Springboard Data Science Bootcamp - Business Intelligence Specialization

The Basic Tools for Dealing w/ Data

• 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.

---

Statistics

• 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.

---

Supervised Machine Learning

• 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.

---

Unsupervised Machine Learning

• 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.

---

Advanced Machine Learning Techniques

• 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.

---

Business Intelligence

• 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.