Scheduled Self-Study PYTHON COURSE
Data analysis with Python is a practical introduction to data analysis using a large number of programming exercises.
This course edition complements the non-scheduled self-study version of the course by following a weekly schedule. Passing this scheduled version of the course requires completing 50% of the automatically assessed weekly exercises (using Test-My-Code system, TMC). After these are done, one can proceed to the final project and to the exam. Grading is based on the TMC assignments, project, its peer-review, and the exam. Project and exam and conducted in Moodle.
Course materials and TMC assignments are available for everyone without officially enrolling on the course. Project and exam require registration through Open University: You will be prompted to register once you have sufficient progress with TMC assignments. See Section Registration and fee for more details on Open University registration requirements.
If you wish to conduct the course in a more relaxed schedule, you may consider instead conducting the non-scheduled self-study version of the course that will be available between summer 2020-Spring 2021.
**NOTE**
Teaching arrangements have changed (17.3.2020):
- Due to the outbreak situation, we are going fully virtual; Friday workshops are canceled.
- Exam has been moved from Examinarium to Moodle
- Course registration through Open University has been opened. See message below or section Moodle access for course project and exam.
Meddelande
Interaktion
A Telegram chat room for the course has been opened. We recommend that you use the channel either through a web browser or the Telegram desktop application.
You can reach the channel through https://t.me/tkt_dap. The browser version can be reached through https://web.telegram.org/.
The discussion channel is based on peer support. The teachers of the course (Saska Dönges and Veli Mäkinen) are participating the discussion on voluntary basis if time permits.
Tidsschema
Material
Föreläsningsmaterial
Instruktioner
Uppgifterna
TMC assignments
Start the course by solving the TMC assignments.
Final project
After completing the TMC assignments, you can proceed with the final project.
Select one of the project options listed below, follow the instructions, and return your solutions to Moodle in Jupyter notebook format . Then conduct peer-review: You should evaluate and give feedback on your own report and two other reports.
The projects on sequence analysis and on regression analysis are integrated to the TMC system and the course material includes detailed instructions on these. For the project on fossil data analysis all the instructions are in the pdf below, and you can start from an empty Jupyter notebook to conduct the project.
The instructions on peer-review will be visible in Moodle after the submission deadline, but meanwhile here are the evaluation criteria:
1. Give a grade 0…5 on the correctness of solutions, and provide constructive comments where you find places for improvement.
0: Less than half of assignments solved satisfactorily
1: At least half of assignments solved satisfactorily
2: At least half of assignment solved pretty correctly
3: 65% of assignments solved pretty correctly
4: 80% assignments solved pretty correctly
5: All but 1 assignment solved almost perfectly
To assess the percentage of correctness, you may give fractional points from a serious (but failed) attempt, 1 point from essentially correct answer, and divide total points by maximum points.
2. Give a grade 0…5 on the clarity of writing and code, and provide constructive comments where you find places for improvement.
0: Writing and code are not at sufficient level in the solved assignments
1: Writing and code are mostly at sufficient level in the solved assignments
2: Writing and code are mostly at satisfactory level in the solved assignments
3: Writing and code are mostly at good level in the solved assignments
4: Writing and code are mostly at very good level in the solved assignments
5: Writing and code are mostly at excellent level in the solved assignments
In each category, in addition to textual feedback, give also a grade in the range 0, 1, …, 5.
The final grade for the project work will be the weighted average over the two categories, where category 1 has weight 2, and the second has weight 1. You must get at least grade 1 for the project work.
Certificate of studies
If you wish to have the credits entered in the University of Helsinki’s student records, you must first register for the course at the Open University. The ECTS-credits are available for those who have Finnish identity number and an online bankig ID, and for International students at the University of Helsinki.
All studies you complete are entered into the University of Helsinki Oodi system within four to six weeks of the date of completion. Failed grades are also entered into Oodi.
Once the results have been recorded in WebOodi, you will receive a notification in your helsinki.fi email. You can then request a transcript of studies , which serves as an official certificate of completed studies.
Moodle access for course project and exam
If you are on the path to finish the required TMC assignments, please do the following steps in order to continue with the project and the exam:
1. Register for the course through the Open University: https://www.avoin.helsinki.fi/palvelut/esittely.aspx?o=129239201
2. Wait for 24 hours and activate your University of Helsinki user ID . You will receive instructions on the activation process the following day.
3. Sign in on the course page, where you will find the enrolment key for the Moodle space: https://courses.helsinki.fi/fi/aycsm90004en/129239201
4. Sign into the Moodle space with your University of Helsinki user ID and the enrolment key.
5. Add your Univ. Helsinki *student number* to your TMC account (Click your account on top -> Settings-> Organizational identifier)! (We'll need this for grading, to match records between TMC and Moodle.)
