CURRICULUM

Curriculum

Quality because of constant improvement

Curriculum evolved over 6 years

We change about 20% of it every batch.

Deep expertise

Others have a single teacher cover the entire curriculum. This doesn’t allow for mastery. Data Science is such a complex topic that a single person cannot be an expert on every area.

At DSR, we have one teacher per topic. This allows for the teacher to be a master for that topic; or at least for it to be something he uses everyday at work.

Where others only scratch the surface, we go deep

Advanced topics that prepare you for the job market 2 years from now

courses

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
Practical Machine Learning
Working with Small Samples
Backpropagation & Deep Learning
Computer Vision with PyTorch
Sequential Models with TensorFlow
Natural Language Processing (NLP)
Unsupervised Learning
Interpreting Machine Learning models
Reinforcement Learning

BUSINESS

Technical communication and presentation skills
Interview question practice & preparation

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

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 Sciencefor 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