Jan Šíla is inviting you to a scheduled Zoom meeting.
Topic: Python Lecture 5 Time: Mar 16, 2021 06:30 PM Prague Bratislava
Join Zoom Meeting https://cesnet.zoom.us/j/97593598285?pwd=Y2k3eGg3cmpXRG90aTBLalM3UGV5Zz09
Meeting ID: 975 9359 8285 Passcode: 704081
The course site for the Data Processing in Python from IES. See information on SIS. The course is taught by Martin Hronec, Vítek Macháček and Jan Šíla.
Date | Topic | who | Project | HW | |
---|---|---|---|---|---|
16/2 | Intro, Jupyter, Git (+ GitHub) | Martin | |||
22/2 | Seminar (Git) | Martin | HW 1 | ||
23/2 | Strings, Floats, Lists, Dictionaries, Functions | Vitek | HW 0 | ||
2/3 | Numpy, Pandas, Matplotlib | Jan | HW 2 | ||
8/3 | Seminar | Jan | |||
9/3 | Object-Oriented Programming | Jan | HW 3 | ||
16/3 | HTML, XML, JSON, requests, APIs, BeautifulSoup | Jan | |||
22/3 | IES Web Scraper | Vitek | HW 4 | ||
23/3 | Seminar | Vitek | |||
30/3 | Advanced Pandas | Vitek | HW 5 | ||
5/4 | State Holiday | -- | |||
6/4 | Seminar - MIDTERM | full house | |||
13/4 | Introduction to Databases | Jan | Project Topic Proposal | ||
19/4 | Efficient Computing | Martin | |||
20/4 | Parallelization | Martin | HW 6 | ||
27/4 | Seminar | Martin | Project Topic Approval | ||
3/5 | Guest Lecture | TBD | |||
4/5 | Project Work 2 (Seminar) | full house | Work-in-progress | ||
11/5 | Project Work 2 | full house | Work-in-progress | ||
17/5 | Project Work 2 (Seminar) | full house | Work-in-progress | ||
18/5 | Project Work 2 | full house | Work-in-progress | ||
TBD | Project Deadline | full house |
The requirements for passing the course are DataCamp assignments (5pts), the midterm (25pts), work in-progress-presentation (10pts), and the final project - including the final delivery presentation (60pts). At least 50% from the DataCamp assignments and work-in-progress presentation is required for passing the course.
- Students in teams by 2
- Deadline: TBD
- The task is to download any data from API or directly from the web. These data should be processed and visualized in the Jupyter Notebook, with auxiliary scripts consisting of functions and classes definitions as .py files. The project should be submitted as a GitHub repository.
- The selection of the data is up to the students. (Conditional on our approval.)
- Git collaboration as a proof of collaboration of both students.
- More details during the lecture.
- Submitted as a Jupyter notebook in a Git repository. All team members pushed to the repo.
- Code is runnable and replicable (after installation of necessary packages).Exception only due to good reasons (data availablity, etc)
- OOP and code structure
- Analysis and visualization
- Code Readibility + Documentation
See example project from the previous semesters here from last year.
- Presentation of work-in-progress related to the final project.
6/4. Live coding (80 minutes), "open browser", no collaboration between the students. More details during the lecture week before
3 assignments out of assignments 1-6 submitted on time is required.
Assignment 0 - Submission on 22/2 (Introduction to Git)
- Compulsory. Git is hard and you will need it throughout the course.
Assignment 1 - Submission on 23/2 (Introduction to Python Course)
- Python Lists
- Python Basics
- Function and Packages
Assignment 2 - Submission on 2/3 (Manipulating DataFrames with pandas)
- Numpy
- Extracting and Transforming Data
- Advanced Indexing
Assignment 3 - Submission on 9/3 (Object-Oriented Programming in Python)
- Getting ready for object-oriented programming
- Deep dive into classes and objects
- Fancy classes, fancy objects
Assignment 4 - Submission on 22/3 (Web Scraping in Python Course)
- Introduction to HTML
- XPaths and Selectors
- CSS Locators, Chaining, and Responses
Assignment 5 - Submission on 30/3 (Merging DataFrames with pandas Course)
- Concatenating and merging data
- Rearranging and reshaping data
- Grouping data
Assignment 6 - Submission on 20/4 (Importing Data in Python (Part 2) Course)
- The Intro to SQL for Data Science (full course)
Introduction to Git for Data Science
Intermediate Python for Data Science
Manipulating DataFrames with pandas
Merging DataFrames with pandas
Importing Data in Python (Part 1)
Importing Data in Python (Part 2)
Introduction to Data Visualization
Interactive Data Visualization in Bokeh
Introduction to SQL for Data Science
Introduction to Databases in Python
Econometrics II. (JEB110) is an explicit prerequisite for bachelor students.
The course is designed for students that have at least some basic coding experience. It does not need to be very advanced, but they should be aware of concepts such as for
loop ,if
and else
,variable
or function
.
No knowledge of Python is required for entering the course.
Passing the course is rewarded with 5 ECTS credits.
Pro Git book, Atlassian Git tutorials, Github resources for learning Git
Resources from the official Python webpage
Python, Pandas, Numpy, requests, BeautifulSoup and Matplotlib.