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:

ENGINEERING​

  • Data science tools – text editors, development environment setup
  • Programming practices – test driven development, reproducibility, packaging
  • Python – Pandas, NumPy, Scikit-Learn, Matplotlib
  • SQL
  • Using a Bash shell
  • Git & GitHub
  • Data visualisation – D3
  • Deploying models with Flask and Docker
  • Distributed machine learning with Spark

ANALYTICS​

  • Probability & Statistics
  • Foundations of Machine Learning
  • Trees and Time Series
  • Practical Machine Learning
  • Backpropagation & Deep Learning
  • Computer Vision with PyTorch
  • Image Processing with TensorFlow
  • Sequential Models with TensorFlow
  • Natural Language Processing (NLP)
  • Unsupervised Learning
  • Interpreting Machine Learning models
  • Reinforcement Learning

BUSINESS​

  • Technical communication and presentation skills
  • Data Science Challenge solving Business use case
  • Interview question practice & preparation
  • Portfolio Project which create Business Value

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:


  • Familiarity with random forests & logistic regression
  • Familiarity with bias & variance
  • understand the difference between test & train error
  • Implemented a machine learning project from scratch, making use of pandas, matplotlib & scikit-learn. The Titanic or MNIST datasets are a good place to start

To join our next batch​

UPCOMING BATCHES

24 June - 01 October, 2024

09 September - 17 December, 2024


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​

DATA SCIENCE​

MACHINE LEARNING​