Introduction to Data Science

Learning Outcomes

  1. Use Python and other tools to scrape, clean, and process data
  2. Use data management techniques to store data locally and in cloud infrastructures
  3. Use statistical methods and visualization to quickly explore data
  4. Apply statistics and computational analysis to make predictions based on data
  5. Describe the outcome of data analysis using descriptive statistics and visualizations
  6. Use cluster and cloud infrastructure to perform data-intensive computation

Forms of Teaching

Lectures

Laboratory

Week by Week Schedule

  1. Data collection
  2. Data reshaping, Data cleanup
  3. Survey of exploratory data analysis
  4. Principal component analysis
  5. Least-squares regression, maximum likelihood estimation for linear regression
  6. Logistic regression
  7. Applied machine learning (predictive analysis; classification and prediction)
  8. Midterm exam
  9. Applied machine learning (predictive analysis; classification and prediction)
  10. Applied machine learning (predictive analysis; classification and prediction)
  11. Bagging and boosting
  12. Classification of resampling methods: randomisation (exact and approximate), Jackknife, Bootstrap, Cross-validation
  13. Data mining with Map-Reduce
  14. Bayesian versus Frequentist inference, Bayesian inference (estimation, prediction, model comparison)
  15. Final exam

Study Programmes

University undergraduate
Computing (study)
Elective Courses (5. semester)
Electrical Engineering and Information Technology (study)
Elective Courses (5. semester)

Literature

(.), Jake VanderPlas, Python Data Science Handbook, O'Reilly Media, 2016.,

General

ID 183455
  Winter semester
5 ECTS
L3 English Level
L1 e-Learning
39 Lectures
0 Exercises
12 Laboratory exercises
0 Project laboratory