Strong mathematical foundation (recommended).
Familiarity with Python and programming fundamentals.
Students are assigned up to 12 hours of Data Science Fundamentals pre-work to prepare for the class.
Physical class / Online / Blended
3 months – 12 weeks training + project
Data Scientist, Data Engineer, Data Analyst
Level 1 Data Skills: Wrangle, explore, model, and communicate the results of multiple analyses with Python and its many packages.
Level 2 Data Skills: Work on rigorous advanced analytics data sets and practice machine learning techniques in depth.This is broken down through the following units:
- Practice the fundamentals of data science by generating descriptive statistics and visualisations in Python
- Practice exploratory data analysis to clean and aggregate data and identify the basic statistical testing values of your data in Python and SQL.
- Explore effective study design and model evaluation and optimisation, implementing linear and logistic regression and classification models.
- Collect and connect external data to add nuance to your models using web scraping and APIs.
- Build machine learning models.
- Explore the differences between supervised and unsupervised learning via clustering, natural language processing, and neural networks.
- Dive deeper into recommender systems, neural networks, and computer vision models, implementing what you’ve learned to productise models.