Podcast
Questions and Answers
Data science techniques have no historical roots in applied statistics.
Data science techniques have no historical roots in applied statistics.
False
The final exam in the DS302 course is worth 40% of the total grade.
The final exam in the DS302 course is worth 40% of the total grade.
True
Knowledge discovery, machine learning, and data mining are synonymous with data science.
Knowledge discovery, machine learning, and data mining are synonymous with data science.
True
The bonus points for project and class work in the grading scheme can only be 1 point.
The bonus points for project and class work in the grading scheme can only be 1 point.
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Data science is solely focused on structured data and does not deal with unstructured data.
Data science is solely focused on structured data and does not deal with unstructured data.
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The mid-term exam in the DS302 course takes place in the 7th week.
The mid-term exam in the DS302 course takes place in the 7th week.
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Artificial intelligence and machine learning are unrelated to data science.
Artificial intelligence and machine learning are unrelated to data science.
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Data science is a field that includes only information technology skills.
Data science is a field that includes only information technology skills.
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Artificial intelligence can sometimes perform better than humans in certain tasks.
Artificial intelligence can sometimes perform better than humans in certain tasks.
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Machine learning is a branch of artificial intelligence that allows machines to become more skilled in performing tasks without explicit programming.
Machine learning is a branch of artificial intelligence that allows machines to become more skilled in performing tasks without explicit programming.
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Training data is irrelevant for machine learning algorithms.
Training data is irrelevant for machine learning algorithms.
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A data scientist only needs programming skills to be effective in their role.
A data scientist only needs programming skills to be effective in their role.
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Data science combines elements of artificial intelligence, machine learning, statistics, and visualization.
Data science combines elements of artificial intelligence, machine learning, statistics, and visualization.
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Data scientists gather structured and unstructured data from various sources.
Data scientists gather structured and unstructured data from various sources.
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A self-driving car's incorrect decision can be attributed to its inability to recognize a detour due to poor programming.
A self-driving car's incorrect decision can be attributed to its inability to recognize a detour due to poor programming.
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Mathematics and statistics are not important for a data scientist's role.
Mathematics and statistics are not important for a data scientist's role.
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Recommendation engines are purely an example of data science without any relation to artificial intelligence.
Recommendation engines are purely an example of data science without any relation to artificial intelligence.
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Data validation is a necessary step in the data processing phase.
Data validation is a necessary step in the data processing phase.
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Machines cannot learn from experience as they only follow predetermined rules.
Machines cannot learn from experience as they only follow predetermined rules.
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A data scientist's primary role is to create visualization tools for business presentations.
A data scientist's primary role is to create visualization tools for business presentations.
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Automated systems can help in detecting abusive content by analyzing examples of both abusive and non-abusive posts.
Automated systems can help in detecting abusive content by analyzing examples of both abusive and non-abusive posts.
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Inquisitiveness is listed as a vital skill for data scientists.
Inquisitiveness is listed as a vital skill for data scientists.
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A data scientist does not need to understand business strategies.
A data scientist does not need to understand business strategies.
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The analysis phase for a data scientist involves identifying patterns and trends in the data.
The analysis phase for a data scientist involves identifying patterns and trends in the data.
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The Data Science Life Cycle includes phases such as Capture, Analyze, and Communicate.
The Data Science Life Cycle includes phases such as Capture, Analyze, and Communicate.
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Preprocess or Process is the phase where data is captured from all relevant sources.
Preprocess or Process is the phase where data is captured from all relevant sources.
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Data preparation involves cleansing, deduplicating, and reformatting the data.
Data preparation involves cleansing, deduplicating, and reformatting the data.
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The Analyze phase is solely about presenting insights through reports and charts.
The Analyze phase is solely about presenting insights through reports and charts.
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A data scientist's main role is to analyze business data to extract meaningful insights.
A data scientist's main role is to analyze business data to extract meaningful insights.
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ETL stands for Extract, Transfer, Load.
ETL stands for Extract, Transfer, Load.
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Data scientists do not examine biases or distributions during the preprocessing phase.
Data scientists do not examine biases or distributions during the preprocessing phase.
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Communicate is the final phase where insights and impacts are visualized for decision makers.
Communicate is the final phase where insights and impacts are visualized for decision makers.
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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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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.