Please note!
- Registration for the course through the Open University is possible until May 3, 2020.
- Credits for the course are only available to those students who have successfully registered for the course through the Open University and have completed the course according to the instructions.
Kursbeskrivningen
First 6 weeks (aka parts) of the course consist of automatically assessed exercises (using TMC system). There is a "soft" deadline in the end of each part until which one can gather full points from that part; late submissions are counted as 75% of their value. After completing these parts (at latest by the hard deadline in the end of the course), one should have gathered at least 50% of maximum points in order to continue with the final evaluation.
Final evaluation of the course consists of a multiple choice exam (in Moodle) and a peer-reviewed project work (in Moodle). The exam tests directly the knowledge gained while the project tests the ability to apply the learned skills in some selected field of science.
To pass the course (with grade 1), one need to pass TMC assignments (50%), exam (50%) and project (50%) and take part in peer-review of the project. Grading (for grades 2-5) is based on an overall assessment of TMC assignments, exam, and project. Each component is assessed independently and weighted average determines the final grade.
For the TMC assignments the grading scale is 50%=1,58%=2,66%=3,74%=4, and 82%=5.
For the exam the grading scale is 120 points=1,140 points=2, 160 points=3,180 points=4, and 200 points=5, where 240 points is the maximum.
The project work consists of similar tasks as in the weekly TMC assignments, but this time building a coherent story around a selected field of science. Currently the project topics offered are on fossil data analysis, on medical data analysis and on sequence analysis, of which you can select your favorite. For the latter two project options we provide a template containing placeholders for discussion around the code, and the outcomes can also be tested in the TMC system. However, for final submission, instead of TMC, the project solutions are to be collected in Jupyter Notebook format and submitted to Moodle for peer-review. From this peer-review one can then get feedback from the clarity of solutions and the style of code, complementing the automatic TMC assessments. In addition to peer-review, the project work is also self-assessed. The grade of the project is determined by an overall assessment of the project and the peer-review work.
Feedback
Thanks for the feedback! Altogether 44 answers makes a good statistics (see below).
The most urgent development item is a better / more structured forum for interaction between students and teachers. We tried moving the local workshops to zoom, but Q/A sessions like that didn't really fly, so we just continued with Telegram. However, whatever the forum is, there is always the resource limitation: For MOOCs like this, we really cannot put much more teaching resources than 2 hours for teaching assistant / week + teacher handling mainly just administration.
Note also that this course is somewhat experimental (product of Digiloikka-incentive) in that we aim for maximum possible automation, even in grading, without sacrificing learning objectives and equal treatment. E.g. regression and sequence analysis projects are integrated to TMC so that peer-review works as well as it can (very little discrepancy in given grades). It would be great to offer more open-ended projects, but then self- and peer-reviews are not much of help for grading. Currently we can only afford one such project (fossil data analysis). Without all these automated elements, we could never have this course in the curriculum.
***
Fully agree Partly agree Partly disagree Fully disagree
Learning objectives of the course were clear to me.
32 11 0 1
Teaching, study methods and assignments supported my learning on the course.
22 18 3 1
Instructions for the learning assignments were clear and easy to understand.
10 20 11 2
The evaluation criteria of the course was clearly presented.
30 11 2 0
I received enough feedback of my learning.
7 17 12 2
Interaction with other students supported my learning.
13 15 4 4
The teacher was sufficiently present during the course.
9 10 9 6
The course schedule worked for me.
35 5 2 0
The workload of the course corresponded with the credits (1 cr = ca. 27 h).
22 11 9 0
The technology of the online learning platform worked well on the course.
31 8 4 0
Anmälning och avgift
Beskrivning
The course is available to students from other degree programmes and to non-degree students through Open University. All course material and exercises are open to anyone.
Programming skills and basic knowledge of probability calculus and linear algebra.
The compulsory basic level courses in Bachelor's Programme in Science form a sufficient background.
-What other courses are recommended to be taken in addition to this course?
- Introduction to Data Science
-Which other courses support the further development of the competence provided by this
course?
- Introduction to Machine Learning
- Biological Sequence Analysis
- Can confidently write basic level Python programs without constantly consulting language/library documentation.
