Podcast
Questions and Answers
Which of the following techniques is NOT traditionally associated with data science?
Which of the following techniques is NOT traditionally associated with data science?
- Computer programming
- Business analytics (correct)
- Data visualization
- Applied statistics
What is the main purpose of employing data science techniques in companies?
What is the main purpose of employing data science techniques in companies?
- To maintain existing market positions
- To reduce the workforce
- To extract actionable insights from data (correct)
- To enhance creative processes
Which field is NOT considered part of the foundational knowledge for data science?
Which field is NOT considered part of the foundational knowledge for data science?
- Machine learning
- Predictive analytics
- Statistics
- Sociology (correct)
How can data science benefit companies in terms of market opportunities?
How can data science benefit companies in terms of market opportunities?
What is a common misconception about data science, artificial intelligence, and machine learning?
What is a common misconception about data science, artificial intelligence, and machine learning?
What is a potential reason why an AI system may fail to perform correctly?
What is a potential reason why an AI system may fail to perform correctly?
Which of the following best describes the role of training data in machine learning?
Which of the following best describes the role of training data in machine learning?
In what way does data science relate to artificial intelligence and machine learning?
In what way does data science relate to artificial intelligence and machine learning?
How can machines be effectively taught to identify abusive content online?
How can machines be effectively taught to identify abusive content online?
How do machine learning algorithms establish models for input-output conversions?
How do machine learning algorithms establish models for input-output conversions?
Flashcards
Data Science
Data Science
A collection of techniques used to extract value from data by identifying patterns, connections, and relationships.
Data Science Techniques
Data Science Techniques
Methods rooted in statistics, machine learning, visualization, logic, and computer science, used to find patterns in data.
Knowledge Discovery
Knowledge Discovery
Another name for data science, emphasizing the process of uncovering valuable insights from data.
Predictive Analytics
Predictive Analytics
A way to use data science to forecast future trends and outcomes.
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Data Mining
Data Mining
The process of extracting valuable information from large amounts of data.
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Structured Data
Structured Data
Data organized in a predefined format (like tables in a database).
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Unstructured Data
Unstructured Data
Data that does not have a predefined format.
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DS302 Course Grading
DS302 Course Grading
Midterm (20%), Quizzes (10%), Assignments (5%), Class work (5%), Project Discussion (10%), Practical Exam (10%), Final Exam (40%), Bonus (1-5% for project and class work)
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Midterm Exam
Midterm Exam
An exam given during the middle of the course, evaluating knowledge and understanding of the concepts taught
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Final Exam
Final Exam
A comprehensive exam given at the end of the course, evaluating the student's overall understanding
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Artificial Intelligence (AI)
Artificial Intelligence (AI)
Giving machines the ability to mimic human behavior, especially cognitive functions, like facial recognition or automated driving.
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Machine Learning (ML)
Machine Learning (ML)
A part of AI that teaches machines to learn from experience through data.
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Training Data
Training Data
Data used to teach machines in machine learning.
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Data Science
Data Science
Applying machine learning, AI, and quantitative fields (like statistics) to extract useful information from data.
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AI System Failure
AI System Failure
Occurs when an AI system isn't properly programmed or trained, or when it receives poor/incomplete data, leading to inaccurate outcomes.
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Machine Learning Algorithm
Machine Learning Algorithm
A set of rules that helps a machine learn from data to find a model for converting input into output.
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Abusive Content Removal
Abusive Content Removal
Using AI/machine learning to automatically identify and remove inappropriate content.
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Recommendation Engine
Recommendation Engine
A system that suggests items (e.g., movies, products) based on user preferences.
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Fraud Detection
Fraud Detection
Using AI and machine learning to detect fraudulent credit card transactions.
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Customer Churn Prediction
Customer Churn Prediction
Using AI/ML to anticipate which customers are likely to stop doing business with a company.
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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
- 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
- Week 3: Quiz 1
- Week 5: Quiz 2
- Week 7: Mid-Term
- Week 10: Quiz 3
- Week 14: Final Exam
Topic: What is Data Science?
- Data science is a compilation of techniques used to extract value from data.
- Techniques have roots in applied statistics, machine learning, visualization, logic, and computer science.
- Data science focuses on finding patterns, connections, and relationships within data.
Topic: AI, Machine Learning, and Data Science
- Artificial intelligence, machine learning, and data science are closely related.
- They are often used interchangeably in common language and communication.
- Artificial intelligence aims to give machines the ability to mimic human behavior, especially cognitive functions (e.g., facial recognition, automated driving).
- Machine learning is a subfield or tool of AI. It enables machines to learn from experience by taking input and output patterns to build a model for a program that converts input to output.
- An AI system's accuracy can be diminished by poor programming or inaccurate/incomplete input data.
- Data is the experience that machines use for learning, called training data.
Topic: Data Science Life Cycle
- Capture: Gathering data from various sources (manual, web scraping, systems).
- Prepare and Maintain: Formatting data for use in models. This includes cleaning, deduplicating, and reformatting data using ETL techniques.
- Preprocess or Process: Examination of data patterns/trends/biases and using statistical models for analytics.
- Analyze: Discovering patterns and insights using statistical analysis, and machine learning techniques like regression.
- Communicate: Presenting insights in organized formats like reports, charts, and visualizations to decision-makers.
Topic: Data Scientist Role
- A data scientist analyzes business data to derive meaningful insights, solving business problems.
- A data scientist tackles problems by asking the right questions and pinpointing the relevant variables/data sets.
- Data collection/analysis procedures include processing/formatting raw data for analysis, and validating the data for accuracy, completeness, and consistency.
- The final steps involve analyzing the data for trends and patterns, and presenting findings to stakeholders for decision-making.
Topic: Data Scientist Skills
- Business Acumen: Understanding the domain, business strategy, problem-solving skills, communication and presentation abilities, inquisitiveness & critical thinking.
- Technology Expertise: Database knowledge (RDBMS, NoSQL databases), programming languages (e.g., Java, Python), open-source tools (e.g., Hadoop, R), data warehousing, data mining, and data visualization tools (e.g., Tableau, Flare, Google visualization APIs).
- Mathematical Expertise: Mathematics and statistical skills, artificial intelligence (AI), machine learning, pattern recognition, and natural language processing.
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