DS402
Python Refresher for Masters
Faculty
Maxim Musin
CEO at rebels.ai
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
Modern Python is a language at the very core of the AI revolution: its flexibility and simplicity makes it an ultimate language to control workflows and processes in various rapidly automated fields. The course covers various python techniques from basic to advanced, particularly methods of data analysis and AI applications: pandas, numpy, sklearn, and some integrations with GenAI and machine learning and AI hubs like huggingface and langchain. We’ll also review various data wrangling and hyperparameter optimisation methods. Collab notebook usage and tricks will also be given as an organic part of the course.
At the end of the module, everyone is expected to be ready to work with ssh and git in a setting close to enterprise development as long as they come up with a simple data wrangling system.
Learning highlights
- Learning in depth python principles allowin its flexibility
- Learning basic Python methods for data analysis
- Learn basic git and ssh operations
- Use of Python’s external libraries
- Use huggingface and langchain libraries
- Learn how to effectively use the Colab notebook.
Course outline
15 classes
Session 1
Colab notebooks ecosystem, gdrive integration, git integration, AI code completion, gemini integrations, limitations of colabs, ssh, jupyter lab ecosystem at glance
Session 2
Memory, functions, decorators, and generators. Data manipulation. Pandas. Reading .csv files, Titanic dataset. Hands-on manipulating the datasets.
Session 3
Data visualization. Hands on visualization, automated data visualization and interactive plotting
Session 4
Classes, inheritance, generics, Sklearn. Basic ML concepts: cross validation, fit/predict. Preparing prediction for Titanic dataset.
Session 5
Checking homework assignments on the first 3 sessions. Sklearn and numpy methods for data manipulations.
Session 6
Data versioning. Git. Working with enterprise data analysis systems, pitfalls, and techniques.
Session 7
Weekly homework revisiting. Performing data analysis at scale.
Session 8
Start working on general projects. Open discussion about what can and cannot be done with Python in seven days. Very basic custdev.
Session 9
Storing custom approximators as custom. Sklearn classes. Sklearn pipelines.
Session 10
Working with textual, visual, and audio data in Python. Hugging face and gen AI integrations.
Session 11
Advanced basic approximators to use in practice: xgboost, catboost, and vw. Off the shelf hyperparameter optimization. Automl.
Session 12
Integrating via Python. Google docs, chatbots, interface prototyping, data annotation, scrapping, and no-code platforms.
Session 13
Heavy dataset processing with Python instruments, Cython.
Session 14
Consultations on student projects.
Session 15
Finals
Course materials
Media
Prerequisites
Knowledge of Python on the level of snakify.org is highly recommended. A general interest in statistics and data analysis is also a plus.
Methodology
We will study a set of practical jupyter notebooks, interrupted by relatively short theoretical parts. There will be two big homework assignments designed to emulate a relatively real data science project. There will also be personal projects based on Python integrations and capabilities of data analysis; this will be a good example of time management in a DS project. Finally, students will have a final exam and a student project demonstration at the end of the course.
Grading
Maxim Musin comes from a background in statistics, advanced multidimensional probability, and random processes. During his career in these fields, he found himself developing skills and gathering experience through working in both academic environments and the private sector. For the last 5 years Maxim is a CEO of for profit AI development laboratory rebels.ai, integrating AI in enterprise and helping startups reach the orbit.
His academic experience ranges from teaching probability and statistics at MSU and MIPT, as a member of the faculty of innovation and high technology, FIHT, which at the time was among the few places worldwide with capabilities for advanced statistics study. During his time there, he produced several notable projects with his students, particularly in regards to the stochastic convergence of neural networks. His course on applied modern statistics became mandatory for the data analysis division of the FIHT MIPT Masters.
See full profileApply for this course
Python Refresher for Masters
by Maxim Musin
Total hours
45 Hours
Dates
Sep 30 - Oct 18, 2024
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.