Podcast
Questions and Answers
What is the primary focus of data science?
What is the primary focus of data science?
Which of the following is NOT commonly associated with data science?
Which of the following is NOT commonly associated with data science?
What contributes to the evidence-based nature of data science?
What contributes to the evidence-based nature of data science?
Which foundational disciplines are essential for data science?
Which foundational disciplines are essential for data science?
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Which statement best describes the relationship between AI, machine learning, and data science?
Which statement best describes the relationship between AI, machine learning, and data science?
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What aspect of business can data science improve?
What aspect of business can data science improve?
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What is a common process involved in data science?
What is a common process involved in data science?
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Which method is primarily used for discovering useful patterns in data?
Which method is primarily used for discovering useful patterns in data?
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What is the first phase of the Data Science life cycle?
What is the first phase of the Data Science life cycle?
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Which phase involves ensuring the raw data is in a consistent format for analysis?
Which phase involves ensuring the raw data is in a consistent format for analysis?
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During which phase do data scientists perform statistical analysis and apply machine learning algorithms?
During which phase do data scientists perform statistical analysis and apply machine learning algorithms?
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What is the primary focus of the Preprocess or Process phase in the Data Science life cycle?
What is the primary focus of the Preprocess or Process phase in the Data Science life cycle?
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What is the primary outcome of the Communicate phase in the Data Science life cycle?
What is the primary outcome of the Communicate phase in the Data Science life cycle?
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Which of the following actions occurs during the Capture phase?
Which of the following actions occurs during the Capture phase?
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What skill set is essential for a data scientist to effectively analyze data and provide insights?
What skill set is essential for a data scientist to effectively analyze data and provide insights?
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Which activity is associated with the Prepare and Maintain phase?
Which activity is associated with the Prepare and Maintain phase?
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What role does a data scientist play in the process of data analysis?
What role does a data scientist play in the process of data analysis?
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Which of the following steps does a data scientist take before data collection and analysis?
Which of the following steps does a data scientist take before data collection and analysis?
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Which programming languages are highlighted as essential for a data scientist?
Which programming languages are highlighted as essential for a data scientist?
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Which stage involves cleaning and validating data for correctness and completeness?
Which stage involves cleaning and validating data for correctness and completeness?
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What type of knowledge is necessary for a data scientist to handle unstructured data?
What type of knowledge is necessary for a data scientist to handle unstructured data?
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What is the primary role of a data scientist when interpreting rendered data?
What is the primary role of a data scientist when interpreting rendered data?
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What does a data scientist do with the data once it has been cleaned and rendered into a usable form?
What does a data scientist do with the data once it has been cleaned and rendered into a usable form?
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Which of the following skills is NOT typically emphasized for a data scientist?
Which of the following skills is NOT typically emphasized for a data scientist?
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What is the primary goal of artificial intelligence?
What is the primary goal of artificial intelligence?
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What common issue can occur with AI systems that are not properly programmed?
What common issue can occur with AI systems that are not properly programmed?
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What term describes the data used to teach machines in machine learning?
What term describes the data used to teach machines in machine learning?
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Which statement is true about the role of machine learning algorithms?
Which statement is true about the role of machine learning algorithms?
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How do machines learn to automate the removal of abusive content on platforms?
How do machines learn to automate the removal of abusive content on platforms?
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Data science can best be described as:
Data science can best be described as:
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Which of the following is an example of a machine learning application?
Which of the following is an example of a machine learning application?
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What might be an outcome of a well-functioning fraud alert model?
What might be an outcome of a well-functioning fraud alert model?
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Study Notes
Fundamentals of Data Science - DS302
- Course taught by 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
- Class work (Lectures + Labs): 5 points
- Project Discussion: 10 points
- Practical Exam: 10 points
- Bonus (for Project and class work): 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
Lecture 1
- Introduction to Data Science
What is Data Science?
- A compilation of techniques that extract value from data
- Techniques rooted in applied statistics, machine learning, visualization, logic, and computer science
- Relies on finding useful patterns, connections, and relationships within data
Data Science and Knowledge Discovery
- Also known as knowledge discovery, machine learning, predictive analytics, and data mining
- Underlying methods are decades if not centuries old
- Based on empirical knowledge, particularly historical observations
Advantages of Data Science
- Increases efficiency
- Manages costs
- Identifies new market opportunities
- Boosts market advantage
- Practice of extracting actionable insights from large data sets (structured and unstructured)
Data Science as an Interdisciplinary Field
- Combines statistics, computer science, predictive analytics, machine learning algorithm development, and new technologies
- Aims to gain insights from big data
Artificial Intelligence, Machine Learning, and Data Science
- Interrelated fields often used interchangeably
- Artificial intelligence aims to give machines the capability of mimicking human behavior, especially cognitive functions (e.g., facial recognition, automated driving)
- Machine learning is a sub-field or tool of AI for experience-based learning
Data as Experience for Machines
- Training data teaches machines
- A program (set of instructions) transforms input signals (data) into output signals (processed data) by predetermined rules and relationships.
- Machine learning algorithms take those input and output values to build a model for the process
Data Science in Action: Social Media Platforms
- Organizations use data science to automate the removal of abusive content
- Training machines requires examples of both abusive and non-abusive content, clearly indicating which is which
Applications of Data Science (User Focus)
- Recommendation engines (e.g., movie recommendations)
- Fraud detection models (e.g., fraudulent credit card transactions)
- Predicting customer churn
- Forecasting revenue
Data Science Life Cycle
- Capture: Gathering data (structured and unstructured) from various sources. Includes manual entry, web scraping, and system data
- Prepare and Maintain: Consistent formatting for analysis/modeling (e.g., cleansing, deduplication, and reformatting). Includes using ETL tools (extract, transform, load) for data combination/integration into a unified store (e.g., data warehouse, data lake)
- Preprocess or Process: Examining data for biases, patterns, ranges, and distributions to determine suitability for using this data with tools such as predictive analysis, machine learning, and/or deep learning
- Analyze: Discovering insights by using statistical analysis, predictive analytics. regression, machine learning, and deep learning that extract significant insights from prepared data
- Communicate: Presenting insights through data visualizations (reports, charts, etc.) for actionable insights for stakeholders
Data Scientist Roles and Responsibilities
- Analyzes business data to determine insightful information.
- Solves business problems following specific steps.
- Determines the problem from data gathering and analysis through asking and answering business questions and identifying variables and data sets to be selected from this data.
- Gathers structured and unstructured data from a wide variety of sources (enterprise, public data, etc.)
- Processes and transforms raw data to format suitable for analytical analysis
- Cleanses and validates data for uniformity, completeness, and accuracy
- Feeds analyzed data to analytical systems/models
- Analyzes the data for trends and patterns
- Develops solutions and opportunities from insights from the data
- Communicates the analysis/results to other appropriate stakeholders
Key Data Scientist Skills
- Business Acumen: understanding of business strategies, problem-solving, and communication skills.
- Technology Expertise: Knowledge of databases (RDBMS and NoSQL), programming languages (e.g., Java, Python), and open-source tools (e.g., Hadoop, R). Includes data warehousing and data mining techniques and visualization tools (e.g., Tableau, Flare, Google visualization APIs).
- Mathematical Expertise: Knowledge of mathematics, statistics, artificial intelligence (AI), machine learning, pattern recognition, and natural language processing.
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Description
This quiz covers essential concepts in the Fundamentals of Data Science course, focusing on techniques that extract value from data. Topics include applied statistics, machine learning, and data visualization. Prepare to test your understanding of data science fundamentals in this comprehensive assessment.
