Data Scientist Bootcamp 

• Data import and storage

• Understand the data – basic explorations

• Data manipulations: Pandas library; Data transformations – Data wrangling

• Exploratory analysis: Missing observations; Outliers detection & strategies

• Standardization, normalization, binarization; Qualitative data recoding

• Types of Machine learning – supervised vs unsupervised learning

• From Statistical learning to Machine learning o The Data Mining workflow

• Machine learning algorithms o Choosing appropriate algorithm to the problem

• Overfitting and bias-variance trade-off in ML

• Generalization and overfitting

• Avoiding overfitting o Evaluating classification algorithms

• Visualizing model performance o Model selection

• Model tuning – grid search strategies

• Binary vs multiclass classification

• Classification via mathematical functions

• Logistic regression and probability approach

• Decision trees

Introduction of different functions such as online database

Project: Bank Fraud Detection Modeling