Introduction to Data Science

Why Study Data Science?

Opportunities: High demand across industries.
Vacancies: 151,717 data science positions remain unfilled due to lack of qualified professionals.
Job Satisfaction: Selected as most satisfying and best-paid job by Glassdoor (2016-2019).

Critical for modern businesses to interpret and utilize massive amounts of data.
Technological advancements have reduced the cost of data storage and processing.
Valuable for competitive business advantage and innovation.

Mathematics: Probability, linear algebra, statistics.
Programming: Proficiency in languages such as Python or R, familiarity with libraries like scikit-learn.
Domain Knowledge: Understanding the specific field you're working in (e.g., finance, healthcare, etc).
Machine Learning Algorithms: Knowledge of neural networks, regression, etc.
Data Visualization: Ability to effectively present data insights.
Multidisciplinary: Intersects computer science, math/statistics, and domain-specific knowledge.

Libraries: Use of existing Python libraries for implementing complex algorithms—no need to start from scratch.
Continuous Learning: Important to stay updated with new techniques and tools.

Python: Most recommended language today for data science, due to its extensive libraries and community support.
- Was competing with R, now has taken the lead in popularity.
R: Still popular among statisticians, useful for specific statistical tasks.
MATLAB: Powerful but costly; less commonly used in startups due to price.
Java and Scala: Other options but less common in the data science community.

Combination of Theory and Practice: Emphasis on learning theoretical concepts and applying them practically through libraries and projects.
Problem-Solving: Each lecture will tackle a specific problem, covering both its theory and implementation.
Machine Learning: Detailed study of critical machine learning algorithms to understand their application in real-world problems.

Interdisciplinary Field: Combines computer science, statistics, and domain-specific knowledge.
- Involves exploratory data analysis, data visualization, machine learning, and high-performance computing.
Emerging Field: No universally accepted definition; always evolving.
Diagram Representation: Intersection of computer science, math/statistics, and domain knowledge.

Data Generation: High volumes of data generated by digital activities and technological advancements.
Technological Advances: Availability of powerful hardware and cheaper storage facilitates complex data analysis.
Business Impact: Enables development of recommendation systems (e.g., Amazon), predictive analytics (e.g., Google), and other innovative solutions.
Role Models: Successful applications by entities like Google, hedge funds, and individuals like Nate Silver—demonstrating the impact of effective data analysis.