# 1
1. **Organizational Information**
    
    - **Page:** 2
    - **Notes:**
        - Contact: [klaus.kaiser@fh-dortmund.de](mailto:klaus.kaiser@fh-dortmund.de)
        - Room: B.2.04
        - Professor Klaus Kaiser has a background in data science across various industries.
2. **Introduction to Data Science**
    
    - **Page:** 4-7
    - **Notes:**
        - Definition: Data science is about turning raw data into meaningful insights.
        - Interdisciplinary field combining statistics, computing, and domain knowledge.
        - Historical context of the term “data science” from 1962 to 2001.
3. **What is Data Science?**
    
    - **Page:** 8-10
    - **Notes:**
        - Data science involves using methods and systems to extract knowledge from data.
        - The intersection of math/statistics, computer science, and domain knowledge is crucial.
4. **Practical Example of Data Science Project: Monkey Detection**
    
    - **Page:** 16-24
    - **Notes:**
        - Steps include understanding the problem, data collection, labeling, model training, and deployment.
5. **Related Fields in Data Science**
    
    - **Page:** 14-15
    - **Notes:**
        - Data Engineering: Building systems for data collection and processing.
        - Data Analysis: Inspecting and transforming data to inform decisions.
6. **Tasks in Data Science**
    
    - **Page:** 16
    - **Notes:**
        - Overview of different tasks within classical machine learning.
7. **Real-World Examples of Data Science Applications**
    
    - **Page:** 25-32
    - **Notes:**
        - Applications include autonomous driving, face recognition, predictive maintenance, fraud detection, recommendation systems, and cancer detection.
8. **Overview of Lecture Content**
    
    - **Page:** 34-35
    - **Notes:**
        - Basic topics include data basics, statistics, presentation techniques, and machine learning.
9. **Organizational: Schedule and Exam Information**
    
    - **Page:** 38-40
    - **Notes:**
        - Lecture and exercise schedules, language of instruction, and exam details (written exam with bonus points for data analytics).
10. **Expectations from Students**
    
    - **Page:** 42-43
    - **Notes:**
        - Emphasis on respect, professionalism, and willingness to participate.
11. **How to Continue in Data Science**
    
    - **Page:** 46-48
    - **Notes:**
        - Suggested literature for further reading and related courses available in the curriculum.
12. **Summary & References**
    
    - **Page:** 51-55
    - **Notes:**
        - Key takeaways: ability to explain data science and recognize its applications.
        - Important references for further study are provided.

# 2
- **Data Science Definition**: Creating knowledge from data using math, statistics, and computer science.
    
- **Data Types**:
    
    - **Structured**: Follows a predefined model (e.g., tables).
        
    - **Unstructured**: Lacks explicit structure (e.g., text, images).
        
- **Data Categories**:
    
    - Discrete vs. Continuous
        
    - Nominal, Ordinal, Interval, Ratio
        
    - Qualitative vs. Quantitative
        
- **Data Interchange Formats**: Common formats include CSV and JSON.
    
- **Data Trust**: Importance of data quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness, validity.

# 3
- **Data Categories**: Discrete, continuous, nominal, ordinal, interval, ratio, qualitative, and quantitative.
    
- **Data Interchange Formats**: Common formats include CSV and JSON.
    
- **Data Quality Dimensions**: Accuracy, completeness, consistency, timelessness, uniqueness, validity.
    
- **Data Types**: Primary (real-time, specific) vs. secondary (past, economical).
    
- **Data Acquisition Methods**: Capturing (sensors, surveys), retrieving (databases, APIs), collecting (web scraping).
    
- **FAIR and Open Data**: Principles for sustainable data usage and importance in scientific reproducibility.

# 4
- **Primary vs. Secondary Data**: Primary data is collected for a specific purpose, while secondary data is sourced from existing datasets.
    
- **Data Collection Techniques**: Includes scraping, which extracts data from websites, and considerations for legality and data protection.
    
- **Data Protection**: Emphasizes GDPR compliance, anonymization, and pseudonymization of personal data.
    
- **Statistics Basics**: Introduces descriptive and inductive statistics, frequency distributions, and graphical representations like histograms and bar charts.
    
- **FAIR Principles**: Focus on data findability, accessibility, interoperability, and reusability.

# 5
- **Data Scraping**: Extracts data from program outputs; should be a last resort.
    
- **Anonymization**: Removes personal info to protect identity; pseudonymization allows identification with additional info.
    
- **Statistics Types**: Descriptive, explorative, and inductive statistics.
    
- **Frequencies**: Absolute and relative frequencies; visualized through histograms, pie charts, and bar charts.
    
- **Central Tendencies**: Mode, median, and mean; box plots visualize data distribution.
    
- **Statistical Dispersion**: Measures spread of data; includes range, quartile range, and empirical variance.

# 6
- **Histograms**: Visual representation of frequency for continuous data.
    
- **Cumulative Frequency**: Measures total frequency up to a certain value.
    
- **Statistical Dispersion**: Includes empirical variance and standard deviation.
    
- **Bivariate Analysis**: Examines relationships between two variables.
    
- **Correlation Coefficients**: Quantifies the strength and direction of relationships.
    
- **Contingency Tables**: Displays frequencies of categorical variables.
    
- **Pearson Coefficient**: Measures linear correlation between metric variables.
    
- **Ordinal Data**: Can be analyzed using rank correlation methods.

# 7
- **Correlation**: Describes relationships between two variables using correlation coefficients based on variable types (nominal, ordinal, metric).
    
- **Contingency Tables**: Used for two-dimensional frequency distributions; includes conditional frequencies and measures of association.
    
- **Probability Theory**: Introduces random experiments, events, and Kolmogorov axioms; covers Laplace experiments and combinatorics.
    
- **Bayes’ Theorem**: Explains conditional probability and its application in real-world scenarios, such as medical testing.
    
- **Outcome**: Understanding of probability basics, combinatorial calculations, and Bayes’ theorem application.

# 8
- **Random Experiment**: Defined by well-defined conditions with unpredictable outcomes (e.g., dice throw).
    
- **Kolmogorov Axioms**: Fundamental properties of probability measures.
    
- **Random Variables**: Assign outcomes to numbers; can be discrete (countable values) or continuous (any value in an interval).
    
- **Distributions**: Includes discrete (e.g., binomial, uniform) and continuous (e.g., normal) distributions.
    
- **Expected Value & Variance**: Key metrics for understanding random variables' behavior.
    
- **Applications**: Used in statistical tests and linear regression.

# 9
- **Random Variables**: Defined as functions mapping outcomes to real numbers.
    
- **Discrete vs. Continuous Distributions**: Discrete has countable outcomes; continuous uses probability density functions.
    
- **Simple Linear Regression**: Models correlation between independent (X) and dependent (Y) variables.
    
- **Key Concepts**:
    
    - **Residual Analysis**: Evaluates fit of regression line.
        
    - **Determinacy Measure (R²)**: Indicates model fit; ranges from 0 to 1.
        
- **Estimation**: Parameters (β0, β1) estimated using least squares method.
    
- **Applications**: Used in various fields to predict outcomes based on correlations.