Applied Data Science Lab, WorldQuant University

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What I learned from this

Module 1:

How to organize information using basic Python data structures.

How to import data from CSV files and clean it using the pandas library.

How to create data visualizations like scatter and box plots.

How to examine the relationship between two variables using correlation.

Module 2:

How to create a linear regression model using the scikit-learn library.

How to build a data pipeline for imputing missing values and encoding categorical features.

How to improve model performance by reducing overfitting.

How to create a dynamic dashboard for interacting with your completed model.

Module 3:

How to get data by querying a MongoDB database.

How to prepare time series data for analysis.

How to build an autoregression model.

How to improve a model by tuning its hyperparameters.

Module 4:

How to get data by querying a SQL database.

How to build a logistic regression model for classification.

How to build a decision tree model for classification.

How to incorporate ethical considerations into your model building.

Module 5:

Navigate a file system from the Linux command line

Load and save files using Python

Address imbalanced data using resampling techniques

Evaluate a model using classification metrics like precision and recall

Module 6:

Compare characteristics across subgroups using a side-by-side bar chart.

Build a k-means clustering model.

Conduct feature selection for clustering based on variance.

Reduce high-dimensional data using principal component analysis (PCA).

Design, build and deploy a Dash web application.

Module 7:

Build a choropleth map to show the distribution of ADSL students around the world.

Create a custom Python class to implement ETL processes.

Design an experiment and analyze the results using a chi-square test.

Build an interactive web application that follows a three-tiered design pattern.

Module 8:

Get data from a web API by making HTTP requests.

Transform and load data to a SQL database using custom Python classes.

Calculate asset volatility and build a GARCH model to predict it.

Build your own web API and server to serve your model’s predictions.

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