Repository for the Data Science learning track to host assignments.
Find powerpoints and helpful resources here:
Finish Any Installations not completed in class.
Assignments are located in ../course_material/week_01/week1_assignments.md
- For those of you interested in learning more in-depth material about Neural Networks, we highly recommend you to complete the Deep Learning Specialization. This is a 5 course series from Coursera which deals with implementing a set of state-of-the-art Neural Networks. This is well beyond the scope of CoderGirl- Data Science, but we wanted to keep this here as a reference.
- Perform Explortatory Data Analysis (EDA) on Heart Disease Kaggle Project
- Post the link to your GitHub repo for Mini-Project part I: EDA
- Your notebook should address each of the following:
- Data issues: missing values, duplicate values, outliers
- Data cleaning solutions: imputation/estimation, dropping entries -- justify your choices!
- Describe the realtionship of features to your target (should include at least a few plots).
- Feature engineering (transformation, normalization, createing new combinations of features, etc), if you think this is necessary. Describe your rationale.
- Your notebook should address each of the following:
- Post the link to your GitHub repo for Mini-Project part II: Modeling
- Your modeling notebook should include each of the following:
- (Feature engineering, if not captured in the EDA notebook. Sometimes it is easier or makes more sense to do feature engineering in the same notebook as your model.)
- Splitting data into train/test sets
- Build (at least one) model
- Predict test set using model(s)
- A quantiative metric of model(s) performance
- Your modeling notebook should include each of the following: