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London Housing - The Basics of Data Science
Simple workflow involving loading, cleaning, formatting, exploring, and visualizing data to answer a data analytics question. -
API Mini Project - APIs & The Python Standard Library
Leveraging APIs to source data from different platforms. -
SQL Country Club - Relational Databases & SQL
Query data from relational databases through python packages and management tools such as pgAdmin. -
Frequentist Inference - Statistical Inference in Python
Understanding the basics of staistical inference -
Statistical Modeling - Python Statistics Essential Training
Builing on the basics of statistical inference and adding statistical modeling. -
App Store Integration - Hypothesis Testing in Python
Making software design decisions using data driven hypothesis testing. -
The Red Wine Dataset - Linear Regression
Build a series of Linear Regression models to predict alcohol levels in wine and choose one by evaluating the models' performance. -
Logistic Regression - Introduction to Classification
Understand the mechanics of Logistic Regression and build a model that predicts gender from weight and height data. -
Coffee Diner Horizontal Expansion - Decision Trees
Use Decision Trees to produce an analysis that can be used when considering if a horizontal expansion is beneficial to a business. -
COVID-19 - Ensemble Methods I: Bagging - Random Forests
Create a Random Forest model to build a classifier to predict the "state" of a patient. -
The Titanic Dataset - Ensemble Methods II: Boosting - Gradient Boosting
The simple, but iconic Titanic dataset was used to introduce Gradient Boosting algorithms. -
Manhattan & Euclidean Distances - Application of Distance Measures I
Preparing for Unsupervised Learning by understanding distance measures. -
Cosine Similarity - Application of Distance Measures II
Alternative ways to think about similarity. -
Wine Customer Segmentation - Clustering
Using the outcome of different marketing campaigns, group similar minded customers together. -
Featuretools - Automated Feature Engineering
Automate the feature engineering process using advanced tools. -
Pima Indian Diabetes Dataset - Model Optimization I: Grid Search Optimization - KNN
Hyperparameter tune a KNN model using GridSearch. -
Flight Departures Dataset - Model Optimization II: Bayesian Optimization - LightGBM
Hyperparameter tune a LightGBM using Bayesian Optimzation. -
GBPUSD FX Profiling - Data Storytelling
Create a story around a dataset. Analyze it and write a report to be read by a diverse audience. -
Growth Hacking - Data Driven Growth
Use a company's existing data store to create analyses that can help with growth and marketing. -
Big Data Technologies - Introduction to Spark SQL in Python
Introduction to tools that can be used to work with the Big Data that will be encountered in the field. -
Future User Adoption - Take Home Challenge I
Determine the most important factors realated with user adoption. -
Ultimate Data Scientist - Take Home Challenge II
Complete a task from segments of the Data Science field. Analze user logins to uncover patterns of demand, design an experiment that could test the effectiveness of a business intiative, and build a predictive model to determine long-term rider retention.
Williamdst/Springboard-DSC
The portfolio of work that was done during my Data Science training at the Springboard Intensive Bootcamp.
Jupyter Notebook