- Can apply efficient and elegant Pythonic idioms to solve problems
- Knows the different phases of data analysis pipeline
- Knows the fundamental data types array, Series and DataFrame
- Can clean data to form consistent Series and DataFrames without anomalies
- Can select subsets, transform, reshape and combine data
- Can extract summary statistics from data (min, max, mean, median, standard deviation)
- Knows the main types of machine learning (supervised learning: regression and classification, unsupervised learning: clustering, dimensionality reduction, (density estimation))
- Knows the estimator API of Scikit-Learn (choose model class, choose hyperparameters, form feature matrix and target vector, fit model, transform data or predict labels or responses)
- Can form feature matrix and target vector suitable for Scikit-Learn's model fitting algorithms
- Can visualize data as simple plots or histograms
- Can apply basic data analysis skills to a simple project on an application field
The course uses practical approach to different phases of data analysis pipeline: data fetching and cleaning, reshaping, subsetting, grouping, and combining data; and using aggregation, machine learning and data visualization to extract knowledge from data.
- Libraries: Numpy, Pandas, Scikit-learn, (Matplotlib)
- Interactive study materials: Jupyter notebook
- Automatic checking of exercises: Test My Code framework
- Basics of Python language
- Numpy
- Creation and indexing of arrays
- Array concatenation and splitting
- Fast computation using universal functions
- Summary statistics
- Broadcasting
- Matrix operations and basic linear algebra
- Pandas
- Creating and indexing of Series and DataFrames
- Handling missing data
- Concatenation of Series and DataFrames
- Grouping and aggregating
- Merging DataFrames
- Gentle introduction to machine learning through Scikit-learn library
- Linear regression
- Naive Bayes classification
- Principal component analysis
- k-means clustering
- Project on applying the learned skills on an application field
-What kind of literature and other materials are read during the course (reading list)?
Material is integrated to the MOOC instructions
-Which works are set reading and which are recommended as supplementary reading?
Jake VanderPlas, Python data science handbook, O'Reilly (2016)
The book is freely available in electronic form from https://jakevdp.github.io/PythonDataScienceHandbook/
MOOC includes automatic assessment of programming exercises
The grading scale is 1...5.
The final project, the peer-review work related to it and the exam are assessed.
Contact information:
- Questions regarding the learning environment: mooc@cs.helsinki.fi
- Questions about registering at the Open University: avoinyo-tietojenkasittelytiede@helsinki.fi
- You can find frequently asked questions on the course at the MOOC learning environment
- If you have questions about the content of the course, please contact the teacher in charge of the course, Veli Mäkinen (veli.makinen@helsinki.fi)
The course is completed in three stages:
1. Study the course material and complete the assignments in the online learning environment (TMC),
2. peer-reviewed project work in Moodle, and
3. a multiple choice Examinarium exam.
1. The first part of course is completed in the TMC learning environment. The course website contains the material and instructions necessary for completing the course. The materials are available to everyone without enrolling for the course through the Open University.
2. The second part of the course consists of peer-reviewed project work in Moodle. To be able to access Moodle, you will need to enroll yourself on the course through the Open University. In order to enroll you will need to meet one of the following criteria:
A. You have a university of Helsinki user ID.
B. You have Finnish personal indentity number.
C. You are able to visit the University of Helsinki Admission Services in Helsinki and verify your identity.
3. The last part of the course is a multiple choice Examinarium exam to be completed in Helsinki or one of the other Finnish Universities offering Examinarium exams.
MOODLE
Online learning environment Moodle opens on March 9, 2020. You will be able to log in to the course's Moodle after completing the course assignments in the MOOC environment (TMC) and registering on the course.
How to get the Moodle-link and course key?
Next day after registration: log into this study programme (this webpage) with your University of Helsinki username. The course key will be visible on this page next to the Moodle button, once you are logged in.
You will receive more information on the username after registration.
EXAMINARIUM
Course grading is based on a multiple-choice Examinarium-exam and a project work handed in in Moodle. See the deadlines for the project works under the navigation bar "Timetable".
Examinarium exams are electronic exams taken on a computer in certain Examinarium rooms at the University. The exam is supervised via recording camera equipment installed in the rooms. You can book the time and the room of the exam yourself. Currently it is possible to take the exam in several Examinarium rooms at the University of Helsinki and it is anticipated that in spring 2020 this service will be available in several universities in Finland.
You can start your project work as well as book the time for the exam after required amount of automatically assessed programming exercises are conducted in the TMC system. You can take the Examinarium exam earliest on 27.4.2020 and latest on 11.5.2020!
How to take the Examinarium exam:
1. Register for the course via Open University (the Register button on this page).
2. Book the time for the Examinarium exam. Remember to register for the exam in good time. This way you'll get the time and Examinarium room you prefer (earliest one day after registering for the course).
Read the Examinarium instruction carefully before taking the exam.
Veli Mäkinen
The course is part of the subject studies in Computer Science.