UCSanDiegoX: DSE200x (Summer 2018): Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.
Ilkay Altintas, Chief Data Science Officer, San Diego Supercomputer Center (SDSC)
Leo Porter, Assistant Teaching Professor, Computer Science and Engineering, UC San Diego
Final Project: We use Pandas, Matplotlib and World Bank data to analyse CO2 emission trends worldwide
Early stage economic development depends on increasing CO2 emission-per-capita unless cheap and reliable alternatives to fossil fuel energy sources like coal are widely available. China’s produces 2/3 of its electricity with coal and her strong economic growth since the mid-1960’s is accompanied by accelerating CO2-per-capita growth up to levels of advanced European economies. France has shown a similar fossil fuel growth dependence in the 1960’s until she switched to nuclear power generation in the 1970’s and 1980’s. Since that change to alternative (i.e. non-fossil) energy sources for electricity generation her impressive economic growth decoupled from the reliance on ever increasing CO2-emissions. The example of France shows that access to cheap and reliable electric energy is the key ingredient for economic growth. Technological advancements in renewable energy sources like wind and solar should enable other developed economies and emerging economies to achieve sustainable economic growth while reducing CO2-emissions.
The course is broken into 10 weeks. The beginning of the course is heavily focused on learning the basic tools of data science, but we firmly believe that you learn the most about data science by doing data science. So the latter half of the course is a combination of working on large projects and introductions to advanced data analysis techniques.
Week 1 - Introduction: The data science process and the value of learning data science.
Week 2 - Background: We provide a brief background in python and unix to get you up and running.
Week 3 - Jupyter and Numpy
Week 4 - Pandas: Pandas, data frames offer critical data analysis functionality and features.
Week 5 - Visualization: you'll often wish to use visualizations to present your results.
Week 6 - Mini Project: Pick a dataset and perform an analysis for this first project.
Week 7 - Machine Learning: how to use sci-kit learn - a powerful library for machine learning.
Week 8 - Working with Text and Databases: analyze text data using Natural Language Processing.
Week 9 - Final Project
Week 10- Final Project