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?
What is the main purpose of employing data science techniques in companies?
What is the main purpose of employing data science techniques in companies?
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?
How can data science benefit companies in terms of market opportunities?
How can data science benefit companies in terms of market opportunities?
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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?
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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?
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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?
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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?
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How can machines be effectively taught to identify abusive content online?
How can machines be effectively taught to identify abusive content online?
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How do machine learning algorithms establish models for input-output conversions?
How do machine learning algorithms establish models for input-output conversions?
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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
- 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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