CURRICULUM
We teach state-of-the-art Curriculum which is constantly improved every batch.
Our latest 14-week Data Science program is the culmination of these continuous improvements.
70 days
53 days of training + 17 days of portfolio
project work and Demo Day
5 Days
5 days a week of classes including weekends.
Classes from 9:30 am to 5:30 pm
We have divided the new 14 weeks program into 4 phases which are Fundamental (2 weeks), Intermediate (4 Weeks), Advanced (5 weeks) and Portfolio Project (3 weeks)
FUNDAMENTAL
Introduction to Data Science
Python – Pandas, NumPy, Scikit-Learn, Matplotlib
Probability and Statistics
Git and Bash
SQL
Machine Learning Fundamentals
INTERMEDIATE
Object Oriented Python and Software Architecture
Data Visualization - D3
Docker and Databases
DS Fundamentals
Advanced Trees
Business Communication
Time Series
Backpropagation
Data Science Challenge solving
Business use case
Practical DS/ Streamlit
Deep Learning
ADVANCED
Natural Language Processing (NLP)
Transfer Learning and Representation
Finetuning LLMS
LLM Quantization
Retrieval Augmented Generation (RAG)
Software testing (unit tests, CI, test driven development)
Deep Learning for Image Processing with TensorFlow
Computer Vision using PyTorch
Complexity Theory Fundamentals
Practical Reasoning
Debugging Deep Learning model
Geometric Deep Learning and GNN
Career Coaching
Deep Reinforcement Learning
Practical ML /MLOps
PORTFOLIO PROJECT
All students design, implement and present a hands-on portfolio project. The project is a chance to show employers your new skillset. We host a public Demonstration Day, where you will present your project to the Berlin data science community.
Data science is the intersection of engineering, analytics and business. Below is our teaching curriculum grouped by these three dimensions:
Requirements
Data Science Retreat is an advanced program; we have higher initial requirements than most data science bootcamps.
Don't worry if you aren't where you need to be yet; we are here to help no matter where you are in your data science transition.
Before the Interview
There are no strict requirements on your level before the interview. Most participants have already taken their first steps learning Python or machine learning before the interview.
We recommend that anyone considering studying at Data Science Retreat to book an interview; we are happy to give advice on what you can study to get up to speed.
Before the Bootcamp
Below we outline the required knowledge for our participants to explore before they study with us:
Python
For Python, we expect students to be familiar with the following concepts outlined in the Python Tutorial:
- Variables, Strings, Floats, Integers (Section 3)
- Conditionals (Section 4.1 - 4.5)
- Functions (Section 4.6, 4.7.1, 4.7.2)
- Lists (Section 3.1.3, 5.1)
- Tuples, Sets, Dictionaries (Section 5.3 - 5.5)
- Reading & Writing Files (Section 7.2)
Linear Algebra & Probability
For linear algebra, participants are expected to understand:
- The difference between a scalar, matrix & tensor
- Element-wise matrix multiplication & dot products
For probability, we expect participants to be familiar with:
- Independent, marginal and conditional probabilities
- Expectation & variance
- The Bernoulli & Gaussian distributions
Machine Learning
For machine learning, we expect students to have:
ADDITIONAL RECommended RESOURCES
One of the most beautiful features of data science is access to resources to learn from; below are some of our favourites.
PROGRAMMING
- Think Python as an introductory textbook
- Fluent Python as a more advanced look at the language
- Practical coding challenges we recommend HackerRank
- The DataCamp Introduction to Shell for Data Science for an excellent introduction to the Bash shell
DATA SCIENCE
- Kaggle offers access to many interesting datasets, along with communities that share their work
- Python for Data Analysis
- Automate the Boring Stuff with Python
- Agile Data Science is recommended as a more advanced textbook that covers application development using a distinct software engineering philosophy
MACHINE LEARNING
- Andrew Ng's Stanford Machine Learning is a classic course that is somewhat of a rite of passage for machine learning practitioners
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow textbook
- Andrew Ng's Machine Learning Yearning is a useful resource for getting insight into the practicalities of model training
- Elements of Statistical Learning and Pattern Recognition and Machine Learning for classic machine learning textbooks
- Deep Learning for neural networks