MITx - MicroMasters Program on Statistics and Data Science - Data Analysis: Statistical Modeling and Computation in Applications - First Project
The first project of the MIT MicroMasters Program course on Data Analysis focused on a review of key statistical concepts related to performing research with empirical data. The concepts covered on how to design research experiments to how to conduct hypothesis testing in complicated problems.
This included being able to discuss the effectiveness of an experimental design, by understanding the different elements such as placebo effect, double-blindness and which test statistic measures the treatment effect and the potential biases in this test statistic.
Furthermore, being able to understand and apply multiple hypothesis testing that includes a statistical model for the data and the hypothesis to be tested together with the adequate test statistics. Running the test by understanding the asymptotic distribution of the test statistic and identifying statistical significance by also incorporating the forms of correction for multiple hypotheses testing such as Holm-Bonferroni correction and Benjamini-Hochberg correction. In the analysis, understanding the mathematics and statistics of p-value and the consequences of these in real applications. Being able to perform Ordinary-Least-Squares (OLS) regression together with gradient descent with multiple parameters, to interpret the results and to adjust accordingly in order to reach meaningful results.
The project included application of statistical methods and models with three different datasets and a written report in a deadline of two weeks of time.
The file project1.ipynb is the Jupyter Notebook that contains all the code, visualizations and analysis of the project.
The dependencies and requirements can be seen from requirements.txt that can be installed in shell with the command:
pip install -r requirements.txt