Java, Python), open-source tools (Hadoop, R), data warehousing, data mining, and visualization (Tableau, etc) to work with data.

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Mathematics Expertise Skills

Data scientists use math, statistics, AI, machine learning, and related fields to analyze data and form conclusions.

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Problem Definition in Data Science

Data scientists start by understanding the business problem; this includes asking questions and gaining background about the problem.

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Data Collection (Data Science)

Data scientists gather structured and unstructured data from diverse sources (e.g., company data, public data).

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Data Cleaning and Validation

Data scientists prepare data for analysis. This involves cleansing, validating, and making the data uniform and accurate.

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Data Analysis and Pattern Recognition

Data scientists use analytic systems (e.g., algorithms, models) to identify patterns and trends in the data.

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Data Interpretation in Data Science

Data scientists draw conclusions about the data. They look for opportunities and solutions using these insights.

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Study Notes

Course Information

  • Course Title: Fundamentals of Data Science
  • Course Code: DS302
  • Instructor: Dr. Islam Saeed

Reference Books

  • Data Science: Concepts and Practice, Vijay Kotu and Bala Deshpande, 2019
  • DATA SCIENCE: FOUNDATION & FUNDAMENTALS, B. S. V. Vatika, L. C. Dabra, Gwalior, 2023

Course Grading

  • Mid-Term Exam: 20 points
  • Lectures Quizzes (Average): 10 points
  • Assignments: 5 points
  • Classwork (Lectures + Labs): 5 points
  • Project Discussion: 10 points
  • Practical Exam: 10 points
  • Bonus (for Project and classwork): 1-5 points
  • Final Exam: 40 points

Exams Schedule

  • Quiz 1: Week 3
  • Quiz 2: Week 5
  • Mid-Term: Week 7
  • Quiz 3: Week 10
  • Final Exam: Week 14

Course Content

  • Chapter 1: Introduction to Data Science

    • Data science is a collection of techniques to extract value from data
    • Data science's roots lie in applied statistics, machine learning, visualization, logic, and computer science
    • Data science techniques identify patterns, connections, and relationships within data

  • AI, Machine Learning, and Data Science

    • AI, machine learning, and data science are related
    • AI aims to give machines human-like capabilities
      • Examples include facial recognition, automated driving, mail sorting
    • Machine learning provides machines the ability to learn from experience
    • Training data is used to teach machines
    • Programs transform input signals into output signals
    • Machine learning algorithms create models for converting input to output
    • Data Science is the business application of machine learning, artificial intelligence, and quantitative fields like statistics, visualization, and mathematics

  • Data Science Life Cycle

    • Capture: Gathering of raw structured and unstructured data from various sources
    • Prepare and Maintain: Transforming raw data into a consistent format for analysis
      • Includes cleansing, deduplicating, reformatting, and data integration technologies like ETL (extract-transform-load)
    • Preprocess or Process: Analyzing data for biases, patterns, distributions to determine suitability
    • Analyze: Discovering insights through statistical analysis, predictive analytics, machine learning, and other methods
    • Communicate: Presenting insights via reports, charts, and visualizations to share with stakeholders

  • Data Scientist

    • Analyzes business data to extract meaningful insights
    • Solves business problems through a series of steps
    • Assesses problems by asking questions and understanding data
    • Selects appropriate variables and datasets
    • Collects structured and unstructured data
    • Processes data into an analytical format
    • Validates data for uniformity, completeness, and accuracy
    • Feeds processed data to analytic systems (ML algorithms or statistical models)
    • Analyzes data to identify patterns and trends
    • Interprets data to find solutions and opportunities
    • Presents results and insights to stakeholders

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Fundamentals of Data Science DS302 Lecture Notes PDF

Description

Test your knowledge on the basics of data science with this quiz focused on Chapter 1 of the course. You'll explore key concepts, techniques, and the interdisciplinary nature of data science. This assessment will help solidify your understanding as you embark on your data science journey.

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