Module 1: Introduction to Programming. Learn the foundations of programming & how to code in Python. Write a simple in Python
Module 2: Programming for Data Analytics. Understand the data analysis pipeline, review the data science foundations using probability, statistics & basic data analysis, & learn classic. Create a predictive model with a given dataset
*Module 3: * Infrastructure & SQL. Learn how to use Linux/Bash & Docker, review SQL & relational databases. Create & query a relational database on Linux using Docker
Module 4: Statistics. Basic statistics lays the foundation for ML & Advanced Data Analysis. In this module we review basic statistics concepts, such as probabilities, central tendency measures & charts & graphs. Use statistical methods to analyse datasets using Jupyter notebooks
*Module 5: * Machine Learning. Learn the differences between supervised & unsupervised machine learning methods, & the different families of algorithms within each group (e.g. regression, classification, clustering). Create a predictive model for a given labelled dataset
Module 6: Advanced Data Analytics. Learn more advanced methods which deal with data types that are more complex than tables of numbers (e.g. text, geospatial data, time-series, AB testing). In this module, we will cover specific methods that apply to these types of data, as well as how to pre-process them and visualise them. Use ADA methods to analyse complex datasets
Module 7: Project Phase. Apply the knowledge gathered in the previous modules to a real use case, implementing a DA project, end-to-end. Learn how to work collaboratively & build a portfolio. Propose a problem & solve it using an ADA method. Cover all stages of the DA lifecycle in both an individual & collaborative project
*Module 8: * Career Prep. Prepare students for job interviews through logical puzzles & data challenges. Career coaching
Thinking about kickstarting a career in data science?Data analysis is the process of inspecting, cleansing, transforming and modelling data, using methods from statistics and database systems to discover useful information, validate conclusions, and support decision-making across domains.
No prerequisites required for this course.
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