HARD_LEC1

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.