Data Science places data mining, machine learning and statistics in context, both experimentally and socially. If you want to correctly deploy data mining techniques, you must be able to translate a (broadly formulated) question by a customer or a co-worker into an experimental set-up, to make the right choices for the methods you use, and to be able to process the data in the right form to apply those methods. After performing your experiments, you should not only be able to evaluate the results but also interpret and translate it back to the original question (e.g. by visualization). Socially, data science is of great importance because the media simplify many data-driven results and statistical research, often making mistakes. Thus, a lot of nonsense comes down on us and it is up to you, the data scientists of the future, to recognize, explain and correct that nonsense. This course is a combination of lectures and practical sessions, in which you take a hands-on approach to solving real-world data science problems.
You can explain the following machine learning concepts: supervised learning, unsupervised learning, classification, regression
You can list two advantages and two disadvantages of rule-based methods and of machine learning methods
You know and can explain the following experimental and statistical principles in your own words: bias, overfitting, cross validation, high-dimensional data, sparseness, dimensionality reduction, feature extraction, class imbalance.
You can describe typical use cases for Decision trees, kNN, SVM, Naïve Bayes and deep neural networks
You can explain the purpose and principles of feature extraction from semi-structured data, text data, image data and graph data.
You can explain the difference between engineered features and raw features, in content and in dimensionality
You know and can explain the use and importance of measuring the quality and reliability of human-labeled data.
You can give the definitions of the most important evaluation measures: Accuracy, Mean Squared Error, Precision, Recall, F1 and Mean Average Precision.
You know and can explain the benefits and challenges of big data.
You know and can explain the principles of responsible data science
You can recognize statistical nonsense in the media and erroneous visualizations, explain and correct it.
After completing the course, you can independently take the steps to set up and execute an experiment within data science, given a (broadly formulated) question:
- Task definition: You can create a clear definition of a task based on a general description of a task, consisting of (a) the research question, (b) whether the task is supervised or unsupervised, (c) whether it is a classification, regression or ranking task (or something else), (d) what the data are and (e) what the labels are;
- Data collection: If answering the question requests data is not given, then you can define what data you need and how to collect it. If you need explicit labels, you can set up a data annotation task for human raters;
- Data exploration: You can collect and visualize statistics about the data. You can calculate and interpret the inter-annotator agreement for annotated data.
- Pre-processing and feature extraction: You can write a Python script to read and process the data, extract features and store the feature vectors. You know how to engineer a low-dimensional feature set
- Model learning: You can apply unsupervised and supervised models to your data. You know how to make an informed decision on the type of classifier given the feature set. You can generate output for unseen data.
- Evaluation: You can correctly set up your model evaluation with a train / test split and cross validation if necessary. You can evaluate your output against human data. You know which evaluation measures you should use given the type of data and model. You can perform significance testing. You can do a sensible error analysis and feature analysis.
The most updated version of the timetables can be found on the students' website:
This course is partly combined with the course Data Science and Process Modelling from the I&E program (dr. Frank Takes). Some lectures are taught by dr. Takes and one assignment is overlapping.
Mode of instruction
The course webpage (http://tmr.liacs.nl/DS.html) will be updated when needed •• 13 lectures, 2x45 minutes
- 1st 45 minutes: lecture
o Online lectures in the second half of the course (as of April 1st) will be recorded with Kaltura, cut into topics, and connected to Blackboard.
o The students will follow the lecture from home, and answer a number of quiz questions that I will provide online.
o After that, we have an interactive lecture (15-30 minutes) via Kaltura Liverooms in which I discuss the quiz questions and the students can ask questions.
- 2nd 45 minutes: practical session (working on a data science problem in Python)
o The practical work can be completely done from home, on the student’s own laptops. Students have remote access to the LIACS cluster if they need more computer power than their laptop.
o The course TAs are available through email (firstname.lastname@example.org) to answer questions. I am in active contact with the TAs through slack
Assessment method, including grading
The assessment of the course consists of a written exam (60% of course grade) and a practical part (40% of course grade). The practical part is subdivided in (1). Visual analytics assignment (10%); (2) Feature extraction assignment (5%); (3) Model comparison assignment (5%); (4) Final assignment (20%). The grade for the written exam should be 5.5 or higher in order to complete the course. The average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted, the grade for that task is 0.
The assignments are submitted and graded in Blackboard.
All materials are distributed as usual in Blackboard .
Signing up for classes and exams
Please also register for this course in Blackboard.
Lecturer: Suzan Verberne (Skype: email@example.com)
Teaching Assistants E-mail: firstname.lastname@example.org