Day 1: Advanced Concepts in Python. On this first day, the course will focus on the Python programming language to build a solid foundation for the rest of the course materials. Participants will be introduced to practical techniques from intermediate to advanced level, such as writing functions, classes, error handling, packing of Python code, and more.
Day 2: Python for Data Science: Day 2 focuses on performing common Data Science tasks using Python. We’ll explain how to use data, process, analyze, visualize, ‘Web Scraping’, and more using Python, while introducing essential packages (Pandas, Geopandas, Numpy, Matplotlib, etc) to perform these tasks.
Day 3: Big Data Handling: On the third day, the course covers handling large data sets using Python.
The following topics will be covered in addition to introduction to Big Data, multiprocessing in Python, Apache Spark, use of common cloud platforms etc.
Day 4: Machine Learning (ML) in Python. On the fourth day, the course will begin with an introductory lecture on Machine Learning. the remainder of the day will be spent completing various ML tasks (e.g data preparation, model building, evaluation and interpretation) using the scikit-learn package in Python.\
Day 5: Putting it all together: In the last day, we will focus on the skills learned in this course to solve real-world data science problems by examining case studies.
Potential case studies to cover include: how to process nighttime satellite images(geo-spatial), how to process large call records from cellphones (mobile data), and how to create ML models to impute sensor data missing (sensor data).
Programming: possibility to write a simple program in Python (basic Python level)
Maths and Statistics: Training in statistics, data science of quantitative sciences.