About This Course

Python is arguably the best software for data management and statistical analysis. It is a fast, powerful statistical package designed by statisticians for researchers of all disciplines. Python is a complete, integrated statistical package that provides everything for data analysis from data management to basic and advanced analysis. Python makes it easy to conduct data cleaning and management, distinctly styled graphs, descriptive analysis and advanced analysis. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. In this course, participants will experience the desired qualities and functionalities that Python widely preferred.

In this course participants will learn how to program in Python and how to use Python for data analysis. The course covers practical issues in statistical computing which include introduction to Python programming, accessing Python packages, reading data into Python, writing Python functions, debugging, profiling Python code, and organizing and commenting Python code. Topics in statistical data analysis will provide working examples.

Participants will learn to import other types of data files, like Excel and CSV files, into Python. They will learn to use Python for data cleaning, management and statistical analysis. They will achieve the understanding of descriptive statistics, confidence intervals, p-values and bi-variate inferential statistics (hypothesis testing). They will understand the concept of weighting and be able to implement it. The theory behind the statistics will be explained and participants will be able to interpret statistical outputs. Practical exercises will give special focus to the Rwanda ISS/DQA datasets.

The course uses a fine blend of interactive discussions, group exercises for every session, self-paced learning and two project reports that simulate real-life scenarios. At the end of the course, participants will be able to produce and interpret basic and intermediate descriptive and bi-variate inferential statistics using Python. As a result, they will be able to take raw data, clean them, summarise them, analyse them and take appropriate action. They will also improve their skills to critically analyse research reports.